Data pipeline management is incredibly complex. It relies on manual processes, custom integrations, and in-built job schedulers, so data becomes more unwieldy as it grows in type and volume.
This white paper follows Jonathan, a data team lead, as he searches for a way to scale and operationalize various data pipelines at his company. Learn from his journey as he discovers how to deploy DataOps methodologies while orchestrating the automated processes across his entire data toolchain.
Download this whitepaper to discover:
- What data operations (DataOps) is... and isn't
- How to effectively incorporate dev/test/prod promotion into DataOps lifecycle management
- Why it's crucial to centrally control and orchestrate the tools and platforms throughout your data pipeline, including data sources, data integrators, data warehouses and lakes, data analytics, and data delivery
- How automation enables data-as-a-service, which makes real-time data available on-demand to decision-makers throughout the enterprise
- How a service orchestration and automation platform can help you centralize control of your data toolchain, encourage cross-functional collaboration, and manage the DataOps lifecycle