Over the past decade, data lakes based on scalable cloud object stores such as Amazon S3 have become a preferred way of storing and analyzing enterprise data. They offer flexibility and scalability, and they are more cost-efficient than on-premises solutions or traditional data warehouses.
While cloud object stores provide an ideal platform for data lake storage, they sometimes fall short when it comes to business intelligence (BI) workloads. Even with SQL query engines, cloud data lakes are often too slow and lack the data governance required for enterprise applications. Organizations are striving to strike the right balance between their data warehouse and data lake investments.
This paper explains how Dremio makes it practical to run BI and data science workloads directly on S3 object stores. With a modern cloud data lake engine, data teams can simplify their environments and get maximum value from their cloud data lake investments while business analysts benefit from live, interactive analytics.
Sponsored by Dremio
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