Top 10 Big Data Processing Tools

What are Big Data Processing Tools

Big Data Processing Tools refer to a set of software applications, frameworks, and technologies designed to process, analyze, and extract insights from large and complex datasets, commonly known as big data. These tools are specifically developed to handle the unique challenges posed by big data, such as the volume, velocity, variety, and veracity of the data.

Big data processing tools are designed to handle and analyze large volumes of data efficiently. They provide capabilities for processing, storing, and analyzing data at scale.

Here are some popular big data processing tools:

  1. Apache Hadoop
  2. Apache Spark
  3. Apache Flink
  4. Apache Storm
  5. Apache Kafka
  6. Google BigQuery
  7. Amazon EMR
  8. Microsoft Azure HDInsight
  9. Cloudera
  10. IBM InfoSphere BigInsights

1. Apache Hadoop:

Apache Hadoop is an open-source framework that provides distributed storage and processing capabilities for big data. It consists of Hadoop Distributed File System (HDFS) for storing large datasets across multiple machines and MapReduce for parallel processing of data across a cluster.

Key features:

  • Distributed File System: Apache Hadoop includes the Hadoop Distributed File System (HDFS), which is designed to store and manage large volumes of data across multiple machines in a distributed environment. HDFS provides fault tolerance, data replication, and high-throughput data access.
  • Scalability: Hadoop is highly scalable and can handle petabytes of data by distributing it across a cluster of commodity hardware. It supports horizontal scaling, allowing organizations to add more nodes to the cluster as their data processing needs grow.
  • MapReduce Processing Model: Hadoop utilizes the MapReduce processing model for distributed data processing. MapReduce breaks down data processing tasks into smaller tasks that can be executed in parallel across the nodes in the cluster. It efficiently processes large datasets by distributing the workload.

2. Apache Spark:

Apache Spark is an open-source cluster computing framework that provides in-memory processing capabilities for big data analytics. It supports various programming languages and offers a high-level API for distributed data processing, including batch processing, real-time streaming, machine learning, and graph processing.

Key features:

  • Speed: Spark is known for its high-speed data processing capabilities. It performs in-memory computations, which allows it to process data much faster than traditional disk-based processing frameworks. Spark leverages distributed computing and parallelism to achieve high throughput and low latency.
  • Distributed Computing: Spark enables distributed data processing, allowing users to process large datasets across a cluster of machines. It automatically distributes data and computation across multiple nodes, taking advantage of the cluster’s resources and providing efficient scaling.
  • Data Processing APIs: Spark provides various APIs for data processing, allowing developers to choose the most suitable interface for their needs. It supports APIs in Scala, Java, Python, and R. The primary APIs in Spark are the core API for general data processing, the Spark SQL API for structured data processing, the Spark Streaming API for real-time streaming analytics, and the MLlib API for machine learning tasks.

3. Apache Flink:

Apache Flink is an open-source stream processing framework that supports both batch and real-time data processing. It provides fault-tolerant stream processing with low latency and high throughput. Flink offers support for event time processing, windowing, state management, and integration with popular message queues and storage systems.

Key features:

  • Stream Processing: Flink provides a powerful stream processing model that enables the processing of real-time data streams with low latency and high throughput. It supports event-time processing, windowing, and stateful computations on streaming data. Flink’s stream processing capabilities make it suitable for applications such as real-time analytics, fraud detection, monitoring, and more.
  • Batch Processing: In addition to stream processing, Flink also supports batch processing, allowing users to run batch jobs on large datasets. It provides a unified programming model for both batch and stream processing, simplifying the development and deployment of hybrid batch-streaming applications.
  • Fault Tolerance and Exactly-Once Processing: Flink offers built-in fault tolerance mechanisms to ensure data reliability and consistency. It provides exactly-once processing semantics, guaranteeing that each event is processed exactly once, even in the presence of failures. Flink achieves fault tolerance by maintaining distributed snapshots of the application state and transparently recovering from failures.

4. Apache Storm:

Apache Storm is an open-source distributed real-time stream processing system. It enables the processing of high-velocity streaming data with low latency. Storm provides fault-tolerant stream processing capabilities and supports complex event processing, real-time analytics, and stream-based machine learning.

Key features:

  • Stream Processing: Storm enables the processing of high-velocity data streams in real-time. It provides a distributed and fault-tolerant architecture to handle continuous streams of data and process them in parallel across a cluster of machines. Storm supports both event-based and micro-batch processing models.
  • Scalability and Fault Tolerance: Storm is built to scale horizontally, allowing users to add more machines to the cluster as the data processing needs grow. It automatically handles load balancing and fault tolerance, ensuring continuous data processing even in the presence of failures. Storm provides reliable message processing guarantees, including at least once and exactly-once semantics.
  • Extensibility: Storm provides a pluggable architecture that allows users to easily extend its functionality. It supports the integration of custom components and allows developers to create their own spouts (data sources) and bolts (processing units) to meet specific processing requirements. This extensibility makes Storm highly flexible and adaptable to different use cases.

5. Apache Kafka:

Apache Kafka is a distributed streaming platform that handles high-throughput, fault-tolerant, and scalable data streams. It is commonly used for building real-time data pipelines and streaming applications. Kafka provides durable and scalable messaging, allowing applications to publish and subscribe to streams of records.

Key features:

  • Publish-Subscribe Messaging System: Kafka follows a publish-subscribe messaging pattern, where data producers (publishers) send messages to Kafka topics, and data consumers (subscribers) consume those messages from the topics. This decouples producers from consumers and allows multiple consumers to subscribe to the same topic and process data independently.
  • Distributed and Scalable Architecture: Kafka is built to handle high data throughput and supports distributed deployment across multiple nodes in a cluster. It scales horizontally by adding more brokers (nodes) to the cluster, allowing it to handle large volumes of data and high-traffic workloads.
  • Fault Tolerance and Replication: Kafka provides fault tolerance and data durability by replicating data across multiple brokers. Each topic partition can have multiple replicas, with one replica acting as the leader and others as followers. If a broker fails, Kafka automatically promotes one of the follower replicas as the new leader, ensuring continuous availability and data integrity.

6. Google BigQuery:

Google BigQuery is a fully managed serverless data warehouse and analytics platform offered by Google Cloud. It enables fast and scalable analysis of large datasets using a SQL-like query language. BigQuery is designed to handle massive amounts of data and supports automatic scaling and data partitioning.

Key features:

  • Scalability and Performance: BigQuery is designed to handle massive datasets and provide high-performance querying capabilities. It utilizes Google’s infrastructure and distributed computing techniques to automatically scale resources based on the workload, allowing for fast and efficient data processing.
  • Serverless Architecture: BigQuery operates in a serverless model, which means users do not have to worry about managing infrastructure, provisioning resources, or handling software updates. It automatically handles all the underlying infrastructure aspects, allowing users to focus on data analysis and insights.
  • Storage and Querying: BigQuery provides a highly scalable and durable storage system that can store and process terabytes or even petabytes of data. It supports a columnar storage format that optimizes query performance and minimizes data scanning. BigQuery’s SQL-like querying language makes it easy to interactively explore and analyze data.

7. Amazon EMR:

Amazon EMR (Elastic MapReduce) is a cloud-based big data processing service provided by Amazon Web Services (AWS). It allows users to easily provision and manage Hadoop, Spark, and other big data frameworks on a cluster of Amazon EC2 instances. EMR provides scalability, fault tolerance, and integration with other AWS services.

Key features:

  • Scalability and Flexibility: Amazon EMR allows you to process and analyze vast amounts of data by automatically scaling resources based on your workload. You can easily add or remove compute resources to match your processing requirements, ensuring high scalability and flexibility.
  • Hadoop Ecosystem Compatibility: EMR is compatible with the Apache Hadoop ecosystem, including popular frameworks like Apache Spark, Apache Hive, Apache Pig, and Apache HBase. It allows you to leverage these tools and frameworks to perform various data processing and analytics tasks.
  • Managed Cluster Infrastructure: EMR provides a fully managed infrastructure for running big data workloads. It handles the provisioning and management of the underlying cluster, including setting up the required compute instances, configuring networking, and managing cluster health. This eliminates the need for manual infrastructure management, saving time and effort.

8. Microsoft Azure HDInsight:

Microsoft Azure HDInsight is a cloud-based big data processing service provided by Microsoft Azure. It supports various open-source big data frameworks, including Hadoop, Spark, Hive, HBase, and Storm. HDInsight allows users to deploy and manage big data clusters easily and integrates with other Azure services.

9. Cloudera:

Cloudera is a platform that combines different big data processing technologies, including Hadoop, Spark, Hive, Impala, and others. It provides a unified and enterprise-ready platform for big data storage, processing, and analytics. Cloudera offers management tools, security features, and support services for big data deployments.

