In this episode we speak with Justin Borgman, Chairman & CEO at Starburst, which is based on open source Trino (formerly PrestoSQL) and was recently valued at $3.35 billion after securing their series D funding.  In this episode we discuss convergence of DW’s / DL's, why data lakes fail and much much more. 

Top 3 takeaways

• The data mesh architecture is gaining adoption more quickly in Europe due to GDPR.
• There were two main limitations of data lakes when comparing to DW’s, performance and CRUD operations. Performance has been resolved with query engines like Starburst and tools like Apache Iceberg, Apache Hudi and Delta Lake are starting to close the gap with CRUD operations. 
• The principle of a single source of truth / storing everything in a single DL or DW is not always feasible or possible depending on regulations. Starburst is bridging that gap and enabling data mesh and data fabric architectures. 

In this episode we speak with Paul Singman Developer Advocate at Treeverse / LakeFS. LakeFS is an open source project  that allows you to transform your object storage into a Git-like repository. 

Top 3 takeaways

• LakeFS enables use cases like debugging to quickly view historical versions of your data at a specific point in time and running ML experiments over the same set of data with branching..
• The current data landscape is very fragmented with many tools available.. Over the coming years there will most likely be consolidation of tools that are more open and integrated. 
• Data quality and observability continue to be key components of successful data lakes and having visibility into job runs. 

In this episode we speak with Matt Topol, Vice President, Principal Software Architect @ FactSet and dive deep into how they are taking advantage of Apache Arrow for faster processing and data access. 

Below are the top 3 value bombs:

• Apache Arrow is an open-source in-memory columnar format that creates a standard way to share and process data structures.
• Apache Arrow Flight eliminates serialization and deserialization which enables faster access to query results compared to traditional JDBC and ODBC interfaces.
• Don’t put all your eggs in one basket, whether you're using commercial products or open source, make sure you design a modular architecture that does not tie you down to any one piece of technology.