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Introduction

Data lakes require adaptation of the data management practices that are already in place with enterprise data warehousing and data archiving. The emphasis is on adaptation because the data lake is a different environment.

Highlights

  • Data lakes must be managed.
  • Data lakes will only succeed if they become shared resources.
  • For data lakes to become a shared resource, business end users must be prepared to take on new self-service responsibilities in curating data.

Features and Benefits

  • Provides the deep dive on how to govern the data lake.
  • Assesses the readiness of the tooling and technology landscape, and the state of best practices.
  • Discusses why business end users must take more active roles in governance compared with their past experience with data warehouses.

Key questions answered

  • What are the key building blocks for governing a data lake?
  • Why govern a data lake?
  • How mature are the technologies, tools, and practices for governing the data lake?
  • How will emerging real-time compute and storage technology price/performance impact data lake governance?
  • What is the division of labor between IT and the business/end users when it comes to governing the data lake?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages

Ovum's definition of "data lake"

  • A lake, not a [choose your metaphor]
  • How do data lakes differ from enterprise data warehouses?

The ground rules

  • What are the degrees of "governance"?
  • Is the data lake for everybody?
  • How to sell the data lake to business end users?
  • Data lake governance: Where to start?

The recipe for the data lake

  • Data inventory
  • Security
  • Operation/integration

Appendix

  • Methodology
  • Further reading
  • Author

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