Smashing Silos with Domain Data Stores. In this blogpost you will learn about how to break big data monoliths apart.
Data-driven decision-making shift In the years since data warehouses became a commodity, much has changed. Distributed systems have gained great popularity, data is larger and more diverse, new database designs have popped up, and the advent of cloud has separated compute and storage for increased scalability and elasticity. Combine these trends with the shift from centralized to domain-oriented data ownership, and you will immediately understand the importance of changing the way data-intensive applications must be designed.
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.
A data lake is totally different from a data warehouse in terms of structure and function. Here is a truly quick explanation of "Data Lake vs Data Warehouse".
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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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.