10. IBM InfoSphere BigInsights:

IBM InfoSphere BigInsights is an enterprise big data platform that leverages Hadoop and Spark for data processing and analytics. It provides tools for data exploration, batch processing, real-time streaming, machine learning, and text analytics. BigInsights integrates with other IBM data management and analytics products.

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Top 10 Data Transformation Tools

What is data transformation?

Data transformation converts data into a format to aid business decisions and identify growth opportunities. The process is also called the ETL process, meaning – extract, transform, and load. It organizes raw data for better indexing in data warehouses. The resulting modernized data infrastructure also aids in detecting and responding to cyber threats and preventing costly breaches. The process is typically performed by developers, data analysts, and data scientists, using software tools to transform the data.

Here are the top 10 data transformation tools:

  1. Alteryx
  2. Trifacta Wrangler
  3. Informatica PowerCenter
  4. Talend Data Integration
  5. Microsoft SQL Server Integration Services (SSIS)
  6. IBM InfoSphere DataStage
  7. Apache Spark
  8. Pentaho Data Integration
  9. SAS Data Management
  10. Syncsort DMX

1. Alteryx:

Alteryx is a comprehensive data preparation and analytics platform that offers a wide range of data transformation capabilities. It provides a visual interface for building workflows and allows users to perform tasks like data blending, cleansing, filtering, aggregation, and joining. Alteryx supports integration with various data sources and offers advanced analytics and predictive modeling features.

Key features:

  • Data Blending and Integration: Alteryx allows users to blend and integrate data from diverse sources, including databases, spreadsheets, cloud services, and more. It provides connectors and adapters for seamless integration with various systems, enabling users to access and combine data easily.
  • Data Preparation and Transformation: Alteryx provides a visual interface for data preparation and transformation tasks. Users can cleanse, reshape, and enrich data using a drag-and-drop workflow design. It offers a comprehensive set of data preparation tools, including data cleansing, parsing, grouping, joining, filtering, and more.
  • Predictive Analytics and Machine Learning: Alteryx integrates advanced analytics and machine learning capabilities into its platform. Users can build predictive models, perform statistical analysis, and apply machine learning algorithms to gain insights from data. It provides a range of statistical and predictive tools to support data-driven decision-making.

2. Trifacta:

Trifacta Wrangler is a self-service data preparation tool that focuses on simplifying the process of cleaning and transforming data. It provides an intuitive interface for visually exploring, cleaning, and structuring data. Trifacta offers automated suggestions for transformations, data profiling, and collaborative features for teams working on data preparation.

Key features:

  • Data Discovery: Trifacta enables users to explore and understand their data through visual profiling and data exploration features. It provides statistical summaries, data distribution visualizations, and data lineage information to help users gain insights into their data.
  • Data Wrangling: Trifacta offers an intuitive and interactive interface for data wrangling. Users can easily clean and transform data by applying various operations such as filtering, splitting, merging, pivoting, and more. Trifacta’s intelligent data wrangling features suggest transformations and provide visual previews of the transformed data in real-time.
  • Machine Learning-Powered Data Transformation: Trifacta leverages machine learning algorithms to automatically suggest and apply transformations based on patterns and relationships in the data. It uses smart patterns and semantic understanding to infer the structure and meaning of the data, making data transformation more efficient and accurate.

3. Informatica PowerCenter:

Informatica PowerCenter is an enterprise-grade data integration and transformation tool. It provides a visual development environment for building data integration workflows and supports a wide range of data transformation operations. PowerCenter offers data quality, metadata management, and advanced scheduling and monitoring capabilities.

Key features:

  • Data Integration: PowerCenter enables the extraction, transformation, and loading of data from diverse sources. It supports a wide range of data formats and provides connectors and adapters for seamless integration with various systems, including databases, files, enterprise applications, cloud services, and more.
  • Data Transformation: Informatica PowerCenter provides a graphical interface for designing data transformation workflows. It offers an extensive set of transformation functions, expressions, and operators to manipulate and cleanse data. Users can visually design complex data transformations using a drag-and-drop interface or custom code.
  • Connectivity and Integration: PowerCenter supports seamless integration with a vast array of data sources and systems. It offers pre-built connectors and adapters for popular databases, file formats, and enterprise applications. It allows users to easily connect to different data sources and integrate data across heterogeneous systems.

4. Talend Data Integration:

Talend Data Integration is a powerful open-source data integration and ETL tool that includes robust data transformation features. It allows users to design data transformation workflows using a visual interface and provides a rich set of pre-built components and connectors. Talend supports data profiling, cleansing, deduplication, and integration with big data platforms.

Key features:

  • Data Integration: Talend Data Integration supports the extraction, transformation, and loading of data from various sources. It offers connectors and adapters for databases, files, cloud services, web services, and more. It allows users to easily connect to different data sources and integrate data across heterogeneous systems.
  • Data Transformation: Talend provides a graphical interface for designing data transformation workflows. It offers a rich set of transformation components, such as data mapping, filtering, sorting, aggregating, and more. Users can visually design complex data transformations using a drag-and-drop interface or custom code.
  • Connectivity and Integration: Talend supports seamless integration with a wide range of data sources and systems. It provides pre-built connectors and adapters for popular databases, enterprise applications, file formats, and cloud services. It allows users to access and integrate data from diverse sources, ensuring data interoperability.

5. Microsoft SQL Server Integration Services (SSIS):

SSIS is a component of Microsoft SQL Server that enables data integration and transformation tasks. It offers a visual development environment for building data transformation workflows and supports various data sources and destinations. SSIS provides a wide range of transformation components and allows scripting for advanced transformations.

Key features:

  • Data Integration: SSIS allows users to extract data from various sources, including databases, files, web services, and more. It supports diverse data formats and provides connectors and adapters for seamless integration with different systems.
  • Data Transformation: SSIS provides a visual interface for designing data transformation workflows. It offers a rich set of transformation tasks, including data cleansing, merging, aggregating, pivoting, and more. Users can define complex data transformations using a drag-and-drop interface or custom code.
  • Parallel Processing: SSIS leverages parallel processing capabilities to optimize data integration and transformation workflows. It can execute tasks in parallel, improving performance and scalability for large datasets and complex transformations.

6. IBM InfoSphere DataStage:

IBM InfoSphere DataStage is an enterprise-level data integration and transformation tool. It offers a graphical interface for designing data integration workflows and includes a set of transformation stages for cleaning, transforming, and enriching data. InfoSphere DataStage supports parallel processing and can handle large volumes of data.

Key features:

  • Data Integration: InfoSphere DataStage enables the extraction, transformation, and loading of data from various sources. It supports diverse data formats, including databases, files, enterprise applications, and web services. It provides connectors and adapters for seamless integration with different systems.
  • Parallel Processing: DataStage leverages parallel processing capabilities to optimize data integration and transformation workflows. It can divide tasks into smaller, parallel processes, improving performance and scalability for large datasets and complex transformations.
  • Data Transformation: InfoSphere DataStage offers a visual interface for designing data transformation workflows. It provides a comprehensive set of transformation functions, operators, and expressions to manipulate and cleanse data. Users can define complex transformations using a graphical interface or custom code.

7. Apache Spark:

Apache Spark is an open-source big data processing framework that includes data transformation capabilities. It provides a distributed computing environment and offers a wide range of transformations and operations on large-scale datasets. Spark supports various programming languages such as Scala, Java, Python, and R.

Key features:

  • Speed: Spark is designed for fast and efficient data processing. It performs in-memory computations, reducing disk I/O and delivering high-speed processing. It can process data up to 100 times faster than traditional big data processing frameworks like Hadoop MapReduce.
  • Distributed Computing: Spark is built for distributed computing, allowing data to be processed in parallel across a cluster of machines. It automatically handles data partitioning, task scheduling, and fault tolerance, enabling scalable and fault-tolerant data processing.
  • Data Processing and Analytics: Spark provides a rich set of APIs and libraries for data processing and analytics. It supports batch processing with its core API, allowing users to perform transformations and aggregations on large datasets. It also offers built-in libraries for SQL, streaming data, machine learning (Spark MLlib), and graph processing (GraphX).

8. Pentaho Data Integration:

Pentaho Data Integration (PDI), also known as Kettle, is an open-source ETL tool. It offers a graphical design environment for building data integration and transformation workflows. PDI provides a rich set of transformation steps, data profiling, and validation features. It supports integration with different data sources and can be extended through plugins.

