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DataOps, which focuses on automated tools throughout the ETL development cycle, responds to a huge challenge for data integration and ETL projects in general. ETL projects are increasingly based on agile processes and automated testing. Click to learn more about author Wayne Yaddow. The […].
DataOps, which focuses on automated tools throughout the ETL development cycle, responds to a huge challenge for data integration and ETL projects in general. ETL projects are increasingly based on agile processes and automated testing. Click to learn more about author Wayne Yaddow. The […].
They must put high-quality data into the hands of users as efficiently as possible. DataOps has emerged as an exciting solution. As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT.
This adaptability allows organizations to align their data integration efforts with distinct operational needs, enabling them to maximize the value of their data across diverse applications and workflows. Organizations must support quality enhancement across structured, semistructured and unstructured data alike.
However, the race to the cloud has also created challenges for data users everywhere, including: Cloud migration is expensive, migrating sensitive data is risky, and navigating between on-prem sources is often confusing for users. To build effective datapipelines, they need context (or metadata) on every source.
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