May 26, 2022
Success in the modern economy depends on an enterprise’s ability to deliver high-quality data analytics into production applications.
Structured data engineering processes ensure that data and analytics are always accurate, relevant, and fit for purpose. Modern data engineering processes—also known as DataOps pipelines—continuously integrate, transform, and prepare data for production deployment.
In order to keep pace with fast-changing business requirements, enterprises need to address key technical challenges such as migrating DataOps pipelines to modern cloud infrastructures, optimizing DataOps processes for both low-latency and batch processing, and scaling DataOps pipelines elastically to support mission-critical workloads.
This TDWI Checklist provides DataOps practitioners and other enterprise stakeholders with six best practices for addressing challenges successfully within a cloud-focused modernization program.