Key features:

  • Data Integration: Pentaho Data Integration allows users to extract data from various sources, including databases, files, APIs, and enterprise systems. It supports both batch and real-time data integration, enabling seamless data movement across different platforms.
  • Data Transformation: PDI provides a visual interface for designing data transformation workflows. It offers a wide range of transformation steps and functions to manipulate, cleanse, aggregate, and enrich data. Users can easily define data mappings, apply business rules, and perform complex data transformations.
  • Data Quality: Pentaho Data Integration includes data quality features to ensure data accuracy and consistency. It allows users to profile data, identify data quality issues, and implement data cleansing and validation rules. It supports data standardization, duplicate detection, and data enrichment to improve data quality.

9. SAS Data Management

SAS Data Management is a comprehensive suite of data integration and data quality tools provided by the SAS Institute. It offers a range of features and functionalities to manage and transform data effectively.

Key features:

  • Data Integration: SAS Data Management enables the integration of data from various sources, including databases, files, and applications. It provides visual tools for designing data integration workflows and supports both batch and real-time data integration processes.
  • Data Quality: SAS Data Management includes capabilities for data quality management, such as data profiling, cleansing, and standardization. It helps identify and resolve data quality issues, ensuring that data is accurate, complete, and consistent.
  • Data Governance: SAS Data Management facilitates data governance practices by providing tools for data lineage, metadata management, and data stewardship. It allows organizations to define and enforce data quality standards, data access policies, and data usage guidelines.

10. Syncsort DMX

Syncsort DMX (Data Integration and Management for Big Data) is a data transformation tool that enables organizations to efficiently integrate, transform, and manage data across diverse data sources and platforms.

Key features:

  • Data Integration: Syncsort DMX offers powerful data integration capabilities, allowing users to extract data from various sources, including databases, files, and applications. It supports both batch and real-time data integration processes, enabling the seamless movement of data across systems.
  • Data Transformation: Syncsort DMX provides a visual interface for designing data transformation workflows. It offers a wide range of built-in transformation functions and operations to manipulate and enrich data. Users can easily cleanse, aggregate, join, filter, and transform data to meet their specific requirements.
  • Data Quality: Syncsort DMX includes data quality features to ensure data accuracy and consistency. It enables data profiling to identify data quality issues, such as missing values, duplicates, or inconsistencies. It also offers data standardization and validation capabilities to cleanse and enhance data quality.
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Top 10 Data Analytics Tools

What are Data Analytics Tools

Data analytics tools are software applications or platforms designed to facilitate the process of analyzing and interpreting data. These tools help businesses and organizations extract valuable insights from large volumes of data to make data-driven decisions and improve performance. Data analytics tools typically offer various features and functionalities to perform tasks such as data cleansing, data transformation, statistical analysis, data visualization, and predictive modeling. They often provide intuitive interfaces, drag-and-drop capabilities, and pre-built algorithms to simplify and automate the data analysis process. Some data analytics tools also integrate with other systems, databases, and data sources to gather data from multiple platforms.

Here are some popular data analytics tools:

  1. Tableau
  2. Power BI
  3. Python (including libraries like Pandas, NumPy, and scikit-learn)
  4. R
  5. SQL (Structured Query Language)
  6. SAS
  7. Alteryx
  8. RapidMiner
  9. KNIME
  10. QlikView

1. Tableau:

One of the most in-demand, market-leading Business Intelligence tools, Tableau is used to analyze and visualize data in a very easy format. It is a commercially available tool that can be used to create extremely interactive data visualization and dashboards without having a lot of expertise in coding or technical knowledge.

Key features:

  • Tableau is an easy-to-use tool that can be used for understanding, visualizing, and analyzing data.
  • It provides fast analytics, that is, it can be used to explore any type of data, for instance, spreadsheets, databases, data on Hadoop and cloud services, etc.
  • It can be used to create smart dashboards for visualizing data using drag-and-drop features. Moreover, these dashboards can be easily shared live on the web and mobile devices.

2. Power BI:

Power BI is yet another powerful business analytics solution by Microsoft. You can visualize your data, connect to many data sources and share the outcomes across your organization. With Power BI, you can bring your data to life with live dashboards and reports. Power BI can be integrated with other Data Analytics Tools, including Microsoft Excel. It offers solutions such as Azure + Power BI and Office 365 + Power BI. This can be extremely helpful to allow users to perform data analysis, protect data across several office platforms, and connect data as well.

Key features:

  • Power BI comes in three different versions: Desktop, Pro, and Premium. The Desktop version is free of cost while the other two are paid.
  • It allows importing data to live dashboards and reports and sharing them.
  • It can be integrated very well with Microsoft Excel and cloud services like Google Analytics and Facebook Analytics so that Data Analysis can be seamlessly done.

3. Excel:

Microsoft Excel is a widely used spreadsheet tool that includes built-in data analytics functionalities. It allows users to perform data cleaning, analysis, and visualization using formulas, pivot tables, and charts. Excel is accessible to users of all skill levels and supports large datasets.

Key features:

  • Microsoft Excel is a spreadsheet that can be used very efficiently for data analysis. It is part of Microsoft’s Office suite of programs and is not free.
  • Data is stored in Microsoft Excel in the form of cells. The statistical analysis of data can be done really very easily using the charts and graphs which are offered by Excel.
  • Excel provides a lot of functions for data manipulation like the CONCATENATE function which allows users to combine numbers, texts, etc. into a single cell of the spreadsheet. A variety of built-in features like Pivot tables (for the sorting and totaling of data), form creation tools, etc. make Excel an amazing choice as a Data Analytics Tool.

4. Python:

Python is one of the most powerful Data Analytics tools that is available to the user. It comes with a wide set of packages/libraries. Python is a free, open-source software that can be used for a high level of visualization and comes with packages such as Matplotlib, and Seaborn. Pandas is one of the widely used data analytics libraries that comes with Python. Most programmers prefer to learn Python as their first programming language due to its ease and versatility. It is a high-level, object-oriented programming language.

Key features:

  • One of the fastest programming languages of the world today, Python is being used in a lot of industries like Software Development, Machine Learning, Data Science, etc.
  • Python is an Object Oriented Programming language.
  • It is easy to learn and has a very rich set of libraries because of which it is being heavily used as a Data Analytics Tool. Two of the most well-known libraries of Python – Pandas and NumPy – are being used a lot as they provide lots of features for Data Manipulation, Data Visualization, Numeric Analysis, Data Merging, and many more.

5. R:

R is the leading analytics tool in the industry and is widely used for statistics and data modeling. It can easily manipulate data and present it in different ways. It has exceeded SAS in many ways like capacity of data, performance, and outcome. R compiles and runs on a wide variety of platforms viz -UNIX, Windows, and macOS. It has 11,556 packages and allows you to browse the packages by category. R also provides tools to automatically install all packages as per user requirements, which can also be well assembled with Big data.

Key features:

  • Data Manipulation: R provides powerful tools for data manipulation, including functions for filtering, sorting, merging, reshaping, and aggregating data. Packages like dplyr and tidyr offer intuitive and efficient syntax for data manipulation tasks.
  • Statistical Analysis: R has extensive built-in functions and packages for statistical analysis. It provides a wide range of statistical tests, including hypothesis testing, regression analysis, ANOVA, time series analysis, and non-parametric methods. R allows users to conduct descriptive statistics, inferential statistics, and exploratory data analysis.
  • Data Visualization: R offers a variety of packages for data visualization, including ggplot2, lattice, and base graphics. Users can create high-quality visualizations, such as scatter plots, bar charts, line graphs, histograms, and heatmaps, to effectively communicate insights and patterns in the data.

6. SAS:

SAS is a statistical software suite widely used for data management and predictive analysis. SAS is proprietary software, and companies need to pay to use it. A free university edition has been introduced for students to learn and use SAS. It has a simple GUI. Hence, it is easy to learn. However, a good knowledge of SAS programming knowledge is an added advantage to using the tool. SAS’s DATA step (The data step is where data is created, imported, modified, merged, or calculated) helps with inefficient data handling and manipulation.

Key features:

  • Data Management: SAS provides powerful data management capabilities to handle data integration, cleansing, and transformation tasks. It supports data extraction from various sources, data quality checks, data profiling, and data manipulation.
  • Advanced Analytics: SAS offers a vast array of advanced analytics techniques and algorithms. It provides statistical analysis capabilities, including descriptive statistics, regression analysis, hypothesis testing, and time series analysis. SAS also supports advanced analytics techniques like data mining, machine learning, and text analytics.
  • Business Intelligence and Reporting: SAS includes tools for business intelligence and reporting, allowing users to create interactive dashboards, reports, and visualizations. It offers flexible reporting options, ad hoc querying, and data exploration functionalities.

7. Alteryx:

Alteryx is a data analytics and data preparation tool that allows users to blend, cleanse, and analyze data from various sources. It provides a user-friendly interface and a range of features to facilitate the data preparation and analytics process.

