Simplifying Big Data in a Multi-Cloud World with Streamlined Workflows

A typical big data workflow that takes data from ingestion to delivery is highly complex and has numerous dependencies along the way. Speed, reliability, and scalability are crucial. So, although data scientists and engineers may do things manually during proof of concept, manual processes don’t scale.

To deal with that complexity you need industrial strength workload automation, but most tools are platform-specific and limited in functionality. You need a reliable, fail-safe way to automate and orchestrate every step of big data processing across all involved environments.

This report looks at how, with the right tools, you can resolve critical issues before deadlines are missed and get your insights into the hands of the right people, whether they are employees within your business, or customers.

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