If you’re a data engineer tasked with building and managing data pipelines, Starburst Galaxy enables you to build a data pipeline workflow using modern data lakes and SQL. This approach offers both simplicity and power. What might have required a complex, user defined function (UDF) in Python using other systems can be accomplished with the accessibility and universality of SQL alongside the ease and cost effectiveness of the data lake.
Today, we are thrilled to introduce support for Python DataFrames in Starburst Galaxy with the launch and support of the PyStarburst and Ibis libraries! These libraries enable you to leverage the flexibility of Python while capitalizing on the scale and performance of the leading MPP SQL query engine. This also allows for a more seamless integration into development practices like version control, CI/CD, unit tests, etc.
A robust incident response program is a key component of a business’ cybersecurity and risk management strategy. The speed of investigation is of paramount importance — delays can lead to regulatory fines, reputational harm and costly system damage. Data analytics is an effective tool in incident investigation and triaging. Read on to see how Starburst Galaxy can be utilized as a building block in your organization’s incident response program.
In a new report Cloud Data Warehouse vs. Cloud Data Lakehouse: A Snowflake vs. Starburst TCO and Performance Comparison, published by GigaOm, concluded that a Starburst lakehouse architecture could achieve superior price-performance and significantly faster time-to-insight at a much lower total cost of ownership (TCO).