ELT isn’t Just Another Buzzword
For decades, mainframe data tasks have regularly included ETL – extract, transform and load – as a key step on the road to insights. Indeed, ETL has been the standard process for copying data from any given source into a destination application or system. ETL got a lot of visibility with the rise in data warehouse operations but was often a bottleneck in those same data warehouse projects.
Today, ETL is still the default choice for data movement, especially in the mainframe. But there is a legitimate alternative – ELT – extract, load, and transform.
As the reshuffling of terms implies, ELT takes a much different approach, first extracting data from wherever it currently resides and then loading it, generally to a target outside of the mainframe. It is there, wherever that “there” is, that the hard work of transform happens, typically as a prelude to the application of analytics.
So, ELT is an acronym, but one that’s pretty revolutionary.
Why? By reframing the idea of ETL with the technologies of today, the entire process has the potential to be faster, easier, and less expensive because it can use the most appropriate and cost-effective resources. Not just the mainframe CPU.
ELT tends to require less maintenance than ETL, which typically has many requirements for manual, ad hoc intervention and management. In contrast, ELT is based on automated, cloud-based processing. Similarly, ELT loads more quickly, since transformation is closely linked to the ultimate cloud-based analysis work. ELT, then, is primarily concerned with getting data from mainframe to the cloud. Finally, of course, it is usually faster overall. And, because it depends primarily on pay-as-you-go cloud resources rather than on the billing structure of the mainframe, it is generally less expensive.
ELT empowers the routine and regular movement of mainframe operational and archived data from expensive and slow tape and VTL to storage environments that are both fast and highly cost-effective, such as AWS S3 Tiered Storage. ELT can also deliver data directly for transformation to standard formats in the cloud – and then make that data available to data lakes and other modern BI and analytics tools. Because ELT retains its original format and structure, the options for how the data can be used (transformed) in the cloud are practically unlimited.
The key to ELT on the mainframe is, of course, zIIP engines, the helpful processing capability provided by IBM for handling exactly this kind of `non-critical’ activity. It’s just that no one tried before.
With zIIP help and TCP/IP to assist in movement, buried data sets can be liberated from mainframe data silos and deliver real monetary value. What’s more, companies that have tried ELT have discovered how easy it is to move mainframe data. They can more easily take advantage of cloud storage economics –potentially eliminating bulky and expensive tape and VTL assets.For these many good reasons, ELT is `NJAA,’ not just another acronym – it’s an acronym worth getting to know.