Every team that takes Data seriously should get a DataOps person ASAP. It starts with a small team where a couple of data engineers (and database engineers) start catering to the requests of data analysts and scientists
It starts with a small team where a couple of data engineers (and database engineers) start catering to the requests of data analysts and scientists. Initially, the requests are simple. They don’t take much time to fulfil. As the team size grows, the data engineers take more and more additional work. A time comes when the data engineering team becomes a blocker for the business. How? Data engineers start by catering to requests that are ad-hoc in nature. They deviate from their main responsibilities of developing new pipelines, adding new sources of data, fixing issues with old data, modelling the data in lakes and warehouses and so on. In addition to the ad-hoc requests from the data analysts and data scientists, the data engineers also start catering to infrastructure, CI/CD, source control, container, system administration issues.
In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
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Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
This post explains what a data connector is and provides a framework for building connectors that replicate data from different sources into your data warehouse
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