Presented by Anthony Algmin
Data warehouses (DWs) used to be the center of the universe, taking relatively raw data inputs, transforming them, and putting the results into a tidy place for downstream people and systems to consume at will. Everybody wanted one!
Over time, businesses continued to evolve, and more data of all sorts was being created at the same time data consumption demands were exponentially escalating. DWs were poorly suited to keeping up with the rapid pace of change, as DWs are big, powerful machines with many interdependencies.
Enter Data Lakes. Created as lightweight data clearinghouses that downstream people and systems could use to do nimble, ad hoc data analytics, Data Lakes were able to handle the unprecedented throughput required by the fast-growing data and related demands. Their downside is, with little structure, things quickly become messy.
Today the Big Data evolution continues, and new technologies strive to give us the best of both DWs and Data Lakes. Learn the latest in this session!
Key takeaways include:
Data Lakes Are Not Just Hadoop Anymore
Why Cloud Has Changed Everything
Highly Aligned, Loosely Coupled Data Architecture Patterns
An Introduction to Ephemeral Data Warehousing
Predictions for the Future