Quick Introduction to DataOps Tools and Best Practices

November 17, 2018 

Quick Introduction to DataOps Tools and Best Practices

What is DataOps?

DataOps is a Data Operation, and it is 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.

How DataOps Works?

DataOps is a Combination of Data + Operations, as supporting an iterative lifecycle for data flow -

  • Build
  • Execute
  • Operate
  • Protect

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 the data flow performance. Monitor Pipelines, gather metrics, fulfill 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.


How to Adopt DataOps?

Add Data and Logic Tests - DataOps duty is to interact every time with a "Data Analytics Team" member makes a change, add tests for that change. There are two types of tests -

  • Logic Tests cover the code in a Data Pipeline.
  • Data Tests cover the data as it flows by in production.

Put all steps to Version Control - There are lots of stages of processing that turn raw data into useful information for stakeholders. To be valuable, data must progress through these steps, linked together in some way, with the ultimate goal of producing a Data-Analytics output.

Branch & Merge - Branching and merging are the main productivity boost for Data Analytics Team to make any kind of changes to the same source code files. Each team member control work environment space. Test programs, make changes and take risks.

Use Multiple Environments - Every Data Analytics team have tools in laptop for development. Version Control tools allow working at a private copy of code while coordinating with other team members. It cannot be productive if don't have the data required.

Reuse and Containerize - In DataOps, the analytics team moves so faster like lighting speed by using highly optimized tools and processes. One of the Productivity tools is to Reuse and Containerize. Reuse Code means reusing Data Analytics components. Reuse code saves time also. Container means to run the code of the application. It a platform like Docker.

Parameterise processing - Parameters allow to code to generalize to operate on a variety of input and also respond it. Parameters used for the improvement of productivity. In this, use program to restart at any specific point.


Benefits of DataOps

  • Raw Source Catalog.
  • Movement/Logging/Provenance.
  • Logica Models.
  • Unified Data Hub.
  • Interoperable (Open, Best of Breed, FOSS & Proprietary).
  • Social (BI Directional, Collaborative, Extreme Distributed Curation).
  • Modern (Hybrid, Service Oriented, Scale-out Architecture).

Why DataOps Matters?

Collaborating throughout the Entire Data Lifecycle - Collaboration is the main part of the both DevOps and DataOps. But DataOps involved in many more desperate parties instead of Software Development counterpart. That’s why DataOps is the entire data lifecycle of the organization.

Establishing Data Transparency while maintaining security - DataOps promote the data locally, team analysis uses computer resources near to data, instead of moving the data required.

Utilizing Vision Control for Data Scientist Projects - DataOps use this concept on the Data Science. They use this concept when hundred of Data Scientists work together or separately on many different projects. When Data Scientist work on their local machines then data saved locally which slow downs the productivity. To reduce this, make a common repository which solves this problem.


Best Practices of DataOps

  • Versioning.
  • Platform Approach.
  • Self-service.
  • Team makeup and Organisation.
  • Unified Platform for all data- historical and Real-Time production.
  • Multi-tenancy and Resource Utilisation.
  • Access Model and Single Security for governance and self-service access.
  • Enterprise-grade for mission-critical applications and Open source tools.
  • Run Compute on data platform- leverage data locality.

Tools For DataOps