Jorrit Sandbrink, a data engineer specializing on open table formats, discusses the advantages of decoupling storage and compute, the importance of choosing the right table format, and strategies for optimizing your data pipelines. This episode is full of practical advice for anyone looking to build a high-performance data analytics platform.
- Lake house architecture: A blend of data warehouse and data lake, addressing their shortcomings and providing a unified platform for diverse workloads.
- Key components and decisions: Storage options (cloud or on-prem), table formats (Delta Lake, Iceberg, Apache Hoodie), and query engines (Apache Spark, Polars).
- Optimizations: Partitioning strategies, file size considerations, and auto-optimization tools for efficient data layout and query performance.
- Orchestration tools: Airflow, Dagster, Prefect, and their roles in triggering and managing data pipelines.
- Data ingress with DLT: An open-source Python library for building data pipelines, focusing on efficient data extraction and loading.
Key Takeaways:
- Lake houses offer a powerful and flexible architecture for modern data analytics.
- Open-source solutions provide cost-effective and customizable alternatives.
- Carefully consider your specific use cases and preferences when choosing tools and components.
- Tools like DLT simplify data ingress and can be easily integrated with serverless functions.
- The data landscape is constantly evolving, so staying informed about new tools and trends is crucial.
Sound Bites
"The Lake house is sort of a modular setup where you decouple the storage and the compute." "A lake house is an architecture, an architecture for data analytics platforms." "The most popular table formats for a lake house are Delta, Iceberg, and Apache Hoodie."
Jorrit Sandbrink:
Nicolay Gerold:
Chapters
00:00 Introduction to the Lake House Architecture
03:59 Choosing Storage and Table Formats
06:19 Comparing Compute Engines
21:37 Simplifying Data Ingress
25:01 Building a Preferred Data Stack
lake house, data analytics, architecture, storage, table format, query execution engine, document store, DuckDB, Polars, orchestration, Airflow, Dexter, DLT, data ingress, data processing, data storage
