Why the Data Warehouse is Back (And Why it Never Really Went Away)

The rise in Enterprise data volumes and the increasing use of semi-structured data gave rise to Big Data and NoSQL platforms. But the conventional data warehousing model never went away. And with innovations in cloud object storage and compute capabilities, the data warehouse model has come out of the shadows and back into the spotlight. Data silos were a problem even in the old days, but the challenge they pose today is acute. Some organizations, still wary of older storage costs and cost models, are conservative in the data they preserve. Others tend towards the opposite extreme, saving data in cloud object storage with such abandon that they engender impenetrable repositories that form huge silos of their own. Since data warehouses have always sought to integrate siloed data, their role – in everything from analytics to machine learning – is more pivotal now than ever. But how can today’s cloud data warehouse platforms address both the old silos and the new? What can they do with semi-structured data? How can they integrate with data lakes and/or purify data swamps? And can they enable analytics on data and platforms where doing so had been an afterthought, at best? To get the answers, join us for this free 1-hour webinar from GigaOm Research. The Webinar features GigaOm analyst Andrew Brust and special guest, Ross Perez from Snowflake, a leader in cloud-native data warehousing. In this 1-hour webinar, you will learn: •How cloud data warehouses can scale both storage and compute, independently and elastically, to meet variable workloads •Distinct approaches for working with semi-structured data from structured data platforms •Why the equation for data warehouse and data lake doesn’t sum to zero •Whether the familiar relational/SQL paradigm can coexist with Big Data analytics and fluid, interactive performance