Key features:

  • Data Blending and Preparation: Alteryx enables users to integrate and blend data from multiple sources, such as databases, spreadsheets, and cloud-based platforms. It offers a visual workflow interface where users can drag and drop tools to manipulate, transform, and clean data. Alteryx supports a wide range of data preparation tasks, including joining, filtering, sorting, aggregating, and pivoting data.
  • Predictive Analytics and Machine Learning: Alteryx includes a set of tools for performing advanced analytics and machine learning tasks. Users can build predictive models, and perform regression analysis, classification, clustering, and time series forecasting. Alteryx integrates with popular machine learning libraries and frameworks, allowing users to leverage advanced algorithms and techniques.
  • Spatial and Location Analytics: Alteryx provides capabilities for spatial and location-based analytics. Users can perform geocoding, and spatial analysis, and create custom maps and visualizations. Alteryx supports integration with mapping platforms and spatial data sources, enabling users to incorporate geographical context into their analysis.

8. RapidMiner:

RapidMiner is a powerful integrated data science platform. It is developed by the same company that performs predictive analysis and other advanced analytics like data mining, text analytics, machine learning, and visual analytics without any programming. RapidMiner can incorporate any data source type, including Access, Excel, Microsoft SQL, Tera data, Oracle, Sybase, IBM DB2, Ingres, MySQL, IBM SPSS, Dbase, etc. The tool is very powerful that can generate analytics based on real-life data transformation settings, i.e. you can control the formats and data sets for predictive analysis.

Key features:

  • RapidMiner makes use of a client and server model. The server of RapidMiner can be offered both on-premises or in public or private cloud infrastructures.
  • It has a very powerful visual programming environment that can be efficiently used for building and delivering models in a fast manner.
  • RapidMiner’s functionality can be extended with the help of additional extensions like the Deep Learning extension or the Text Mining extension which are made available through the RapidMiner Marketplace. The RapidMiner Marketplace provides a platform for developers to create data analysis algorithms and publish them to the community.

9. KNIME:

KNIME is an open-source data analytics platform that allows users to perform data integration, preprocessing, analysis, and visualization through a visual workflow interface. It supports a wide range of data sources and offers extensive data manipulation and machine-learning capabilities.

Key features:

  • KNIME provides a simple, easy-to-use drag and drops graphical user interface (GUI) which makes it ideal for visual programming (Visual programming is a kind of programming language which helps in letting humans describe processes using illustration.).
  • KNIME offers in-depth statistical analysis and no technical expertise is required to create workflows for data analytics in KNIME.

10. MATLAB:

MATLAB is a programming language and computing environment commonly used for numerical analysis, data visualization, and algorithm development. It provides a comprehensive set of tools and functions for data analytics and scientific computing.

Key features:

  • Numerical Analysis: MATLAB offers a rich set of mathematical functions and algorithms for numerical analysis. It provides built-in functions for linear algebra, optimization, interpolation, numerical integration, and differential equations.
  • Data Visualization: MATLAB provides powerful data visualization capabilities to explore and present data effectively. It offers a variety of plotting functions, including 2D and 3D plots, histograms, scatter plots, and surface plots. Users can customize plots, add annotations, and create interactive visualizations.
  • Data Import and Export: MATLAB supports importing and exporting data from various file formats, such as spreadsheets, text files, databases, and image files. It provides functions and tools for data preprocessing and cleaning, including handling missing data, data alignment, and data transformation.
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Top 10 Data Science Platforms

Data science platforms are comprehensive software systems that provide an integrated environment for performing end-to-end data analysis and machine learning tasks. These platforms typically combine a variety of tools, libraries, and features to streamline and enhance the data science workflow.

Some key components and functionalities commonly found in data science platforms are:

  1. Dataiku
  2. Databricks
  3. Alteryx
  4. KNIME
  5. RapidMiner
  6. Domino Data Lab
  7. H2O.ai
  8. Azure Machine Learning
  9. Google Cloud AI Platform
  10. Amazon SageMaker

1. Dataiku:

Dataiku offers an advanced analytics solution that allows organizations to create their own data tools. The company’s flagship product features a team-based user interface for both data analysts and data scientists. Dataiku’s unified framework for development and deployment provides immediate access to all the features needed to design data tools from scratch. Users can then apply machine learning and data science techniques to build and deploy predictive data flows.

Key features:

  • Data Integration: Dataiku provides a unified interface to connect and integrate data from various sources, including databases, data lakes, cloud storage, and APIs. It supports both batch and real-time data ingestion, allowing users to prepare and cleanse data for analysis.
  • Data Preparation: The platform offers a range of data preparation capabilities, such as data cleaning, transformation, enrichment, and feature engineering. Users can perform data wrangling tasks using a visual interface or by writing code in languages like SQL, Python, or R.
  • Visual Data Science: Dataiku provides a collaborative and visual environment for data scientists to build and experiment with machine learning models. It offers a wide array of pre-built algorithms, along with the flexibility to bring in custom code. Users can visually construct workflows, leverage automated machine learning (AutoML), and explore model performance.

2. Databricks:

Databricks Lakehouse Platform, a data science platform and Apache Spark cluster manager were founded by Databricks, which is based in San Francisco. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines and data modeling.

Key features:

  • Data Integration: Dataiku provides a unified interface to connect and integrate data from various sources, including databases, data lakes, cloud storage, and APIs. It supports both batch and real-time data ingestion, allowing users to prepare and cleanse data for analysis.
  • Data Preparation: The platform offers a range of data preparation capabilities, such as data cleaning, transformation, enrichment, and feature engineering. Users can perform data wrangling tasks using a visual interface or by writing code in languages like SQL, Python, or R.
  • Visual Data Science: Dataiku provides a collaborative and visual environment for data scientists to build and experiment with machine learning models. It offers a wide array of pre-built algorithms, along with the flexibility to bring in custom code. Users can visually construct workflows, leverage automated machine learning (AutoML), and explore model performance.

3. Alteryx:

Alteryx offers data science and machine learning functionality via a suite of software products. Headlined by Alteryx Designer which automates data preparation, data blending, reporting, predictive analytics, and data science, the self-service platform touts more than 260 drag-and-drop building blocks. Alteryx lets users see variable relationships and distributions quickly, as well as select and compare algorithm performance with ease. No coding is required while the software can be deployed in the cloud, behind your own firewall, or in a hosted environment.

Key features:

  • Data Integration and Blending: Alteryx allows users to connect and integrate data from multiple sources, such as databases, spreadsheets, cloud platforms, and APIs. It provides a visual interface to blend and join data from different sources, enabling users to create a unified view of their data for analysis.
  • Data Preparation and Cleaning: Alteryx offers robust data preparation capabilities, allowing users to cleanse, transform, and reshape data easily. It provides a visual workflow designer that enables users to perform tasks like data cleansing, data quality profiling, data imputation, and data enrichment. Users can create reusable data preparation workflows for efficient data cleaning and transformation.
  • Predictive Analytics and Machine Learning: Alteryx provides a range of advanced analytics tools and machine learning capabilities. It includes a variety of pre-built predictive models and algorithms, allowing users to perform tasks like regression, classification, clustering, time series analysis, and text analytics. Alteryx also offers integration with popular machine-learning frameworks such as Python and R.

4. KNIME:

KNIME shines in end-to-end workflows for ML and predictive analytics. It pulls big data from huge repositories including Google and Twitter and is often used as an enterprise solution. You can also move to the cloud through Microsoft Azure and AWS integrations. It’s well-rounded, and the vision and roadmap are better than most competitors.

Key features:

  • Visual Workflow Design: KNIME provides a visual workflow design interface, allowing users to create data processing and analysis workflows by dragging and dropping nodes onto a canvas. Users can connect nodes to define the flow of data and operations, enabling a visual representation of the data analytics process.
  • Data Integration and Transformation: KNIME offers extensive data integration capabilities, allowing users to connect and merge data from various sources, including databases, file formats, APIs, and web services. It provides a range of data transformation and manipulation nodes for cleaning, filtering, aggregating, and reshaping data.
  • Pre-built Analytics and Machine Learning: KNIME includes a rich library of pre-built analytics and machine learning algorithms. Users can leverage these algorithms to perform tasks such as classification, regression, clustering, text mining, time series analysis, and image processing. KNIME also supports integration with popular machine learning frameworks, such as TensorFlow and scikit-learn.

5. RapidMiner:

RapidMiner offers a data science platform that enables people of all skill levels across the enterprise to build and operate AI solutions. The product covers the full lifecycle of the AI production process, from data exploration and data preparation to model building, model deployment, and model operations. RapidMiner provides the depth that data scientists need but simplifies AI for everyone else via a visual user interface that streamlines the process of building and understanding complex models.

