Why workflow orchestration is critical to delivering scalable and reliable data and analytics solutions

Build, run, and manage complex data pipelines at scale with workflow orchestration

CIOs and business unit leaders are on a critical mission to find ways to use insightbased analytics to support business transformation and create competitive advantage. Some version of that mission is already in place at many enterprises, and the executive pressure behind it is strong. As companies look to rapidly harness the power of artificial intelligence and machine learning (AI/ML), data architects and engineers have a vast and growing ecosystem of tools to choose from, adopt, and operationalize to deliver on those modern data initiatives. A good representation of this ecosystem is available in FirstMark’s 2023 MAD (Machine Learning, Artificial Intelligence & Data) Landscape report. The industry response to this challenge has been to adopt new operational models such as DataOps and MLOps. In fact, the data community has started The DataOps Manifesto with guidance on several principles. One of those, which we will focus on here, is orchestration, a crucial building block in bringing together many moving parts of a data pipeline.

The DataOps Manifesto defines orchestrations as: “The beginning-to-end orchestration of data, tools, code, [and] environments.“ Control-M from BMC is the orchestration and operationalization platform that enables end-to-end orchestration of data pipelines and predictive service level agreements (SLAs) for any data technology or infrastructure across four key data stages: ingestion, storage, processing, and analytics. This whitepaper will delve into the orchestration and operationalization challenges faced by modern data teams and how Control-M addresses them.