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Data Integration for Cloud Data Lakes: Architecture and Best Practices
A practical handbook for data engineers, data scientists and data architects
Data lakes are key to scalable, open data architectures – but they can pose challenges to data engineering teams. Efficient data integration is the difference between a bunch of log files sitting in Amazon S3 or Azure Storage and a high-performance data lake that provides real value to analytics, data science and engineering teams.
Download our free white paper to learn:
- Guiding principles for modern data lake architecture
- Best practices in data lake engineeing
- How to create ingestion pipelines: schema discovery, retention policy, logographical ordering
- Guidelines for evaluating ETL, ELT and data movement tools
- Data processing with Spark vs alternative solutions
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Who should read this guide?
- Data architects and engineering leaders looking to improve data freshness and availability while reducing engineering overhead
- Data engineers who want to build more efficient ingestion, ETL and data replication flows on their data lake
- Data scientists who need self-service access semi-structured data from cloud storage using SQL
