This problem existed 15 years ago, when the volume and variety of data flowing through ETL tools into on-prem data warehouses was smaller. The scope of the problem today is expanded, as enterprises ...

The ETL/data engineering bottleneck can be broken when you are able to increase your ability to achieve the following goals: Increase self-service: ...

ETL is hard, and building pipelines laborious, so avoid building bridges to places that no business inquiry will ever visit. 5. Avoid ETL Where You Can. While for some organizational processes there’s ...

Drori decided the problem of traceability in multi-source ETL processes was great enough that he co-founded a company called Octopai to address it. The Israeli company’s eponymous product gathers all ...

Choosing the right data processing approach is crucial for any organization aiming to derive maximum value from its data. The debate between Extract, Transform, Load (ETL) and Extract, Load ...

Nam holds a BS in computer science from the University of Pittsburgh and an MS in information security from Carnegie Mellon University.

Many enterprises, vendors, and startups often confuse the role of data scientist and data engineers. While the overlap of these roles is substantial they’re not particularly interchangeable.

It demands lengthy re-engineering cycles by dedicated data engineers. In ETL, data scientists receive the data sets only after they are transformed and refined by the engineers.