For example, a company might be looking to pull raw data from a database or CRM system and move it to a data lake or data warehouse for predictive analytics. ... Data pipeline vs. ETL pipeline.

Building effective data pipelines is critical for organizations seeking to transform raw research data into actionable ...

Apache Airflow is a great data pipeline as code, but having most of its contributors work for Astronomer is another example of a problem with open source. Topics Spotlight: AI-ready data centers ...

Who needs rewrites? This metadata-powered architecture fuses AI and ETL so smoothly, it turns pipelines into self-evolving ...

For example, one CIO has a team of 500 data engineers managing over 15,000 extract, transform, and load (ETL) jobs that are responsible for acquiring, moving, aggregating, standardizing, and ...

Today, at the Current conference, the company is introducing a new tool called Stream Designer to make it easier to build a streaming data pipeline in a visual workflow.

Finance and data analysis teams can use the pipeline to reconcile payment data faster since all of the data is consolidated in a single place. Security and fraud teams can combine a delivery ...

Through his work, Muvva has achieved several significant milestones. Notably, he was able to optimize the data processing pipeline, reducing processing time by 40% and cutting AWS costs by 25%.