Top 10 Decision Management Systems

Decision Management Systems (DMS) are software platforms or frameworks that facilitate the management, automation, and optimization of business decisions. These systems typically incorporate business rules management, analytics, and decision modeling capabilities to enable organizations to make informed and consistent decisions. DMS can be used across various industries and business functions, including finance, healthcare, customer service, supply chain management, and more.

Here are 10 popular Decision Management Systems (DMS):

  1. IBM Operational Decision Manager
  2. FICO Decision Management Suite
  3. SAS Decision Manager
  4. Oracle Business Rules
  5. Pega Decision Management
  6. TIBCO BusinessEvents
  7. Red Hat Decision Manager
  8. SAP Decision Service Management
  9. OpenRules
  10. Drools

1. IBM Operational Decision Manager:

IBM’s DMS provides a comprehensive platform for modeling, automating, and optimizing business decisions. It combines business rules management, predictive analytics, and optimization techniques.

Key features:

  • Business Rules Management: IBM ODM offers a powerful business rules management system (BRMS) that allows organizations to define, manage, and govern business rules. It provides a user-friendly interface for business analysts to author and update rules without the need for coding.
  • Decision Modeling: ODM includes decision modeling capabilities that enable organizations to model and visualize their decision logic using decision tables, decision trees, and decision flowcharts. This makes it easier to understand and communicate complex decision-making processes.
  • Decision Validation and Testing: ODM provides tools for validating and testing decision models and business rules. Users can simulate different scenarios, analyze rule conflicts or inconsistencies, and verify the accuracy and completeness of their decision logic.

2. FICO Decision Management Suite:

FICO’s DMS offers a suite of tools for decision modeling, optimization, and rules management. It enables organizations to automate and improve decision-making processes using advanced analytics.

Key features:

  • Decision Modeling and Strategy Design: The suite provides a visual decision modeling environment that allows business analysts and domain experts to define and document decision logic using decision tables, decision trees, and decision flows. It enables the creation of reusable decision models and strategies.
  • Business Rules Management: FICO Decision Management Suite includes a powerful business rules engine that allows organizations to define, manage, and execute complex business rules. It provides a user-friendly interface for managing rule sets, rule versioning, and rule governance.
  • Analytics Integration: The suite integrates with advanced analytics capabilities, including predictive modeling, machine learning, and optimization techniques. This enables organizations to leverage data-driven insights to enhance decision-making and optimize outcomes.

3. SAS Decision Manager:

SAS Decision Manager is a comprehensive platform that allows organizations to model, automate, and monitor decision processes. It provides a visual interface for creating and deploying rules and decision flows.

Key features:

  • Decision Modeling: SAS Decision Manager allows users to model and visualize decision logic using graphical interfaces and decision tables. It provides a user-friendly environment for business analysts and domain experts to define decision rules and dependencies.
  • Business Rules Management: The platform offers a powerful business rules management system (BRMS) that enables organizations to define, manage, and govern business rules. It supports the creation and management of rule sets, rule libraries, and rule versioning.
  • Decision Automation: SAS Decision Manager enables the automation of decision processes. It allows for the execution of decision logic within operational systems and workflows, reducing manual effort and ensuring consistent and timely decision-making.

4. Oracle Business Rules:

Oracle Business Rules provides a platform for modeling, automating, and managing business rules. It integrates with other Oracle products and offers a range of features for decision management.

Key features:

  • Rule Authoring and Management: Oracle Business Rules offers a user-friendly interface for defining, authoring, and managing business rules. It provides a graphical rule editor that allows business users and subject matter experts to define rules using a visual representation.
  • Decision Modeling: The platform supports decision modeling using decision tables, decision trees, and other visual representations. It enables users to define decision logic and dependencies in a structured and intuitive manner.
  • Rule Repository and Versioning: Oracle Business Rules includes a rule repository that allows for the storage, organization, and versioning of rules. It provides a centralized location to manage and govern rules, ensuring consistency and traceability.

5. Pega Decision Management:

Pega Decision Management is part of Pega’s unified platform for business process management and customer engagement. It provides tools for designing, executing, and optimizing business decisions.

Key features:

  • Decision Modeling: Pega Decision Management allows users to model and visualize decision logic using decision tables, decision trees, and other visual representations. It provides a user-friendly interface for business users and domain experts to define and manage decision rules.
  • Business Rules Management: The platform includes a powerful business rules engine that enables organizations to define, manage, and govern business rules. It supports the creation and management of rule sets, rule libraries, and rule versioning.
  • Decision Strategy Design: Pega Decision Management provides tools for designing decision strategies. It allows users to define and orchestrate a series of decisions, actions, and treatments to optimize customer interactions and outcomes.

6. TIBCO BusinessEvents:

TIBCO BusinessEvents is a complex event processing platform that enables organizations to make real-time decisions based on streaming data and business rules. It offers high-performance event processing and decision automation capabilities.

Key features:

  • Event Processing: TIBCO BusinessEvents provides powerful event processing capabilities that allow organizations to detect, analyze, and correlate events in real-time. It can handle high volumes of events from multiple sources and process them with low latency.
  • Complex Event Processing (CEP): The platform supports complex event processing, which involves analyzing and correlating multiple events to identify patterns, trends, and anomalies. It enables organizations to gain insights from event data and take appropriate actions in real-time.
  • Business Rules and Decision Management: TIBCO BusinessEvents incorporates a business rules engine that allows organizations to define, manage, and execute business rules. It enables the automation of decision-making processes based on real-time event data.

7. Red Hat Decision Manager:

Red Hat Decision Manager is an open-source decision management platform that combines business rules management, complex event processing, and predictive analytics. It provides tools for building and managing decision services.

