Topic: Data transformation
5 reasons why ETL is the wrong approach to moving mainframe data and why ELT is the ideal method.
Using the right AWS stack, data engineers, scientists, and decision makers can now easily unlock mainframe data sets to support enterprise data analytics pipelines that power modern initiatives such as machine learning, AI, and business intelligence.
In the 2021 Arcati Yearbook, Model9 shares how our customers know they need to do more to leverage their vast store of mainframe data, despite feeling squeezed by the costs and limitations of traditional storage options.
Until now, mainframe data has been locked in by legacy storage hardware and proprietary formats, and ETL processes have been unaffordably compute-intensive, making change all but impossible. But there is now another way, a technology path that speeds data to its destination for rapid transformation to any standard format, with practically no impact on mainframe processing. Now, critical data can inform analytics, shaping business results, and opening opportunities.
Eliminate dependence on cumbersome and slow mainframe methods with Model9’s powerful cloud-based data transformation.