Five Reasons Why ELT Is the Best Way to Move Mainframe Data to The Cloud

Even in an age of almost continual technological evolution, change can be difficult. In most cases, change has rewards that make it worthwhile.

That's exactly where many IT operations stand with regard to Extract, Load, and Transform (ELT), the reimagined way of moving and using data. In comparison with ETL, the "classic" solution for moving large collections of data, ELT is often faster and, because it usually takes advantage of rapidly scalable cloud computing, can be particularly cost-effective with regard to delivering business results.

Streamline

ELT streamlines the data handling process, according to Gigaom analyst Andrew Brust. In a 2020 blog, he explained some of the advantages of ELT. Brust highlighted three in particular: because data doesn't have to be prepared or "massaged" first, it can be raw and disorderly, and you can determine how to process it and how much of it to process at a later point.

Reduce risk

ELT reduces risk because transformations can be tried and tested on subsets of the data or different kinds of transformations can be tried to arrive at the "best" way. Brust also noted that potentially, the result could be more effective data pipelines. "You should also see higher levels of cost efficiency," he adds.

Unify core data

"Rather than building multiple small bridges between data islands, [with ELT] you are creating a single data 'mainland' as a basis for data transformation at scale." Brust also notes, "BI and analytics are not standing still…The key to this evolution will be how we can manage data both flexibly and at a massive scale, minimizing any bottlenecks to data delivery."

Prepare for ad hoc queries

Picking up that thread, blogger Bjorn Cornelis at Biztory, a European Tableau consulting company, says the very nature of data and analytics has evolved over the past decade to favor ELT.

He explains, "Data is no longer used to feed predefined reports but is consumed by a variety of tools, technologies, and users. Scorecards, dashboards, metrics, self-service analytics, predictive analytics, and so on… they all want a piece of your data, and they all want it in their own unpredictable way." That requirement for ad hoc and changeable data movement and data analysis is the perfect fit for ELT.

Leverage your zIIP engines

For mainframe organizations, the added plus of ELT is that zIIP engines can handle the extract function with little or no impact on the performance of other tasks and next to none on MSUs. It's a compelling advantage that not only opens the door to rich, rapid, and responsive cloud-based analytics but can also free your organization from dependence on expensive, slow, on-premises archival and backup storage costs.

Conclusion

Where ETL was formal, cumbersome, labor-intensive, and slow, ELT is almost the opposite. That's why ELT needs to be part of IT thinking. It's a great way to get agile, to get more use from your existing data, and to pave the way for any kind of modernization initiative – it is a necessity for getting mainframe data to the cloud.

Author

Gil Peleg

Gil Peleg, Founder, and CEO of Model9 have more than two decades of hands-on experience in mainframe system programming and data management, as well as a deep understanding of methods of operation, components, and diagnostic tools. He is a co-author of eight IBM Redbooks on z/OS Implementation. He holds a B.Sc. in Computer Science and Mathematics. www.model9.io