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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

Upsolver powers data lakes for data-intensive companies