Key features:

  • Business Rules Management: Red Hat Decision Manager offers a powerful business rules engine that allows organizations to define, manage, and execute business rules. It provides a user-friendly interface for business users and domain experts to author and maintain rules.
  • Decision Modeling: The platform supports decision modeling using decision tables, decision trees, and other visual representations. It allows users to model and visualize decision logic in a structured and intuitive manner.
  • Decision Services and Execution: Red Hat Decision Manager enables the deployment of decision services as reusable components that can be integrated into operational systems and workflows. It supports real-time or near-real-time decision execution within existing applications.

8. SAP Decision Service Management:

SAP Decision Service Management is a component of SAP’s business process management suite. It allows organizations to model, execute, and monitor decision services based on business rules.

Key features:

  • Business Rules Engine: SAP decision management solutions typically include a business rules engine that allows organizations to define and manage their business rules. This engine enables the execution of rules in real time or as part of automated processes.
  • Decision Modeling and Visualization: These solutions often provide tools for decision modeling and visualization, allowing business users and analysts to design decision logic using graphical interfaces, decision tables, or other visual representations.
  • Decision Automation: SAP decision management solutions support the automation of decision-making processes. This involves integrating decision services into operational systems and workflows, enabling consistent and automated decision execution.

9. OpenRules:

OpenRules is an open-source decision management platform that focuses on business rules management. It provides a lightweight and flexible solution for modeling and executing business rules.

Key features:

  • Rule Authoring and Management: OpenRules offers a user-friendly and intuitive rule authoring environment. It provides a spreadsheet-based interface, allowing business users and subject matter experts to define and maintain rules using familiar spreadsheet tools such as Microsoft Excel or Google Sheets.
  • Rule Execution Engine: OpenRules includes a powerful rule execution engine that evaluates and executes business rules. It supports both forward and backward chaining rule execution, allowing complex rule dependencies and reasoning to be handled effectively.
  • Decision Modeling and Visualization: The platform supports decision modeling using decision tables, decision trees, and other visual representations. It enables users to model and visualize decision logic in a structured and easy-to-understand manner.

10. Drools:

Drools is an open-source business rules management system that enables organizations to model, validate, and execute business rules. It offers a rich set of features and integrates well with other systems.

Key features:

  • Rule Authoring and Management: Drools offers a rich set of tools and editors for authoring and managing business rules. It provides a domain-specific language (DSL) and a graphical rule editor, allowing both business users and developers to define and maintain rules effectively.
  • Rule Execution Engine: Drools includes a highly efficient and scalable rule execution engine. It supports forward chaining, backward chaining, and hybrid rule execution strategies, allowing complex rule dependencies and reasoning to be handled efficiently.
  • Decision Modeling and Visualization: The platform supports decision modeling using decision tables, decision trees, and other visual representations. It allows users to model and visualize decision logic in a structured and intuitive manner.
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Top 10 Cloud Computing Platforms

What is Cloud Computing?

Cloud computing stores and accesses data and programs over the internet instead of hard drives, physical servers, or personal computers. In its simplest terms, cloud computing uses a network of remote servers to store, manage, and process data instead of relying on local storage devices like hard drives. A cloud is essentially a group of servers that are accessible online to store and share information.

Cloud computing is used by individuals and businesses alike to store their data remotely and access it from any computer or device with an internet connection. For example, with cloud computing, you can send files back and forth while working with colleagues, access your photos on your phone or computer, or even use programs like Google Docs or Microsoft Word. Using the cloud means that the servers you’re using are not located in the exact physical location as you are; they’re accessible via the internet, making them more accessible and secure. Also, you can store your data and backup essential files in case of a disaster.

Cloud Computing Platform

The operating system and hardware of a server in an Internet-based data center are referred to as a cloud platform. It enables remote and large-scale coexistence of software and hardware devices. The distribution of various services through the Internet is what a cloud computing platform is. This is a common definition of a cloud computing platform. These resources include data storage, servers, databases, networking, and software, among other tools and applications. 

Cloud-based storage allows you to store files in a distant database rather than maintaining them on a proprietary hard drive or local storage device. As long as an electronic device has internet connectivity, it has access to the data as well as the software applications needed to run it. People and businesses are increasingly turning to cloud computing platforms for a variety of reasons, including cost savings, enhanced productivity, speed and efficiency, performance, and security.

Some popular cloud computing platforms include:

  1. Amazon Web Services (AWS)
  2. Microsoft Azure
  3. Google Cloud Platform (GCP)
  4. Alibaba Cloud
  5. IBM Cloud
  6. Oracle Cloud
  7. Salesforce
  8. VMware Cloud
  9. Tencent Cloud
  10. DigitalOcean

1. Amazon Web Services (AWS):

AWS is a comprehensive cloud platform offered by Amazon. It provides a wide range of services, including computing power, storage, databases, networking, machine learning, and analytics.

Key features:

  • Compute Services: AWS provides a range of computing services, including Amazon Elastic Compute Cloud (EC2) for scalable virtual servers, AWS Lambda for serverless computing, and AWS Batch for batch computing workloads.
  • Storage Services: AWS offers multiple storage services, such as Amazon Simple Storage Service (S3) for object storage, Amazon Elastic Block Store (EBS) for block-level storage volumes, and Amazon Glacier for long-term data archival.
  • Database Services: AWS provides a variety of database services, including Amazon Relational Database Service (RDS) for managed relational databases, Amazon DynamoDB for NoSQL databases, and Amazon Redshift for data warehousing.

2. Microsoft Azure:

Azure is Microsoft’s cloud platform that offers a broad set of services for building, deploying, and managing applications and services. It includes capabilities for virtual machines, storage, databases, AI, analytics, and more.

