#9 Jorrit Sandbrink on Modern Data Infrastructure for Analytics and AI, Lakehouses, Open Source Data Stack
May 24, 2024
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Jorrit Sandbrink, a data engineer, discusses lake house architecture blending data warehouse and lake, key components like Delta Lake and Apache Spark, optimizations with partitioning strategies, and data ingress with DLT. The podcast emphasizes open-source solutions, considerations in choosing tools, and the evolving data landscape.
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.
Deep dives
Lake House Architecture and Technology Choices
Decoupling storage and compute in a lake house architecture allows for various choices starting with selecting storage location, often between the cloud or on-premise. Popular table formats like Delta Lake, Iceberg, and Apache Hoodie, with a religious debate among users, provide metadata layers on Parquet file formats for efficient data management. The importance of choosing a table format like Delta Lake with Parquet as a unified file format is highlighted.
Lake House Architecture Advantages and Implementation
The lake house architecture combines the benefits of data warehouses and data lakes into a unified platform, addressing the limitations of both. Introduced in a 2021 white paper by Databricks founders, the architecture aims to streamline data analytics workflows into a single platform. Challenges in setting up a data lake house emphasize the need for selecting suitable file and table formats alongside a query execution engine.
Orchestration, Optimization, and Data Ingestion
Factors to consider in a lake house setup include active management strategies like partitioning data based on collection date to enhance query performance. Various tools like Pollers and DuckDB offer lightweight compute engine alternatives to Apache Spark for querying Delta tables efficiently. Orchestration tools such as Dexter provide streamlined data pipeline creation with triggers, processing steps, and connections to data sources and targets for effective data processing and storage.
Future Data Stack Considerations and Ideal Tooling
The ideal data stack for storage would include Delta as a mature table format, while considering Pollers for its lightweight and fast compute engine suitable for Python users for data processing. Orchestration with Dexter, known for improvements over tools like Apache Airflow, complements data collection and processing smoothly. Desired data tooling advancements involve open-source libraries for type mapping to streamline data type conversions between different systems for enhanced data handling and management.
Professional Growth and Future Plans
Looking ahead, the individual aims to continue working on data tooling and contribute to projects like DLT, bridging the domains of data engineering and software engineering. Balancing interests in data and software engineering, the focus remains on innovating tools and platforms for streamlined data workflows. The LinkedIn profile serves as a platform for sharing updates, content, and connecting with others interested in data and software engineering endeavors.
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."
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
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