Key features:

  • Visual Workflow Design: RapidMiner offers a visual workflow design interface that allows users to create end-to-end data analytics processes by connecting predefined building blocks called operators. Users can drag and drop operators onto the canvas, define the flow of data, and configure parameters using a graphical interface.
  • Data Preparation: RapidMiner provides a wide range of data preparation tools to clean, transform, and preprocess data. Users can perform tasks such as data cleansing, feature engineering, attribute selection, data imputation, and outlier detection. It offers an extensive library of operators for data manipulation and transformation.
  • Machine Learning and Predictive Analytics: RapidMiner includes a rich set of machine learning algorithms and predictive modeling techniques. Users can leverage these algorithms to perform tasks like classification, regression, clustering, association rule mining, time series analysis, and text mining. RapidMiner also supports ensemble learning and automatic model selection.

6. Domino Data Lab:

Domino Data Lab is a data science platform that helps organizations manage, deploy, and scale data science models efficiently. It provides a collaborative environment for data scientists and data teams to work on projects and streamline the end-to-end data science workflow.

Key features:

  • Model Management: Domino Data Lab offers robust model management capabilities. It allows users to track, version, and organize their models effectively. Users can compare different model versions, manage dependencies, and maintain a centralized repository of models for easy access and reuse.
  • Collaborative Workspace: Domino Data Lab provides a collaborative workspace where data scientists and teams can collaborate on projects. It offers a central hub for sharing code, notebooks, and research findings. Users can work together in real-time, leave comments, and have discussions within the platform.
  • Experimentation and Reproducibility: Domino Data Lab enables data scientists to conduct experiments in a controlled and reproducible manner. Users can capture and document their workflows, including code, data, and environment settings. This ensures that experiments can be reproduced and validated, promoting transparency and collaboration.

7. H2O.ai:

H2O.ai is an Open-source and freely distributed platform. It is working to make AI and ML easier. H2O is popular among novice and expert data scientists. H2O.ai Machine learning suite.

Key features:

  • It works across a variety of data sources, including HDFS, Amazon S3, and more. It can be deployed everywhere in different clouds
  • Driverless AI is optimized to take advantage of GPU acceleration to achieve up to 40X speedups for automatic machine learning.
  • Feature engineering is the secret weapon that advanced data scientists use to extract the most accurate results from algorithms, and it employs a library of algorithms and feature transformations to automatically engineer new, high-value features for a given dataset.

8. Azure Machine Learning:

The Azure Machine Learning service lets developers and data scientists build, train, and deploy machine learning models. The product features productivity for all skill levels via a code-first and drag-and-drop designer and automated machine learning. It also features expansive MLops capabilities that integrate with existing DevOps processes. The service touts responsible machine learning so users can understand models with interpretability and fairness, as well as protect data with differential privacy and confidential computing. Azure Machine Learning supports open-source frameworks and languages like MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R.

9. Google Cloud AI Platform:

Google Cloud AI Platform is a cloud-based data science and machine learning platform provided by Google Cloud. It offers a suite of tools and services to help data scientists and machine learning engineers build, train, and deploy machine learning models at scale.

Key features:

  • Machine Learning Pipelines: Google Cloud AI Platform provides a managed and scalable environment for building end-to-end machine learning pipelines. It supports the entire workflow, including data ingestion, preprocessing, feature engineering, model training, and evaluation.
  • Distributed Training and Hyperparameter Tuning: The platform offers distributed training capabilities, allowing users to train large-scale models efficiently. It also provides built-in hyperparameter tuning to automate the process of finding optimal hyperparameter settings.
  • Pre-built Machine Learning Models: Google Cloud AI Platform offers a repository of pre-built machine learning models and APIs, such as image recognition, natural language processing, and speech-to-text conversion. These pre-trained models can be easily integrated into applications and workflows.

10. Amazon SageMaker:

Amazon SageMaker is a fully managed machine learning service provided by Amazon Web Services (AWS). It offers a comprehensive platform for building, training, and deploying machine learning models at scale. SageMaker provides a range of tools and services that facilitate the end-to-end machine-learning workflow.

Key features:

  • Notebook Instances: SageMaker provides Jupyter Notebook instances that are fully managed and scalable. These instances allow data scientists to perform interactive data exploration, model development, and experimentation in a collaborative environment.
  • Built-in Algorithms and Frameworks: SageMaker includes a collection of built-in machine learning algorithms and frameworks, such as XGBoost, TensorFlow, PyTorch, and scikit-learn. These pre-built algorithms and frameworks enable users to quickly build and train models without the need for extensive custom development.
  • Custom Algorithm Development: SageMaker allows users to bring their own custom algorithms and models. It provides a flexible and scalable infrastructure for training and deploying custom models, giving users full control over the training process.
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What are the next gen. projects in the Jira cloud?

Hi, my loving friends. Welcome to this course. In this article, I will explain to you about the next-gen project. This is the new feature of Jira and atlas Sian introduced it in the year 2018 so, let’s have a look at the agenda of this content. In this, we will learn what are the next-gen projects, how to create next-gen projects? How can we create issues and add issue types in next-gen projects and how to configure the board? So. Let’ start.

What are the next-gen projects?

Next-gen projects are the newest projects in Jira software and it is only available on the cloud platform to the server one. If you are using a server the server platform and you wouldn’t be able to sit. The third one is configured by project team members. Any team member with the project’s admin role can modify the setting of their next-gen projects. There is no need to take the help of the Jira administrator to add the issue types and to add some fields to your screen. The Fourth one is it is easier and faster to configure than classic projects. You can easily configure setting like issue types and fields with drag-and-drop editing and reordering all in a single place because the best part of next-gen projects is the ability to enable and disable the features and it allows you to scale your projects as your team grows and tailor your projects to your team changing needs so, this is the best thing in the next-gen projects. And before going forward, I would like to tell you one more thing like atlas Sian is building the next-gen Jira software from the ground up.

How to create next-gen projects?

So, it doesn’t yet have all the features that classic projects have. And they are a lot of differences between the next-gen and the classic projects like if I’m talking about the configurations then in the classic projects. We are able to share the configurations from one project to another. But in the next gen, you can’t share it. If you did some configurations for the particular projects in the next-gen, you wouldn’t be able to share that configuration with another project. And there are many more like estimations in the classic projects, you have the options, you can give the estimations in story point in your basis but in the next-gen, there is only one option is available and that is a story point. If you will go to the next-gen cloud instance and see how can you create the next-gen projects and configure them? There will be your cloud instance and you will create the next-gen projects from there. You will see that there will be two options are available one is classic and the other is next-gen. if that particular option is created out for you then this is the permission issue so, before creating the next-gen project. I would like to tell you about the permission so, once you will click on the Jira setting and go to the global permissions, there will be your global permission schema, and create next-gen project option will be there. At the time, you will have permission to create the next-gen project. You will click on the next-gen project and you will see the interface is similar to the classic.

You can simply change the template from there but you will see only two templates are available one is scrum and another one is Kanban. If you will go with Kanban and name the project, you will see the access option (open, limited, private) and project key as a classic one. If you want to change the project key then you can do it there. You will go forward to clicking the create button then the next-gen project board will appear which would be similar to the classic one but what is the difference and how can you identify that this is the next-gen project but what will happen? If you will haven’t created it yet you didn’t create it. Maybe, you are using the project which is created by another one. You will see in the bottom line that you are in the next-gen project. You can find out and identify that you are using the next-gen project. You will see there are the options which are roadmap, board, pages, add an item, and project setting. A Roadmap is a good option which is given by the next-gen project. I will discuss this later in the course. I hope this will give you exact direction in the way of your Jira learning and about its next-gen project. So, you will stay tuned with this course for further next information regarding Jira next-gen project.

Related Video:

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How to set up a Jira cloud instance?

Hi dears, welcome to the course study of Jira. Today, through this article, I will explain to you how you will set up a Jira instance so that you can start to use Jira software for your projects. I have miscellaneous agenda for you. On which topics you will learn is types of Jira instance, what is the difference between server and Cloud instance? And how to create a cloud instance of Jira? So, let’s move forward to learn more about Jira concepts

Jira instance

Basically, there are two types of Jira instances, the first one is cloud and the second one is server. I will tell you what is the difference between the cloud and server instances and which one you should use if you are a beginner to learn about the Jira software. So, let’s move forward to learn about the cloud and the server instances.