Key features:

  • Virtual Machines: Azure provides virtual machines (VMs) that offer scalable computing power, allowing users to run a wide range of operating systems and applications in the cloud.
  • Azure App Service: This feature allows users to build, deploy, and scale web and mobile applications easily. It supports multiple programming languages and frameworks.
  • Azure Functions: Azure Functions enables serverless computing, allowing users to run code without managing infrastructure. It automatically scales based on demand and charges only for the actual execution time.

3. Google Cloud Platform (GCP):

GCP is Google’s cloud computing platform that offers a suite of cloud services, including computing, storage, databases, machine learning, and data analytics. It also provides tools for big data processing and IoT applications.

Key features:

  • Compute Engine: Compute Engine provides virtual machines (VMs) with flexible configurations and high-performance computing options. Users can choose from predefined machine types or create custom machine types.
  • App Engine: App Engine is a fully managed platform that allows developers to build and deploy scalable web applications and APIs. It automatically scales applications based on demand and handles infrastructure management.
  • Kubernetes Engine: Kubernetes Engine is a managed container orchestration service based on Kubernetes. It simplifies the deployment, management, and scaling of containerized applications.

4. IBM Cloud:

IBM Cloud is an enterprise-grade cloud platform that offers a range of services, including compute, storage, AI, blockchain, and IoT. It focuses on hybrid cloud deployments, allowing businesses to integrate their existing infrastructure with cloud resources.

Key features:

  • Virtual Servers: IBM Cloud provides virtual server instances known as IBM Virtual Servers, offering flexible configurations and a wide range of compute options.
  • Kubernetes Service: IBM Kubernetes Service is a managed container orchestration platform based on Kubernetes. It simplifies the deployment, management, and scaling of containerized applications.
  • Cloud Object Storage: IBM Cloud Object Storage offers scalable and durable object storage for storing and retrieving unstructured data. It provides flexible storage tiers and global data availability.

5. Oracle Cloud:

Oracle Cloud provides a set of cloud services, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). It offers solutions for database management, application development, analytics, and more.

Key features:

  • Compute: Oracle Compute provides virtual machine instances with customizable configurations and options for both Intel and AMD processors. It offers high-performance computing capabilities.
  • Autonomous Database: Oracle Autonomous Database is a fully managed and self-driving database service. It uses AI and machine learning to automate database management tasks, such as patching, tuning, and backups.
  • Object Storage: Oracle Object Storage provides scalable and durable object storage for storing and retrieving unstructured data. It offers high durability and data protection capabilities.

6. Alibaba Cloud:

Alibaba Cloud is the cloud computing arm of Alibaba Group, one of the largest e-commerce companies. It offers a wide range of services, including computing, storage, networking, security, and big data processing. It is prevalent in China and Asia.

Key features:

  • Elastic Compute Service (ECS): Alibaba ECS offers scalable virtual server instances with flexible configurations. It provides a wide range of instance types and allows users to customize CPU, memory, storage, and networking resources.
  • Object Storage Service (OSS): Alibaba OSS provides scalable and secure object storage for storing and retrieving large amounts of unstructured data. It offers high durability, availability, and low latency.
  • ApsaraDB for RDS: ApsaraDB for RDS is a fully managed relational database service that supports various database engines, including MySQL, SQL Server, PostgreSQL, and Oracle. It offers automated backups, high availability, and scalability.

7. Salesforce:

While Salesforce is primarily known for its customer relationship management (CRM) software, it also offers a cloud computing platform known as Salesforce Platform. It allows users to build and deploy custom applications using Salesforce’s infrastructure and services.

Key features:

  • Contact and Account Management: Salesforce provides a centralized database to store and manage customer contact information, accounts, and related details. It allows businesses to track each customer’s interactions, activities, and history.
  • Sales Opportunity Management: Salesforce offers tools for managing sales opportunities, including tracking leads, managing pipelines, and forecasting sales revenue. It enables sales teams to collaborate, prioritize leads, and close deals more effectively.
  • Sales Performance and Analytics: Salesforce provides dashboards and reports to analyze sales performance, track key metrics, and gain insights into the effectiveness of sales efforts. It helps identify trends, forecast revenue, and make data-driven decisions.

8. Huawei Cloud:

Huawei Cloud is a global cloud service provider with a growing presence, offering a wide range of services across various industries.

Key features:

  • Elastic Compute Service (ECS): Huawei ECS offers scalable virtual servers with customizable configurations. It provides a wide range of instance types and allows users to easily adjust resources according to their needs.
  • Object Storage Service (OBS): Huawei OBS provides highly available and durable object storage for storing and retrieving unstructured data. It supports multiple storage classes and offers flexible data management options.
  • Database Services: Huawei Cloud offers various database services, including Relational Database Service (RDS) for MySQL, PostgreSQL, and SQL Server databases, as well as Distributed Relational Database Service (DRDS) for distributed database management.

9. VMware Cloud:

VMware Cloud is a hybrid cloud platform that enables organizations to seamlessly run, manage, and secure applications across multiple clouds and on-premises environments.

Key features:

  • VMware Cloud Foundation: VMware Cloud Foundation is an integrated platform that combines compute, storage, networking, and management services into a unified infrastructure stack. It provides a consistent operational experience across private and public clouds.
  • VMware vSphere: VMware vSphere is a virtualization platform that enables the creation and management of virtual machines. It provides high-performance compute resources, scalability, and workload mobility across on-premises and cloud environments.
  • VMware vSAN: VMware vSAN is a software-defined storage solution that is tightly integrated with vSphere. It aggregates local storage devices and provides distributed shared storage for virtual machines. It offers features like data deduplication, compression, and encryption.

