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Data Management in 2022 - ML → DB, Data Meshes, and Data Lakes

By Anil Inamdar, the VP & Head of Data Solutions at Instaclustr

Get ready for a big year in data management. 2022 is going to be about machine learning driving database indexing and analytics, the expanding tentacles of data lakes and the proliferation of the "data mesh."

Here are three data management predictions heading into the new year:

Database management will find new ML-powered paths to optimization

Machine learning and predictive analytics are coming to database management, helping enterprises burst through traditional limitations set by inflexible data design and data usage trends humans can't foresee. Database admins, once saddled with the unenviable task of producing optimized and performant queries based on imperfect knowledge, will get welcome relief from ML solutions that can intuit where data resides using reliably predictive models. This capability will go further, with ML creating entirely optimized data indexes and automatically handling reindexing and storage management. Whereas AIOps (similarly ML-powered solutions for operations and predictive maintenance) shows some signs of sputtering as a much-anticipated technology, predictive database management should find the brighter destiny as a crucial component of any database operations strategy once its training sets are appropriately refined.

The landscape around data lakes will see sprawling growth

Data lake adoption shows no signs of slowing, and that growth will further contribute to the vibrant ecosystem of data integration solutions popping up around this dominant modern data storage technology. These lakefront properties (if you will) will flourish as enterprises seek to ensure that data lakes can harness and provide benefits based on the entirety of their data. Specifically, technologies like open source Apache Kafka and Pulsar will enable organizations to integrate data from third-party solutions and production workloads featuring real-time transactions. For services that call for active data awareness, options such as Debezium and Kafka Connect will facilitate that necessary data lake connectivity.

Data mesh will hand data management responsibilities to its closest users

"Distributed" isn't just for architecture anymore. In 2022, responsibility for data itself will increasingly become decentralized and distributed to the individual teams that understand it best. This "data mesh" approach enables benefits such as self-service data access and more efficient data management from an organizational perspective. Vendor products already tout the term in sales materials, so expect data mesh to soon become ubiquitous - both as a term and, more importantly, as a practical data control strategy.

No doubt about it, 2022 will be an exciting one for data management. And these three predictions don't even take into account the 2021 trends that I expect will continue - such as open source data technologies gaining enterprise adoption against more restrictive open core solutions. 

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ABOUT THE AUTHOR

Anil Inamdar is the VP & Head of Data Solutions at Instaclustr, which operates and supports customers' data infrastructure using open source technologies. Anil has 20+ years of experience in data and analytics roles. Joining Instaclustr in 2019, he works with organizations to drive successful data-centric digital transformations via the right cultural, operational, architectural, and technological roadmaps. Before Instaclustr, he held data & analytics leadership roles at Dell EMC, Accenture, and Visa. Anil lives and works in the Bay Area.