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TECHNICAL PAPER
Iceberg Lakehouse Architecture: Adapting for High-Scale Streaming Data
Apache Iceberg and lakehouse architectures are rapidly gaining popularity. While implementing Iceberg for some use cases is straightforward, adapting it for large-scale streaming data requires advanced configuration and expertise.
Read this technical paper to understand the adaptations needed to optimize Iceberg for high-scale streaming data, addressing key challenges and performance improvements.
Some of the topics covered include:
- Merge-On-Read Paradigm: How Iceberg’s native support for Merge-On-Read (MoR) tackles the small files problem and enhances data update performance.
- Efficient Data Deletes: Techniques for applying equality deletes in streaming data scenarios to reduce file scan overhead and improve query efficiency.
- Query Engine Optimizations: Approaches for handling frequent updates and minimizing I/O operations in streaming environments.
- Streaming Updates API: Insights into the new API designed to commit multiple data updates efficiently, reducing overhead and improving overall system performance.
- And more...
Download Whitepaper
This whitepaper serves as a comprehensive resource for those looking to leverage Apache Iceberg in their data engineering and machine learning applications.
Who should read this guide?
- Data Engineers and Data Architects: Professionals focused on designing and optimizing data pipelines and storage solutions will find valuable insights into implementing Apache Iceberg to manage large-scale data efficiently.
- Technical Managers and CTOs: Decision-makers responsible for choosing technologies and planning data strategies will gain insights into the advantages of Iceberg over traditional data management solutions, supporting informed technology selection and investment.
- Database Administrators and Developers: Those managing and developing database systems can learn about Iceberg's approach to solving common issues related to data consistency, performance, and schema management.