10. Tencent Cloud:

Tencent Cloud is one of the leading cloud providers in China, offering a comprehensive suite of cloud services for businesses and developers.

Key features:

  • Elastic Compute Service (ECS): Tencent ECS offers scalable virtual server instances with customizable configurations. It provides a wide range of instance types and allows users to easily adjust resources according to their needs.
  • Object Storage Service (COS): Tencent COS provides highly available and durable object storage for storing and retrieving unstructured data. It offers features like automatic tiering, data archiving, and data migration.
  • Database Services: Tencent Cloud offers various database services, including TencentDB for MySQL, PostgreSQL, and MariaDB, as well as distributed databases like TDSQL. It provides options for high availability, scalability, and data management.
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Top 10 Data Cleaning Tools

What are Data Cleaning Tools

Data cleaning tools, also known as data cleansing tools or data preprocessing tools, are software applications or platforms designed to assist in the process of cleaning and preparing data for analysis. These tools automate and streamline data cleaning tasks, helping to improve data quality, consistency, and accuracy.

Data cleaning, also known as data cleansing or data preprocessing, is an essential step in data analysis to ensure data quality and reliability. There are several tools available that can help with data-cleaning tasks.

Here are some popular data-cleaning tools:

  • OpenRefine
  • Data Standardization
  • Handling Missing Values
  • Removing Duplicates
  • Outlier Detection
  • RapidMiner
  • Talend Open Studio
  • Microsoft Excel
  • Python Libraries
  • R Programming

1. OpenRefine:

OpenRefine (formerly Google Refine) is a free and open-source tool that allows users to explore, clean, and transform messy data. It provides features for data standardization, removing duplicates, handling missing values, and performing text and numeric transformations.

Key features:

  • Free and open source
  • Supports over 15 languages
  • Work with dta on your machine
  • Parse data from the internet

2. Trifacta Wrangler:

Trifacta Wrangler is a data preparation tool that offers a user-friendly interface for cleaning and transforming data. It provides visual tools for data profiling, data quality assessment, and data wrangling tasks, making it easy to identify and fix data issues.

Key features:

  • Less formatting time
  • Focus on data analysis
  • Quick and accurate
  • Machine learning algorithm suggestions

3. Dataiku DSS:

Dataiku DSS is a comprehensive data science platform that includes data cleaning capabilities. It provides visual tools for data exploration, data cleaning, and data transformation. Users can define data cleaning rules, handle missing values, and apply transformations to ensure data quality.

Key features:

  • Data Integration: Dataiku DSS offers a visual and interactive interface for connecting and integrating data from various sources, including databases, file systems, cloud storage, and streaming platforms. It supports data ingestion, transformation, and data pipeline creation.
  • Data Preparation and Cleaning: Dataiku DSS provides tools for data cleaning, data wrangling, and data preprocessing. It allows users to handle missing values, perform data transformations, apply filters, and perform feature engineering tasks.
  • Visual Data Flow: Dataiku DSS offers a visual data flow interface, where users can design and build data transformation workflows using a drag-and-drop approach. This visual interface allows for easy data manipulation and simplifies the creation of data pipelines.

4. Talend Data Preparation:

Talend Data Preparation is a data cleaning tool that offers a user-friendly interface for data profiling, data cleansing, and data enrichment. It provides features for handling missing values, removing duplicates, and standardizing data formats.

Key features:

  • Data Profiling: Talend Data Preparation provides data profiling capabilities to analyze the structure, quality, and content of datasets. It automatically generates statistical summaries, data quality assessments, and data distributions to help users understand their data.
  • Visual Data Exploration: The tool offers a visual interface that allows users to explore and interact with their data. It provides visualizations, such as histograms, charts, and scatter plots, to gain insights into the data distribution, patterns, and potential data quality issues.
  • Data Cleansing and Standardization: Talend Data Preparation includes features for data cleaning and standardization. It provides functions for handling missing values, removing duplicates, correcting inconsistent or erroneous data, and standardizing formats and values across the dataset.

5. IBM InfoSphere QualityStage:

IBM InfoSphere QualityStage is a data quality tool that includes features for data cleaning and data profiling. It provides a comprehensive set of data cleansing rules, such as data validation, standardization, and correction, to improve the quality of the data.

Key features:

  • Data Profiling: IBM InfoSphere QualityStage offers data profiling capabilities to analyze the structure, content, and quality of datasets. It provides statistics, summaries, and data quality metrics to understand the characteristics and issues within the data.
  • Data Cleansing and Standardization: The tool includes robust data cleansing and standardization features. It allows users to cleanse and correct data by identifying and resolving data quality issues such as misspellings, inconsistencies, and incorrect formats. It also provides functions for standardizing data values, transforming addresses, and normalizing data across the dataset.

6. RapidMiner:

RapidMiner is a data science platform that offers data cleaning and preprocessing capabilities. It provides visual tools for data transformation, missing value imputation, outlier detection, and handling inconsistent data formats.

Key features:

  • Data Preparation: RapidMiner provides powerful tools for data cleaning, transformation, and integration. It allows you to import data from various sources, handle missing values, filter and aggregate data, and perform data formatting tasks.
  • Data Exploration and Visualization: RapidMiner enables you to explore your data visually through interactive charts, histograms, scatter plots, and other visualization techniques. This feature helps you gain insights into your data and identify patterns or trends.
  • Machine Learning: RapidMiner supports a vast array of machine learning algorithms and techniques. It provides a drag-and-drop interface for building predictive models, classification, regression, clustering, and association rule mining. It also offers automated model selection and optimization capabilities.

7. Talend Open Studio:

Talend Open Studio is an open-source data integration tool that includes data cleaning and data transformation features. It provides a graphical interface for designing data cleaning workflows and offers a wide range of data transformation functions.

