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Companies building a modern data lake on Starburst
Data lakes promised a cost-effective, scalable storage solution but lacked critical features around data reliability, governance, and performance. And legacy lakes required data to be landed in their proprietary systems before you could extract value. Enter the modern data lake.
The modern data lake is a cost-effective, performant, and future data architecture that is built on an open foundation:
HOW THE MODERN DATA LAKE COMPARES
The modern data lake overcomes the limitations of legacy lakes, because it’s built with the understanding that center of gravity does not mean a single source of truth. It works with your other data sources in an open, scalable manner – creating a single, open system to access and govern the data in and around your lake.
Legacy Data Lake
Modern Data Lake
Limited to the data lake
Universal access to data in and around the lake
Limited to a single format (e.g. file formats in Hadoop)
Support for all modern formats Iceberg, Delta Lake, Hudi
$ (can be expensive with proprietary vendors)
Raw data storage, ML
BI, SQL, ML, Real-Time Apps
Low quality, data swamp
High-quality, reliable data with ACID transactions
Poor governance because security needs to be applied to files
Fine-grained security and governance for row/columnar level for tables
Starburst is the analytics platform for your modern data lake. It provides a single point of access for teams to discover, govern, analyze, and share data in and around your data lake.
Hundreds of the most data-driven companies on the planet, including Grubhub, Verizon, and Lucid, chose Starburst to break down data silos and increase time-to-insight.
With a multitude of databases and data platforms, Genus’ data engineers were burdened by complex ETL pipelines that took weeks to run.
Time-to-insight was accelerated by 75% after turning to Starburst to query data directly from Genus’ data lakes (in Amazon S3 and ADLS).
Senior Manager of Data Science & Data Engineering, Genus
Transitioning from a legacy data warehouse to an AWS cloud data lake proved challenging without a fast and reliable way to query its distributed data.
Having a powerful data lake analytics engine allows Zalando to accomplish its Customer 360 program, which increases wallet share and improves buyer recommendations.
Engineering Lead, Zalando
Requests for data sets took hours, and sometimes days, to fulfill and required lots of movement between zones in the data lake.
Time-to-insight was reduced from days to seconds by using Starburst to explore near real-time data on and around Banco Inter's data lake.
Data Engineering Manager, Banco Inter
Up to $500 in usage credits included