Building an Efficient Data Architecture for Maximum Productivity
Are your data engineering teams spending weeks or even months on tedious ETL, OLAP cubes and BI extracts in order to provision data sets with enough performance for your BI and data science stakeholders?
It is finally possible to shrink that time to minutes with a dramatically different—and simpler—data architecture. What’s needed is a new category of query engine, a data lake engine, that is purpose-built for lightning-fast queries directly against cloud data lake storage like Amazon S3 and Microsoft ADLS.
Meet the co-founder & CPO of Dremio in this live webinar, as he describes how you can increase productivity and efficiency while also reducing time to insight.
Tomer Shiran Founder & CPO Dremio
Jeff King Sr. Program Manager Microsoft
Ryan Murray Principal Consulting Engineer Dremio
Speaker Name 4
Learn how to:
Eliminate time-consuming ETL, cubes, extracts and aggregation tables
Leverage virtual datasets to avoid redundant, hard to govern copies of data
Enable secure self-service for data analysts
Drastically reduce the EC2 costs associated with compute queries