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Enterprise Data Management: Big Data

A guide to Implementing big data solutions with open source

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As the sophistication and complexity of our connected world rapidly increases, so

do the challenges faced by enterprise data management teams. With concerns

ranging from information security to the cost of operations and adopting

emerging technologies like AI, data management teams are increasingly finding

themselves in need of cost effective, scalable solutions to so-called “big” data

management. Teams need effective solutions to help address the multiplicity of

demands placed on them by the business.

Big data management should not be complex

In this guide, we review the needs of enterprise data management teams in

detail and take a view on the contemporary big data landscape. This report also

examines big data management through the lens of the new AI revolution and its

implications for enterprise data management teams. We present the Canonical

Data Fabric and MLOps technology suites for data management and outline two

of Canonical’s open source solutions for enterprise data management at scale -

Charmed Spark and Charmed Kafka.

Learn how to navigate big data management with open source

The contemporary landscape for big data is dominated by open source software and cloud based software as a service, which bring a number of advantages as well as challenges for their users. In this guide we discuss:

  • Tasks and challenges of big data management
  • Enterprise solutions for big data
  • How to kick off big data management with open source
  • Canonical Data Fabric and MLOps tools
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