Key features:

  • Data Integration: Talend Open Studio offers a graphical interface for designing data integration workflows. It allows you to extract data from various sources such as databases, files, and APIs, transform the data using a wide range of transformations and functions, and load the data into target systems.
  • Connectivity and Integration: Talend Open Studio provides a vast library of connectors and components to connect to different data sources and systems. It supports integration with databases, cloud services, enterprise applications, web services, and more.
  • Data Quality: Talend Open Studio includes built-in data quality tools to ensure the accuracy, completeness, consistency, and integrity of your data. It offers features like data profiling, data cleansing, deduplication, standardization, and validation.

8. Microsoft Excel:

Although not specifically designed for data cleaning, Microsoft Excel can be used for basic data cleaning tasks. It provides functions for removing duplicates, handling missing values, text manipulation, and basic data transformations.

Key features:

  • Spreadsheet Creation and Formatting: Excel allows you to create spreadsheets and organize data into rows and columns. You can format cells, apply styles, adjust column widths, and customize the appearance of your data.
  • Formulas and Functions: Excel provides a vast library of built-in formulas and functions that enable you to perform various calculations and operations on your data. Functions range from simple arithmetic calculations to complex statistical and financial calculations.
  • Data Analysis and Modeling: Excel includes features for data analysis, such as sorting, filtering, and pivot tables. It allows you to summarize and analyze large datasets, perform what-if analysis, and build data models using tools like Power Pivot and Power Query.

9. Python Libraries:

Python offers several powerful libraries for data cleaning, including pandas, numpy, and scikit-learn. These libraries provide functions and methods for handling missing values, data imputation, outlier detection, and data transformation.

Key features:

  • NumPy: NumPy is a fundamental library for scientific computing in Python. It provides support for efficient numerical operations on large multi-dimensional arrays and matrices. NumPy offers a wide range of mathematical functions, linear algebra operations, and random number generation.
  • Pandas: Pandas is a powerful library for data manipulation and analysis. It offers data structures such as DataFrames for organizing and analyzing structured data. Pandas provides tools for data cleaning, filtering, grouping, merging, and reshaping. It also supports data I/O operations and integrates well with other libraries.
  • Matplotlib: Matplotlib is a versatile library for creating visualizations and plots. It provides a wide range of plot types, including line plots, bar charts, histograms, scatter plots, and more. Matplotlib allows customization of plots, labeling, and adding annotations. It can be used interactively or in scripts.

10. R Programming:

R, a popular programming language for data analysis, also provides various packages and functions for data cleaning. Packages like dplyr, tidyr, and stringr offer tools for data manipulation, handling missing values, and data transformation.

Key features:

  • Data Manipulation and Analysis: R provides extensive tools for data manipulation and analysis. It offers data structures such as vectors, matrices, data frames, and lists to handle and process data efficiently. R supports a variety of data operations, including filtering, sorting, merging, reshaping, and aggregation.
  • Statistical Modeling and Analysis: R has a rich set of built-in statistical functions and libraries for conducting various statistical analyses. It includes functions for descriptive statistics, hypothesis testing, regression analysis, ANOVA (analysis of variance), time series analysis, and more. R is widely used in academic research and data-driven industries for statistical modeling.
  • Data Visualization: R offers powerful data visualization capabilities through libraries such as ggplot2 and lattice. These libraries allow you to create a wide variety of high-quality graphs and plots, including scatter plots, bar charts, line charts, histograms, heatmaps, and interactive visualizations. R’s visualization capabilities make it easy to explore and communicate data insights effectively.
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Top 10 Data Mining Tools

Data mining tools are software applications or platforms designed to discover patterns, relationships, and insights from large datasets. These tools employ various techniques from statistics, machine learning, and database systems to extract useful information from complex data.

Here are some popular data mining tools:

  1. RapidMiner
  2. Weka
  3. KNIME
  4. Orange
  5. IBM SPSS Modeler
  6. SAS Enterprise Miner
  7. Microsoft SQL Server Analysis Services
  8. Oracle Data Mining
  9. Apache Mahout

1. RapidMiner:

Incorporating Python and/or R in your data mining arsenal is a great goal in the long term. In the immediate term, however, you might want to explore some proprietary data mining tools. One of the most popular of these is the data science platform RapidMiner. RapidMiner unifies everything from data access to preparation, clustering, predictive modeling, and more. Its process-focused design and inbuilt machine learning algorithms make it an ideal data mining tool for those without extensive technical skills, but who nevertheless require the ability to carry out complicated tasks. The drag-and-drop interface reduces the learning curve that you’d face using Python or R, and you’ll find online courses aimed specifically at how to use the software.

Key features:

  • Predictive Modeling (a technique for predicting the future.)
  • Recognize the Present, revisit, and analyze the past.
  • Provides RIO ( Rapid Insight online) webpage for users to share reports and visualizations among teams.

2. Weka:

Weka is an open-source machine learning software with a vast collection of algorithms for data mining. It was developed by the University of Waikato, in New Zealand, and it’s written in JavaScript. It supports different data mining tasks, like preprocessing, classification, regression, clustering, and visualization, in a graphical interface that makes it easy to use. For each of these tasks, Weka provides built-in machine-learning algorithms which allow you to quickly test your ideas and deploy models without writing any code. To take full advantage of this, you need to have a sound knowledge of the different algorithms available so you can choose the right one for your particular use case.

Key Features:

  • If you have a good knowledge of algorithms, Weka can provide you with the best options based on your needs.
  • Of course, as it is open source, any issue in any released version of its suite can be fixed easily by its active community members.
  • It supports many standard data mining tasks.


