whitepaper

All types of enterprises use data lakes as a cost-effective, centralized repository to support a wide range of analytics use cases, from operational dashboards to data science, machine learning and even big data processing. Amazon Web Services (AWS) powers many of today’s cloud data lakes, making them more scalable and simpler to deploy and manage—and making the data they house easier to manage and analyze. Despite these advantages, some AWS users still experience challenges related to data lake performance, data workflows and data access. But there are ways to overcome them.

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Read this eBook to learn:

  • The top five best practices to help data lake architects realize an optimal data architecture
  • How to accelerate queries of data stored in an AWS data lake while reducing data engineering costs
  • How to simplify data access for analysts and data scientists
  • How to complement and extend the existing capabilities of an AWS data lake

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