DuckDB + DuckLake: Building a Lightweight Data Lakehouse Without Heavy Infrastructure скачать в хорошем качестве

DuckDB + DuckLake: Building a Lightweight Data Lakehouse Without Heavy Infrastructure

📌 TL;DR DuckDB and DuckLake enable a lightweight data lakehouse architecture built on an embedded OLAP database model for modern data stacks. How to build a portable analytics platform with warehouse-grade features (transactions, schema evolution, time travel) without heavy infrastructure? 👉 In this AI Tech Experts Webinar, Grzegorz Rybak (Senior Data Engineer) and Cezary Gorczyński (Data Engineer) explore whether #DuckDB + #DuckLake can serve as a practical lightweight data platform for modern analytics workloads. They walk through common consulting scenarios where teams must balance fast delivery with long-term scalability and explain how a DuckDB-based stack can provide strong analytical performance without committing to a full warehouse platform. Topics covered DuckDB as an embedded OLAP analytical engine querying Parquet directly with a zero-ingest workflow DuckLake as a lakehouse management layer the “Holy Trinity” architecture: compute, storage, metadata real trade-offs: concurrency limits, streaming gaps, file fragmentation 🧷 Check out our website: https://deepsense.ai/ 🧷 Linkedin:   / applied-ai-insider   00:00 DuckDB intro 00:57 Client problems: greenfield vs legacy data stacks 06:42 DuckDB architecture and in-process analytics engine 12:38 DuckLake lakehouse layer and the “Holy Trinity” architecture 17:23 Trade-offs: concurrency, scaling and streaming limits 21:37 Conclusions: portable lakehouse strategy #dataengineering #DataLakehouse #ModernDataStack #AnalyticsEngineering #OLAP #DataInfrastructure #AIData