KNIME (short for the Konstanz Information Miner) is yet another open-source data integration and data mining tool. It incorporates machine learning and data mining mechanisms and uses a modular, customizable interface. This is useful because it allows you to compile a data pipeline for the specific objectives of a given project, rather than being tied to a prescriptive process. KNIME is used for the full range of data mining activities including classification, regression, and dimension reduction (simplifying complex data while retaining the meaningful properties of the original dataset). You can also apply other machine learning algorithms such as decision trees, logistic regression, and k-means clustering.

Key features:

  • Offers feature such as Social media Sentiment analysis
  • Data and Tools Blending
  • It is free and open-source, hence accessible to a large number of users easily.

4. Orange:

Orange is an Open-Source Data Mining Tool. Its components (referred to as widgets) assist you with a variety of activities, including reading data, training predictors, data visualization, and displaying a data table.vOrange can format the data it receives in the correct manner, which you can then shift to any desired position using widgets. Orange’s multi-functional widgets enable users to do Data Mining activities in a short period and with great efficiency. Learning to use Orange is also a lot of fun, so if you’re a newbie, you can jump right into Data Mining with this tool.

Key features:

  • Beginner Friendly
  • Has a very vivid and Interactive UI.
  • Open Source

5. IBM SPSS Modeler:

IBM SPSS Modeler is a data mining solution, which allows data scientists to speed up and visualize the data mining process. Even users with little or no programming experience can use advanced algorithms to build predictive models in a drag-and-drop interface.
With IBM’s SPSS Modeler, data science teams can import vast amounts of data from multiple sources and rearrange it to uncover trends and patterns. The standard version of this tool works with numerical data from spreadsheets and relational databases. To add text analytics capabilities, you need to install the premium version.

Benefits are :

  • It has a drag-and-drop interface making it easily operable for anyone.
  • Very little amount of programming is required to use this software.
  • Most suitable Data Mining software for large-scale initiatives.

6. SAS Enterprise Miner:

Statistical Analysis System is the abbreviation for SAS. SAS Enterprise Miner is ideal for Optimization, and Data Mining. It provides a variety of methodologies and procedures for executing various Analytic capabilities that evaluate the organization’s demands and goals. It comprises Descriptive Modeling (which can be used to categorize and profile consumers), Predictive Modeling (which can be used to forecast unknown outcomes), and Prescriptive Modeling (useful to parse, filter, and transform unstructured data). SAS Data Mining tool is also very scalable due to its distributed memory processing design.

Key features:

  • Graphical User Interface (GUI): SAS Enterprise Miner offers an intuitive graphical user interface that allows users to visually design and build data mining workflows. The drag-and-drop interface makes it easy to create, edit, and manage data mining processes.
  • Data Preparation and Exploration: The tool provides a comprehensive set of data preparation and exploration techniques. Users can handle missing values, perform data transformations, filter variables, and explore relationships between variables.
  • Data Mining Algorithms: SAS Enterprise Miner offers a variety of advanced data mining algorithms, including decision trees, neural networks, regression models, clustering algorithms, association rules, and text mining techniques. These algorithms enable users to uncover patterns, make predictions, and discover insights from their data.

7. Microsoft SQL Server Analysis Services:

A data mining and business intelligence platform that is part of the Microsoft SQL Server suite. It offers data mining algorithms and tools for building predictive models and analyzing data.

key features:

  • Data Storage and Management: SQL Server provides a reliable and scalable platform for storing and managing large volumes of structured data. It supports various data types, indexing options, and storage mechanisms to optimize data organization and access.
  • Transact-SQL (T-SQL): SQL Server uses Transact-SQL (T-SQL) as its programming language, which is an extension of SQL. T-SQL offers rich functionality for data manipulation, querying, and stored procedures, enabling developers to perform complex operations and automate tasks.
  • High Availability and Disaster Recovery: SQL Server offers built-in features for high availability and disaster recovery. It supports options like database mirroring, failover clustering, and Always On availability groups to ensure data availability and minimize downtime.

8. Oracle Data Mining:

Oracle Data Mining (ODB) is part of Oracle Advanced Analytics. This data mining tool provides exceptional data prediction algorithms for classification, regression, clustering, association, attribute importance, and other specialized analytics. These qualities allow ODB to retrieve valuable data insights and accurate predictions. Moreover, Oracle Data Mining comprises programmatic interfaces for SQL, PL/SQL, R, and Java.

Key features:

  • It can be used to mine data tables
  • Has advanced analytics and real-time application support

9. Apache Mahout:

Apache Mahout is an open-source platform for creating scalable applications with machine learning. Its goal is to help data scientists or researchers implement their own algorithms. Written in JavaScript and implemented on top of Apache Hadoop, this framework focuses on three main areas: recommender engines, clustering, and classification. It’s well-suited for complex, large-scale data mining projects involving huge amounts of data. In fact, it is used by some leading web companies, like LinkedIn or Yahoo.

key features:

  • Scalable Algorithms: Apache Mahout offers scalable implementations of machine learning algorithms that can handle large datasets. It leverages distributed computing frameworks like Apache Hadoop and Apache Spark to process data in parallel and scale to clusters of machines.
  • Collaborative Filtering: Mahout includes collaborative filtering algorithms for building recommendation systems. These algorithms analyze user behavior and item properties to generate personalized recommendations, making it suitable for applications like movie recommendations or product recommendations.
  • Clustering: Mahout provides algorithms for clustering, which group similar data points together based on their attributes. It supports k-means clustering, fuzzy k-means clustering, and canopy clustering algorithms, allowing users to identify natural groupings in their data.

