DataOps
DataOps is a process-driven, automated technique, which data engineering and analytics teams can adopt for continuous integration and continuous deployment (CI-CD) of their DDL, DML, ETL, scripts, code and more to bring down the cycle time of data analytics and enhance data quality.
Business aspires to use modern data technologies to gain a competitive edge and answer to business needs while IT is challenged in meeting this innovation need through faster and agile data engineering processes. What took weeks and months to do, now needs to happen in hours and days.
DataOps, Data Operations, is an extension of DevOps that focuses on streamlinig data engineering processes that enhances the speed, accuracy and thrustworthiness of data for analytics, insights and data science. DataOps is a process-driven, automated technique, which data engineering and analytics teams can adopt for continuous integration and continuous deployment (CI-CD) of their DDL, DML, ETL, scripts, code and more to bring down the cycle time of data analytics and enhance data quality. DataOps requires change in Data Engineering & Analytics creation processes.
Why DataOps?
- The business will have more rapid access to trustworthy data
- More empowered data analytics team
- Improves speed of deploying
- Improves quality of software
- Change request management becomes more easy
- More flexible IT & Business operating model
DataSense DataOps Framework
The devops framework is built in Python to enable auto deployment and CI-CD. It enables automatically deploying Vaultspeed generated DDL and Talend ELT jobs using Jenkins and Git + updating the flow management control (FMC). A dashboard built in MS Power BI gives you an overview for each release, this enables the team to gain insights for each release.
The framework will save the team a lot of time in manual interventions and will increase the speed and quality of each release.
