November 7, 2022
Our guest today is Andrew Churchill from Qlik. He provides the audience with some solutions to the frustrations of managing in the world of hybrid clouds.
Ten years ago, it was a major accomplishment to move an application from a federal on-premises server to the cloud. Fast forward to 2022.
There is so much data flooding into a wide variety of clouds used by the federal government that the term “terabyte” is tossed around like a stickie note. Updated terms are being generated to make sense of the large data stores: phrases like “data lakes” and “data warehouses” are being coined to give some structure to the fire hose of zeros and ones.
During this increase, large secure systems are being tasked with a transition to the cloud – while preventing attacks and absorbing data twenty-four hours a day.
Federal leaders have seen the problem and have attempted to generate ways to understand data being presented from networks, storage arrays, servers, and much more. At this level, automation is the only way to be able to present information in a manner where someone can make a decision. At one level, there is a challenge to get observability of federal systems. Taken to the next level, can we harness data to be able to make predictive decisions?
Today’s interview gives a view of how technology companies can combine to help understand how the complex federal cloud can be managed. Rather than one ring to rule them all, he suggests assembling a team of disparate skills to be able to take advantage of unique skills.
Andres gives an overview of this approach in the interview. He touches on automation and real-time operational intelligence. If you want a deeper dive, he suggests you attend an event called “Cloud Modernization with AWS and Qlik.” It will take place near the Rosslyn Metro on November 14, 2022. It will have subject matter experts with serious federal experience teamed with data scientists and analysts.
If you enjoyed this episode, you may want to listen to episode #26 How Federal IT can Leverage Artificial intelligence
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