Cloud vs server instance

Here I will discuss the cloud instance first. So, what is a cloud instance? In the cloud instance rather than installing and maintaining, your product on your own server Atlas Sian host and set up your Jira application on the cloud for you so, this is the cloud instance and according to me, this is the best instance for the beginner, who want to learn from the very beginning. It is very fast because it’s easy to set up and get started in minutes as soon as you sign up. You may start working and inviting the team members and the second one is reduced costs because it helps to save money on the physical hardware maintenance installation support and any other hidden administration cost as well. This is a cloud instance so, everything is on the cloud and there is no need to bear the cost of your own hardware. The third one is no need to upgrade because on the cloud you get immediate access to the latest version on Jira so, there is no need to upgrade the cloud instance. The fourth one is security, it is very secure because atlas Sian takes the responsibility to maintain the security and compliances of your data and organization so, I describe the benefits of the cloud instances but I want to tell you one more thing about you don’t have direct access to change the database structure, file system, or other server infrastructure because you have a cloud instance and atlas Sian already set up your cloud instance. So, they will not give you administrator access. Let’s learn more about the server instance. What is a server instance? In the server instance, basically, you install, host, and run the Atlas Sian’s product on your own hosting environment. So, basically, those teams will prefer that want to manage all the details on their own and are able to handle the complexity of setting up and hosting the product on their own server. So, if your organization is very big and you have a department of administrators. They can manage the administrator task of the Jira or the server so, you can go with the server version but you are working in a startup and you want to learn your own then go with a cloud one.

In this complete course, I will also use the cloud instance because as I already told you, it is very easy to set up and everyone can access it from anywhere but in the server instance may be the organization installed it on the internal environment so, you would not be able to access it from outside the organization but in the cloud instance, you may access anywhere as I told you I will explain the cloud version in this course so may be the people who are using the server one they will see some differences in the UI because the cloud and the server UI is different in the server we have the navigation on the top but in the cloud we have the navigation in the left hand side and many more other differences in the UI so as I told you the difference between the cloud and the server instances so let’s move forward and see how can you create the cloud instance of Jira so for creating the cloud instance of JIRA so, you will go to the atlas Sian website, there is an atlas Sian official website www.atlassian.com once you will click on the product tab then you can see the complete list of the atlas Sian the product but there you will go with the Jira software so, you will click on Jira software.  It will take you on that page you may try it free then you can see the three plans and there will be cloud one. You may see try the product in the cloud and that is a free 7 days trail period you can go with any of the packages so, you will go there with any of the packages and you will go there with that one so, you will click on try it free, you will say that is a 7 day free cloud trial and it will give you the full access to all features, you can add the unlimited users and access to sales and technical support so, you will create the account there so you will use your ID and you will click on agree and sign up once you will end with the sign up process then you will see the screen where they will ask you to check your inbox and confirm your email ID so, you will go to your email so to verify that email you will click yes verify and it will redirect to you to the page if you want to select what type of team with do you work in then you can but for that you will skip and see the URL there that is a JIRA all sort of gentle that you have mentioned at the time of creating the account and you can invite your team with the help of the email address if you will enter the email address there and send the invitation then the people will get the email will get the invite and they can accept and use this environment and that JIRA instance for now you will skip that  and there they will ask are you new to Jira or are you already experienced with Jira then you can find out for now. That will be the next page where you can see there are the two types of templates are available first one is the classic template and the second one is a next-generation template. You can see there the Kanban is scrum and the bug tracking three templates are available and these three templates come under the software category so if you will go and will select all types you can see there are many more templates are available which are related to business. In this course, that’s it for today this is a process where you can create the cloud instance.

Related video:

 

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What are Agile and DevOps?

Agile – Agile refers to software development methodologies based on iterative development, where requirements and solutions evolve through collaboration between self-organizing cross-functional teams. It is a process that promotes disciplined project management that encourages inspection and adaptation, self-organization and accountability, rapid delivery of high-quality software, and aligns the development of software with customer needs and company goals.

DevOps – DevOps is originated from Dev and Ops word which is development and operation. It has come out as a cultural philosophy and practice change which makes the collaboration between Development and operation team to fasten the software development and delivery. DevOps is originated from Dev and Ops word which is development and operation. It has come out as a cultural philosophy and practice change which makes the integration between Development and operation team fasten the software development and delivery. At the time of development of software its has always been seen that security is always a major concern, so for this DevSecOps came between that look after the security concern from the development process. It ensures the finished application is secured at all aspects of running the application.

What are some common misconceptions about Agile and DevOps?

DevOps & Agile Implementation - Landmark System & Solutions Pvt.Ltd

Agile and DevOps both are meant to help in functioning smoothly and efficiently the development and release process. But still, some people have spread the rumor which harming the DevOps and Agile image. So for today, we going to discuss some of the myths/ Misconceptions. Let’s start.

DevOps Misconceptions:-

DevOps Requires Agile – DevOps doesn’t require agile methodology, it’s a whole process on its own. DevOps and agile both have different ways to work and to develop software. DevOps is a process that integrates the development and operations team to enable continuous development and delivery of software whereas Agile emphasizes the iteration of development and testing in the SDLC process (which means it breaks down the product into smaller pieces and integrates them for final testing to build a ready to use application).

You Can’t have DevOps without Cloud – Basically, it’s not true. There are ways to use DevOps separately as DevOps is a philosophy rather than a technology, it can scale and adapt to change much better in comparison to cloud computing. But still with this advantage, without the ability to set up and provision new machines programmatically and without the cloud’s API, DevOps functioning feels limited because the cloud provides the ability to flexibly manage the computing resources we need, So cloud is important for DevOps functioning efficiently.

DevOps Doesn’t Work for Large, Complex Systems – It is not like this. Earlier It was said, that the waterfall model is best for large and complex systems, but it’s not true. DevOps has been led just to remove all vulnerabilities which all the older models had in the development process to function well whether it is for a large or complex system. It happening because DevOps using modern methods to make the tasks easy and day by day things are improving. So the conclusion is DevOps fits over all types of systems.

It is Exclusive to Native Internet Companies – DevOps is an approach that is widely used by the whole world. So DevOps can’t be limited to any extent which means it is inclusive to native internet companies.

DevOps Requires Teams’ Physical Proximity – It’s a baseless myth I have ever heard. DevOps never require any kind of physical proximity. DevOps can work without physical appearance. With the help of the latest techniques like remote workers, third-party contractors, and cloud service providers, DevOps can perform much better than anyone as well as With the right tools and frameworks to support communication and collaboration in the DevOps lifecycle could give an effective result.

DevOps is Only for Continuous Delivery – It would be wrong if it is said like this. DevOps is not only for continuous delivery it is for continuing operations as well. the duty of its to ensure the continuous development, delivery as well as deployment to the market so the organization can achieve the required goal. Even though After deployment DevOps teams monitor the performance as well to push the updates. So the DevOps works are much more than only continuous delivery of software.

Soft Skills Aren’t Necessary – Soft skills needs everywhere whether it is in DevOps or not. Dev and op team is bound to work with each other so it’s important to be polite with each other in terms of working efficiently. Sometimes some organizations provide such soft skill training as well to be one of the well-disciplined organizations.

Agile Misconceptions:-

Agile models cannot work with other models – It’s not true, Instead, Agile methodology offers more flexibility to their users to include various aspects of traditional methods into it. The stages of product development cycles of the agile method are shorter and multiple, and they are complete like other traditional methods. In such a manner, agile methods are compatible with the processes of traditional methods. The only way to combine the agile method with a traditional plan-driven model like the waterfall model, waterfall uses the sprints of the agile method within the linear structure to start work for the next stage without completing the work of the previous stage.

No planning is required for the projects – The development process of agile is neither plan-driven nor has Gantt charts or WBS but still its plan at the number of points like Dev Sprint Planning of formalized ceremonies comprising PO and PBR to address the goals and priorities of the project of the team. The ceremony is related to the owner of the product communicating the details to the project team about their requirements and the project manager and the team establishing their priorities to complete the task as planned, to build and run the project successfully.

Role of management is eliminated in agile methodology – The is also one of the myths because the role of every person is defined in agile as well as the owner of the product involved as the manager of the project. The supervision of the project ( the goals and priorities of the project team and leading the team to accomplish the task ) is the responsibility of the product owner.
In agile projects the product owner, along with a Scrum Master who is responsible to ensure the development teams of the project complete the tasks within each sprint by working in the best condition.

Agile is specifically for Software Development – Initially agile started with the development of software but later it emerged as a complete methodology, which can be used in distinctive projects where the ability of change and continuation is higher and feedback cycles are shorter. So again it’s a myth.

Agile means no need for software testing – In Agile, test cycles are planned for every sprint with the user stories that developers intend to address in that sprint. Testing is the central part of the success of an agile development lifecycle and it keeps continuous until the final product meets all requirements.

Agile means DevOps – Both are different from each other. Agile is based on iterative development and DevOps is a cultural change that works with the integration between dev and op teams to continuous development and delivery of software.