10. is an open-source platform for machine learning and data analytics. It provides a range of key features and capabilities that make it a popular choice for building and deploying machine learning models.

Key features:

  • Scalability and Distributed Computing: is designed to scale and leverage distributed computing frameworks like Apache Hadoop and Apache Spark. It can handle large datasets and perform parallel processing to speed up model training and prediction.
  • AutoML (Automated Machine Learning): includes an AutoML functionality that automates the machine learning workflow. It can automatically perform tasks such as data preprocessing, feature engineering, model selection, and hyperparameter tuning, making it easier for users to build accurate models without manual intervention.
  • Broad Range of Algorithms: offers a wide variety of machine learning algorithms, including popular ones like generalized linear models (GLMs), random forests, gradient boosting machines (GBMs), deep learning models, k-means clustering, and more. This rich set of algorithms allows users to choose the most appropriate technique for their specific problem domain.
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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
  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.


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. 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. 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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Social Media Marketing – TIPS – Interconnect Everything & Jumps on trends early

Interconnect your entire marketing efforts:- In this blog, we are going to write down “How to interconnect your social media with your other marketing channels”.

Social Media links in Newsletter:- For starters, starting today any newsletter you send out, needs to have at least two of your favorite social media networks included in them. (I would recommend Facebook and Twitter).

Social Media Links in Website:- Your website needs to have visible links to all your social media platforms. The header and footer are usually is a good place for those.

Coupon Redeem through Social platforms:- If you have a plan on creating any coupons, give them options to redeem them on your Facebook page.

Social Media links to your contents:- If you do any kind of Content marketing, include relevant social platforms links at the end of it. Also, put your Twitter handles in your business cards.

Mention Social Networks in outdoor events:- Put all major social handles into any outdoor promotions, events, and campaigns.

Try to create as many follow-up opportunities as possible, to reach your audience in the future. At the end of the day, that’s what Social Media is all about, creating authentic connections.

According to the science:

The mere-exposure effect, and the familiarity effect:- Its psychological phenomenon by which people tend to develop a preference for things merely because they are familiar with them.

Studies on this have been done since the 1960s. And more recent 2001 proved that this is not even done at a conscious level. This means your brain doesn’t even think when liking something which it considers familiar.

Do you think your choice of a brand is random or on purpose?

Well, guess again. And this knowledge has been used by advertising agencies for ages, that’s why you see the same ADs over and over again. So remember this the next time you have any doubts about mentioning your social media accounts anywhere else within your marketing channels. Because it can make the difference if people picks your products from the line-up, or goes for your biggest competitor.

Jumps on trends early

As a business owner it’s your responsibility to innovate and bring something new for the customer before your competitiors – so always JUMPS on trends early.

Peter Drucker (Marketing Expert) once said “Business is nothing more than marketing and innovation”.

The reason why you should be an early adopter, it’s obvious – get more traction. Because there is less competition. It’s like having more channels on TV. The more of them available, the smaller the percentage of people that will look at a particular channel. Even if the content your produce is of impeccable quality, due to the sheer number of alternatives, few people will ever get to discover you. But if you are an early adopter, the chances of users stumbling onto your brand are much higher.

Thus’ the chance for your business to grow on that particular platform will be higher. Even, this effect is more powerful for non-mega brands like Coke or Ford or GM. And let’s say the network you join doesn’t grow and is all hype, the loss in time spent there for your business is always outweighed by the alternative of that particular network exploding in attention, and getting the spotlight on your brand as well.

Being an early adopter is more than a social media choice. It should be a life choice if your goals are to deliver groundbreaking work. In the past, the risks involved were higher. Think of all the lives lost and accidents when designing and creating the first airplanes, submarines or cars. It took lots of iterations to get things safe for the masses. And it will take you as well, a lot of effort to create and offer great products and services. But unlike the past, then the risk was literally life-threatening, your risk is the only time, and sometimes money, and even these two are not in huge amounts.

So the next time you hear about a new social media network on the rise, give it a look. See if it makes sense for your business, and jump on it early on, and become a power player on that platform.

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Top 5 online platforms where DevOps practitioners hangout

This is the age of Internet and social media and people’s spend more and more time on to the net and social networks. And when people spend their time somewhere they are looking for the persons who share the same interest or skills. This is the reason you can see the online world is full bustling with online communities. You can find tons of dedicated communities, forums, groups or platforms for various purposes by the people who share the same interest. But, what about DevOps ? Where you can find DevOps practitioners ? Where they interact or hangout with their communities?

Don’t worry! You will get the answer. Just keep reading.

Here, I am going to share five platforms which will provide you the opportunity to connect with peers and industry leaders, where you can share or get information and grow your professional network.

FaceBook group, DevOps India is rocking on this platform with 5000+ members in very small span of time. This is a pubic group for DevOps interested professionals around the world. Very much active and updated group. This group’s strictly follows on no-promotional activity, only updates, share information, discussion etc. Yo can see almost every members from this group participate in every discussion and at the same time we have number of followers for this group as well. You will find jobs as well where recruiters post their vacancies here, which is also very important for us and others as well.
With regards to finding helpful articles, news and general data in the DevOps, CD or Agile industry, LinkedIn is typically the ticket. We as a whole know LinkedIn is an incredible instrument for systems administration and interfacing with companions, however it has likewise ended up being an apparatus used to begin important and beneficial discussions about whatever the most recent advancement or industry organization might be. A significant number of these discussions are had in particular gatherings on LinkedIn and on the off chance that you aren’t a part, you are certainly passing up a great opportunity. This group is for all interested people in DevOps, Networking, Discussions, news, meetups and sharing materials and anything else related to DevOps. Group is getting stronger day by day with number of peoples and information they share. This group is very much active and one of the most strongest groups among all the existing groups on linkedin.
Google has given a platform to stay together and share information and update everyone. We utilises this platform and created a DevOps Group which is having 5000+ members and is one of the biggest group on google for DevOps. People who joined this are very much interested in getting updates and recent news about DevOps and Agile and Build and Release thing. Members do discussion in group about the DevOps and how to improve the field of development. Peoples do Updates on recent webinars and Newsletters which is important for every professionals of this group. So, Connected with and Stay updated.
this portal offer Q&A forums, free ebook, Free learning materials and much more., share information with others of DevOps. Here you can join us and share information related to devops and programming languages and about software development. You can share information on your research and if you have questions you can ask here and you will get solutions from the professionals. People knows how important it is to share information now a days. So this is the main motive to bring people around the world on one page where they can come and share and get solutions of the problems as well. This website is for professionals and people who want to keep themselves updated with the on-going studies, research and share informative materials.

