Dataops has become a recent trend these years while building data & analytics solution on public cloud platforms like Azure, AWS & GCP, etc. Dataops elevates the best practices of Devops & Data Engineering to build the data platforms on cloud.
In this article, we will explore about what is the difference between devops & dataops, dataops for data engineering on Azure.
Difference between Devops & Dataops
Devops engineers focus on developing & delivering the software system while dataops focuses on building, testing, and releasing the data solutions.
But CI/CD pipelines for Dataops & Devops has different delivery life cycles.
Devops life cycle focuses on,
- Continuous Integration with Build pipelines
- Continuous Deployment with Release pipelines
- Continuous Testing to improve data quality
Dataops life cycle focuses on,
- CI/CD pipelines for application deployment
- Ensure Relevant data & related components are present & configured
- Monitor authenticated & authorized access to data
Dataops for Data Engineering on Azure
Dataops helps data engineering to implement the end-to-end orchestration pipelines including the data platform components & application code (Python, spark, etc) & environment specific information.
It helps data engineers to efficiently collaborate with the data stakeholders to achieve scalability, reliability, and agility.
Major steps involved in building the dataops pipelines on Azure are as follows,
Datazones in Azure Data Lake
Most enterprise organizations follow below strategy to manage datazones in the Azure Data Lake ADLS Gen2.
- Raw Data Store
- Data Cleansing & Transformation Store
- Aggregated Data Store
Automated Data Validation & Quality Checks using Azure Data Factory & Databricks
We can use databricks notebooks to create automated data validation & quality check using programming languages like python, scala, pyspark, etc.
In order to use the sequence of databricks notebooks in the logical order using Azure Data Factory as an orchestrator.
Git Integration for Code Development
While doing the development with databricks notebooks and data factory code then integrate the azure services with Git integration tools like Azure Devops, Bit bucket etc to maintain the code versioning & centralized code repository.
Continuous Integration & Deployment for Data Engineering workloads
The best practice is to integrate Azure Devops with CI/CD pipeline which will download the artifacts from the Azure Devops repo, perform continuous testing to ensure the quality of the code. Once the testing is successful then release pipeline will make sure that deployment to all environments will be done automatically.
Dataops enables data engineers to efficiently develop the code ensuring the quality of the code and reduce time to market for the application development.