Conclusion

In this blog, we have discussed the Definition of Agile and DevOps, and some misconceptions about both of them. As I have mentioned above both are good to each other but in a current scenario, DevOps is much better than anything. but still, all are good at their place and Agile is also one of them and I have tried to remove some of the misconceptions above. Hope so it will be helpful to you guys.

Training place

If you looking for training in DevOps, DevSecOps, and SRE, then you guys can consider about DevopsSchool. It is a platform where you guys can get certified training as well as certification in any particular tools related to DevOps. It promises the best environment as well as the best trainer who holds a good experience in DevOps and also they provide real-time projects which can boost your carrier as well as a resume.

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What are DevOps, DevSecOps, and SRE, and differences among them?

DevOps – DevOps is the combination of culture, practices, and tools that increase an organization’s ability to deliver applications and services at high quality, as well as automate and integrate the processes between development and IT teams.


DevOps teams use tools to automate the process, which helps to increase reliability and efficiency.
DevOps ensures fast software delivery with minimum problems to fix and faster solution to problems.
The term DevOps has been made up of two words development and operations.


DevOps is a process that permits the Developer and operation teams to collaborate with each other to manage the whole application development life cycle, i.e. development, testing, deployment, monitoring, etc. DevOps aims to shorten the period and cost of development of the application.

DevSecOps – DevSecOps is a useful umbrella term that collects the processes introduced by organizations who want to run their operations on AWS, Azure, and Google cloud.


DevSecOps is about not only making software easily installable but making the process of installing it more secure and usable.

DevSecOps is not only making the software installation easy, but it makes the installation process more secure and usable as well.


Prior, the development cycles lasted for months or even years, and the release of new versions or software updates of their applications used to be released just once or twice a year.
It gave enough time for quality assurance and security testing teams to carry out security measures which is make the process very slow.


But these outdated security practices or separate security teams cannot keep up with the speeds of DevOps initiatives.
This vulnerability leads to the evolution of the DevSecOps methodology, where the development, operation, and security team, work together and share end-to-end responsibilities in the entire development life cycle to finish the project in less time.


DevSecOps methodology automates the integration of security at every stage of the software development lifecycle, from the initial design.


DevSecOps integrates the security of application and infrastructure seamlessly in Agile and DevOps processes and tools.

SRE – SRE stands for site reliability engineering.


In around 2000 Google realize DevOps is good as it is but there is something else that can be done. So there were a lot of different ideas flowing around then Google come up with this idea called an SRE.


It is a software engineering approach to operations where an SRE team uses software as a tool to manage systems and solve problems and automate operational tasks.


So basically, SRE takes the tasks which have been done often manually by the operation teams and instead of giving them to engineers or Operations teams who use software or automation to solve these problems, they do it themselves and manage the production environment.


In other words, SRE teams are made up of software engineers who build and implement software to improve the reliability of their systems.


SRE teams are responsible for how code is deployed, configured, and monitored as well as checks for the availability, latency, change management, emergency response as well as capacity management of service in production.


So how SRE does all these things, Basically it helps to determine the new features that are being launched, they test it across a few different metrics, so they check it across these things called SLA (Service Level Agreement), SLI (Service level indicator), and SLO (service level objectives).

Differences between DevOps, DevSecOps, and SRE

DevOps, DevSecOps, and SRE all work to bridge the gap between development and operation teams to deliver faster and reliable services.

DevOps and DevSecOps


DevOps is the process of integrating development and operations and focuses on eliminating the communication gap between different teams so that the whole code development and deployment process is done faster whereas DevSecOps solves the security concerns along with deployment.


DevOps is only responsible for Development and operational tasks related to a single project but DevSecOps suggests that security is everyone’s responsibility.


DevOps team requires the skillset of Linux fundamentals and scripting knowledge of various tools and technologies whereas DevSecOps engineers should be skilled with addressing the vulnerabilities with automated security tools. Need to have knowledge in cloud security and provide support to infrastructure users.


DevOps has some benefits like speed, rapid delivery, reliability, scale, improved collaborations, security whereas DevSecOps has improved agility, considers security automation, keeps security as code.


Automation is done for security testing so the development is tested on regular basis.

The report generates if any vulnerabilities are found during CI and CD. DevSecOps never allow security to get compromised. whereas automation in DevOps is for releasing codes in a higher environment. This helps developers to know about the changes has done by the members and to work accordingly.


Monitoring the security incident is done through incident management. Proper standards are created to raise Thus security concerns are managed in DevSecOps. In DevOps, Application infrastructure is managed through codes as infrastructure as codes. Here designing and managing the code is happen on the same platform.

DevOps And SRE


DevOps reduce silos whereas SRE doesn’t concern about the silos. DevOps involve unexpected failures, whereas SREs focus on no failure happening at all.


The automated workflow needs constant monitoring, in this process DevOps team ensures software is working effectively whereas SRE believes that operations are a software issue.


SRE practice involves a contribution from each level of the organization whereas DevOps is all about development and operations only.


SRE uses developers and tools to solve IT operation problems and workflow problems. Thus, SRE does most things through software engineers whereas DevOps uses a development and operation team to finish the work from building to deploying the software in the market.


SRE doesn’t have any special script to follow, but it offers a hard prescription to solve the problems and which tools to use. Whereas DevOps has a development lifecycle that describes what to do.

All these courses are being done at one of the best platforms which are DevOpsschool. If anyone is looking for an institute where you can learn DevOps, you should go for this.

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SDLC (Software Development Life Cycle) Phases, Process, Models – Complete guide

Introduction

Software Development Life Cycle (SDLC) may be a method employed by the software industry to style, develop and check high-quality software. The SDLC aims to supply high-quality software that meets or exceeds client expectations, reaches completion among times and price estimates.

SDLC is that the acronym of Software Development Life Cycle.

It is conjointly referred to as the software development method.

SDLC may be a framework process task performed at every step within the software development method.

ISO/IEC 12207 is a world quality software life-cycle process. It aims to be the quality that defines all the tasks needed for developing and maintaining software.

What is Software Development Lifecycle (SDLC)?

The software Development Life Cycle (SDLC) could be a structured method that permits the assembly of high-quality, low-priced software, within the shortest attainable production time. The goal of the SDLC is to provide superior software that meets and exceeds all client expectations and demands.

SDLC may be a method followed for a software project, inside a software organization. It consists of an in-depth setup describing the way to develop, maintain, replace and alter or enhance specific software. The life cycle defines a strategy for improving the standard of software and also the overall development method.

Why SDLC is important for developing a software system?

SDLC permits developers to research the necessities. It helps in reducing unnecessary prices throughout development. It allows developers to style and builds high-quality software products. This can be as a result of them following a scientific method that permits them to check the software before it’s extended.

  • Forms the foundation for project planning and scheduling
  • Helps estimate cost and time
  • Includes the project activities and deliverables of each phase
  • Boosts the transparency of the entire project and the development process
  • Enhance the speed and accuracy of development
  • Minimizes the risk potential and maintenance during any given project
  • Its defined standard improves client relations

What are the Benefits of the Software Development Lifecycle?

  • It makes it clear what the problem or goal is. It is easy to get ahead of yourself when taking on a large project. With the SDLC you can clearly see the goals and the problems so that the plan is implemented with precision and relevance.
  • The project is designed with clarity. Project members cannot move from one stage to another until the prior stage is completed and signed off on by the project manager. A formal review is created at the end of each stage, which allows the project manager to have maximum management control.
  • It will be properly tested before being installed. The installation in a project that is executed using an SDLC has the necessary checks and balances so that it will be tested with precision before entering the installation stage.
  • If a key project member leaves, a new member can pick up where they left off. The SDLC gives you a well-structured and well-documented paper trail of the entire project that is complete with records of everything that occurs.
  • Without the SDLC, the loss of a project member will set you back and probably ruin the project. If paperwork is missing or incomplete, the new project member can have to be compelled to begin from the start and even probably amendment the project to create sense of it. With a well-designed SDLC, everything is so as in order that a replacement project member will continue the method while not complications.
  • The project manager will properly manage a project if deliverables are completed on time and among the budget. sticking to a budget is simpler with a well-organized arrange during which you’ll see all the timetables and prices. Project members will submit their work to an integrated system that flags something that’s past due. Once the project manager will pay less time micromanaging, he or she will be able to pay longer improving potency and production.
  • The project can continuously loop around until it is perfect. The stages are meant to feed back into the earlier stages, so the SDLC model provides the project with flexibility.
  • When designing and implementing a project, a software development life cycle is the solution. It’s the best way to ensure optimal control, minimize problems, and allow the project manager to run production without having to micromanage the project members.

Stages of the SDLC:

Every software development company goes through an array of stages as they embark on a systematic process of development. From planning to design and development, here is a brief glance at the six essential stages of SDLC required to create flawless software:

Planning

Without a clear, visionary plan in place, it is difficult to align everything with your project goals and judge all its strengths, scope, and challenges involved.