BestDevOps are professional and expert in delivering practical and solution to transform and accelerate the way that organisation deliver software. We believe that DevOps offers a new operation model for IT organization to deliver software at speed. This enables innovation which drive competitive advantage. We are slowly getting famous in engaging customer by learning, educating, transforming. We gather all the blogs from different companies, different country and from experts of DevOps in one place which is I am sure soon we gonna be widely regarded as a global best leader in educating people in devOps space. Bestdevops is the DevOps portal for a website that covers a wide range of area in DevOps. This portal gets frequently updated. Our followers are on facebook, linkedin, pinterest, tumblr, scoop.

I hope that this list will help you to connect and hear from DevOps professionals who can provide you with information about everything DevOps related that you need to know.


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Platforms where you can find DevOps, Trainers, Coach, Training and Instructors easily


If you are related with Software industry or if you are in IT, you must heard about the word “DevOps”. It is a small word which consist the whole process or life-cycle inside of software development these days. So, it’s must for every software and IT professionals and students to know about the DevOps complete process and its best practices because without this you cannot get your project done which means your business or your job will be suffer. So who wants to lose their business or job? No one!!! Right! So here comes the Question how can you save your business or job? Of course by learning and implementing DevOps in your work environment and for that you need DevOps trainers or I would like to say Quality trainers.  So, if you want to know where you can get the training, trainers or instructors or  coach for DevOps than keep reading this article.

Before going further, let’s see what is DevOps?

It is a culture that improves the IT services delivery agility on the basis of continuous collaboration, communication and integration. It might sound straightforward but it’s a little more complex than that. It’s identifies the connections among ideas, tools and subject of development of software and IT operations, using rapid iterations and continuous improvement. In other words operations and developers teams work together to get the result faster without sacrificing the quality.

Now, let’s see the benefits of DevOps?


DevOps improves the organization performances, enhances the profitability and effectiveness of developers and operations groups. If we look on to the benefits of DevOps than the major benefits are

  • Technical benefits: Continuous software delivery
  • Technical benefits: Less complex problems to fix
  • Technical benefits: Faster resolution of problems
  • Business benefits: Faster delivery of features
  • Business benefits: More stable operating environments
  • Business benefits: More time available to add value (rather than fix/maintain)

These are the major benefits of implementing DevOps, but to get these benefits you must know how to start and implement it in your work environment with right set of tools in your arsenal and for that you need Quality DevOps trainers and Coach who can help you to get best out of it. But before searching keep some qualities in your mind a DevOps trainer should have.

“Qualities and skills” should have in a DevOps Trainer


1.       Experience: – You should find DevOps trainer or coach that has successfully embraced a DevOps culture on a large scale and also has experience in industry.
2.       Knowledge: – You need an expert to guide you, to share dos and don’ts and to give rules and best practices so you comprehend when not to compromise.
3.       Personal Abilities:- Trainer ought to be patience, flexibility, empathy, ability to nurture others, creativity, commitment to the work and also the ability to be a team player
4.       Simplifying Ability: – Illustrate complex concepts you must draw comparisons to a variety of easily recognizable elements.
5.       Create an Environment :- An experienced trainer is attuned to his or her own energy level and that of the class
6.    Motivational Skills: – In order to create as many organic learning moments as possible a trainer has to encourage participants to learn themselves.
7.       Subject Expertise:- An expert DevOps trainer should have excellent understanding of the subject of the training
8.       Communication: – A qualified DevOps trainer must have good communication skills for effective delivery.

These are the important skills or qualities a DevOps Trainer should have in them.

Now, the real challenge “Where to find DevOps Trainers or Coach”? As we all do “ask Google”, I also did it and find myself lost in the search results. I just got confused by reading them, that’s why I shortlisted few of them and did some more research and I got the result by finding two best platforms for DevOps Trainers.

1. DevOpsTrainer

This is the right platform for searching DevOps Trainers, Instructors and Coach for Individual and Corporate training. The reasons is, this site has a very strong policies and regulations for listing of DevOps trainers. This site contains the list of best trainers for almost all cities worldwide.  Some of them are Amsterdam, Pune, Bangalore, San Diego, Dubai, Singapore, Mumbai, Noida, Hyderabad, Israel, Paris, Madrid, Dublin, Seattle and various others across the world.

2. scmGalaxy

This is one platform where you can find everything related to DevOps. Whether you are looking for DevOps tutorials, Trainers, Coach’s, Instructors, DevOps Community, Forum, DevOps Courses, Certification and Webinars you can get all in one place. scmGalaxy is the largest scm, DevOps, Build & Release community worldwide. They provide all there services worldwide and there dedicated DevOps trainers are well known in the industry.

I recommended these two platforms on the basis of my research, explore them and share your experience and if you know about any other platforms than feel free to share with us in comment box.
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