Ultimately, the consistent and reliable flow of data across people, teams and business functions is crucial to an organization’s survival and ability to innovate. And while we are seeing companies realize the value of their data — through data-driven product decisions, more collaboration or rapid movement into new channels — most businesses struggle to manage and leverage data correctly.

The vast majority of company data today flows into a data lake, where teams do data prep and validation in order to serve downstream data science and machine learning initiatives. At the same time, a huge amount of data is transformed and sent to many different downstream data warehouses for business intelligence (BI), because traditional data lakes are too slow and unreliable for BI workloads. Depending on the workload, data sometimes also needs to be moved out of the data warehouse back to the data lake. And increasingly, machine learning workloads are also reading and writing to data warehouses. The underlying reason why this kind of data management is challenging is that there are inherent differences between data lakes and data warehouses.