The planning is to ensure the development goes easy and smooth, meets its purpose, and achieves its desired progress within the given time limit.

Analysis

Analyzing the requirements and performance of the software through its multiple stages is key to deriving process efficiency.

Analysis always helps be in the know of where you exactly stand in the process and where you need to be and what it takes to pass through the next step down the path.

Design

After the analytical part is complete, the design is the next step that needs to look forward to. The basic aim in this phase is to create a strong, viable architecture of the software process.

As it works by standard adherence, it helps eliminate any flaws or errors that may possibly hinder the operation.

Development

Once the design is ready, the development takes over along with efficient data management and recording. This is a complicated phase where clarity and focus are of great significance.

Post-development, implementation comes into the picture to check whether or not the product functions as expected.

Testing

The testing phase that comes now is inevitable as it studies and examines the software for any errors and bugs that may cause trouble.

Maintenance

If the software has performed well through all the previous five steps, it comes to this final stage called maintenance. The product here is properly maintained and upgraded as and when needed to make it more adaptive to the target market.

How many SDLC models are there?

Today, there are more than 50 recognized SDLC models in use. None of them is perfect, and each brings its favorable aspects and disadvantages for a specific software development project or a team.

Waterfall

Through all development stages (analysis, design, coding, testing, deployment), the method moves in a very cascade model. Every stage has concrete deliverables and is strictly documented. The consecutive stage cannot begin before the previous one is totally completed. Thus, as an example, software needs cannot be re-evaluated any in development. There’s additionally no ability to check and take a look at the software until the last development stage is finished, which ends up in high project risks and unpredictable project results. Testing is usually rushed, and errors are expensive to repair.

SDLC Waterfall model is used when:

  • Requirements are stable and not changed frequently.
  • An application is small.
  • There is no requirement which is not understood or not very clear.
  • The environment is stable
  • The tools and techniques used is stable and is not dynamic
  • Resources are well trained and are available.

V-model (Validation and Verification model)

The V-model is another linear model with every stage having a corresponding testing activity. Such workflow organization implies exceptional internal control, however, at constant time, it makes the V-model one among the foremost costly and long models. Moreover, although mistakes in needs specifications, code, and design errors will be detected early, changes throughout development are still costly and tough to implement. As within the waterfall case, all needs are gathered at the beginning and can’t be modified.

V model is applicable when:

  • The requirement is well defined and not ambiguous
  • Acceptance criteria are well defined.
  • Project is short to medium in size.
  • Technology and tools used are not dynamic.

Incremental and Iterative model

Incremental: An incremental approach breaks the software development process down into small, manageable portions known as increments.

 Iterative: An iterative model means software development activities are systematically repeated in cycles known as iterations.

Use cases: Large, mission-critical enterprise applications that preferably consist of loosely coupled parts, such as microservices or web services.

Spiral model

The Spiral model puts concentrates on thorough risk assessment. Thus, to reap the advantages of the model to the fullest, you’ll have to be compelled to have interaction with people with a powerful background in risk evaluation. A typical Spiral iteration lasts around six months and starts with four important activities – thorough designing, risk analysis, prototypes creation, and evaluation of the antecedently delivered part. Continual spiral cycles seriously extend project timeframes.

Uses of the spiral model:

  • projects in which frequent releases are necessary;
  • projects in which changes may be required at any time;
  • long term projects that are not feasible due to altered economic priorities;
  • medium to high risk projects;
  • projects in which cost and risk analysis is important;
  • projects that would benefit from the creation of a prototype; and
  • projects with unclear or complex requirements.

The Rational Unified Process (RUP)

The Rational Unified Process (RUP) is also a mixture of linear and reiterative frameworks. The model divides the software development process into four phases – inception, elaboration, construction, and transition. Every phase however inception is typically done in many iterations. All basic activities (requirements, design, etc) of the development process are done in parallel across these four RUP phases, although with completely different intensities.

RUP helps to make stable and, at a similar time, versatile solutions, but still, this model isn’t as fast and adaptable because of the pure Agile cluster (Scrum, Kanban, XP, etc.). The degree of client involvement, documentation intensity, and iteration length could vary betting on the project wants.

Use cases: Large and high-risk projects, especially, use-case-based development and fast development of high-quality software.

Scrum

Scrum is probably the most popular Agile model. The iterations (‘sprints’) are usually 2-4 weeks long and they are preceded with thorough planning and previous sprint assessment. No changes are allowed after the sprint activities have been defined.

Extreme Programming (XP)

With Extreme Programming (XP), a typical iteration lasts 1-2 weeks. The model permits changes to be introduced even once the iteration’s launch if the team hasn’t begun to work with the relevant software piece yet. Such flexibility considerably complicates the delivery of quality software. To mitigate the matter, XP needs the utilization of try programming, test-driven development and test automation, continuous integration (CI), little releases, easy software style and prescribes to follow the coding standards.

Kanban

As for Kanban, its key distinguishing feature is that the absence of pronounced iterations. If used, they’re unbroken very short (‘daily sprints’). Instead, the emphasis is placed on arranged visualization. The team uses the Kanban Board tool that has a transparent illustration of all project activities, their variety, responsible persons, and progress. Such increased transparency helps to estimate the foremost urgent tasks a lot accurately. Also, the model has no separate strategy planning stage, thus a new modification request will be introduced at any time. Communication with the client is in progress, they’ll check the work results whenever they like, and therefore the meetings with the project team will happen even daily because of its nature, the model is usually employed in projects on software support and evolution.

What are the different phases of the SDLC life cycle?

I have explained all these Software Development Life Cycle Phases

  1. Requirement collection and analysis

The requirement is the first stage in the SDLC process. It is conducted by the senior team members with inputs from all the stakeholders and domain experts in the industry. Planning for the quality assurance requirements and recognization of the risks involved is also done at this stage.

2. Feasibility study

Once the requirement analysis phase is completed the next SDLC step is to define and document software needs. This process was conducted with the help of the ‘Software Requirement Specification’ document also known as the ‘SRS’ document. It includes everything which should be designed and developed during the project life cycle.

3. Design

In this third phase, the system and software design documents are prepared as per the requirement specification document. This helps define the overall system architecture.

This design phase serves as input for the next phase of the model.

There are two kinds of design documents developed in this phase:

High-Level Design (HLD)

  • Brief description and name of each module
  • An outline about the functionality of every module
  • Interface relationship and dependencies between modules
  • Database tables identified along with their key elements
  • Complete architecture diagrams along with technology details

Low-Level Design(LLD)

  • Functional logic of the modules
  • Database tables, which include type and size
  • Complete detail of the interface
  • Addresses all types of dependency issues
  • Listing of error messages
  • Complete input and outputs for every module

4. Coding

Once the system design phase is over, the next phase is coding. In this phase, developers start to build the entire system by writing code using the chosen programming language. In the coding phase, tasks are divided into units or modules and assigned to the various developers. It is the longest phase of the Software Development Life Cycle process.

In this phase, the developer needs to follow certain predefined coding guidelines. They also need to use programming tools like compilers, interpreters, debuggers to generate and implement the code.

5. Testing

Once the software is complete, and it is deployed in the testing environment. The testing team starts testing the functionality of the entire system. This is done to verify that the entire application works according to the customer’s requirements.

During this phase, QA and testing team may find some bugs/defects which they communicate to developers. The development team fixes the bug and sends it back to QA for a re-test. This process continues until the software is bug-free, stable, and working according to the business needs of that system.

6.Installation/Deployment

Once the software testing phase is over and no bugs or errors are left in the system then the final deployment process starts. Based on the feedback given by the project manager, the final software is released and checked for deployment issues if any.

7. Maintenance

Once the system is deployed, and customers start using the developed system, the following 3 activities occur

Bug fixing – bugs are reported because of some scenarios which are not tested at all

Upgrade – Upgrading the application to the newer versions of the Software.

Enhancement – Adding some new features into the existing software.

Which SDLC Model is Best?

Agile is that the best SDLC methodology and conjointly one in every of the foremost used SDLC within the tech trade as per the annual State of Agile report. At RnF Technologies, Agile is that the most loved software development life cycle model. Here’s why. Agile is very adaptive that making it totally different from all alternative SDLC.

Conclusion
The software development life cycle is a resourceful tool for developing high-quality software products. This tool provides a framework for guiding developers in the process of software development. Organizations can use various SDLC strategies such as waterfall, V-model, iterative, spiral, and agile models.
You should consider consulting with a resourceful IT company before embracing an SDLC approach for your team from the list above.
DevOpsSchool has enough expertise to help you know how different models come in handy in certain business scenarios and industry environments. From our experience, we will guide you to the best fit for your software product.

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