November 10, 2022
High-performance data engineering pipelines are the backbone of modern organizations. These infrastructures extract data from transactional applications and other sources, transform it for consumption by downstream applications, and deliver it to a vast range of increasingly cloud-native computing environments.
As enterprises modernize their data engineering pipelines, they seek agile programming and runtime environments to support an ever-expanding range of cloud-native applications. You can make your enterprise data pipeline more agile, for example, by unifying streaming and historical data workloads, converging SQL and non-SQL data processing, hybridizing transactional and analytics applications, or developing coordinated cloud-native data applications.
This TDWI Checklist discusses best practices for building agile pipelines to accelerate development of artificial intelligence, machine learning, predictive analytics, and other data applications.