Big Data Needs DevOps and Agile Practices as DataOps Principles for Continuous Delivery and Integration Pipeline with Different Tools
DataOps is a Data Operation, and it is the latest Agile operations method from the collective of IT and Big Data professional. It works on Data Management practices and processes which improves the accuracy of analytics, speed, automation including data access, integration, and management. It also helps in managing data with goals for that data. DataOps combines Agile Development, DevOps and Statistical Process controls and applies them to Data Analytics.
DataOps is a Combination of Data + Operations, as supporting an iterative lifecycle for data flow –
Build – Build is a design topology of repeatable data flow pipelines, flexible using configuration tools rather than hard coding. Cross-functional teams build adaptable, repeatable data flow topologies.
Execute – On Edge system run pipelines and also run a pipeline in Autoscaling On-premises Cluster or Cloud-environment. Across Multiple Cloud and On-premises.
Operate – Continuous Monitoring manages data flow performance. Monitor Pipelines, gather metrics, fulfil SLA’s.
Protect – Data protection done by DataOps tools integrated with unauthorized access, data stores, authorized systems, and authentication. Handles sensitive data, provide metadata to governance systems.
Visual Analytics and Advanced Data Visualization - How CanvasJS help enterprises in creating custom Interactive and Analytical Dashboards for advanced visual analytics for data visualization
Visualization Best Practices for Data Scientists. Disclaimer: The ideas presented in this article are from the book: Story Telling With Data by Cole Nussbaumer Knaflic.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
The Importance of Data Visualization - It is the process of converting raw data at hand into easy and understandable image-photo-graphics for fast, effective and accurate…
How to use graphs effectively while working on Analytical problems. Data visualization is the process of creating interactive visuals to understand trends, variations, and derive meaningful insights from the data. Data visualization is used mainly for data checking and cleaning, exploration and discovery, and communicating results to business stakeholders.