The year 2020 has been extremely eventful on many levels. Timelines for digital transformation—supported by data analytics—suddenly had to accelerate from 5-year horizons to overnight implementations. Expect more of this continuing velocity in the year ahead, as companies fast-track their plans for initiatives ranging from AI to edge computing. These all require enormous volumes of quality data, meaning data managers will be quite busy in the months ahead. DBTA spoke to leaders across the industry to gain their perspective on what to expect.
For starters, the year ahead will likely see a convergence between databases and machine learning systems, predicted Monte Zweben, CEO of Splice Machine. This confluence is more than a technical consideration. “This will enable the business to deploy and continuously improve machine learning applications 100x faster with half the staff.”
Therefore, data and AI teams need to collaborate, with the shared goal of “simplifying the machine learning architecture and removing the latency caused by data movement between compute engines,” he continued. “Business and IT leaders can prepare for this immediately by cataloging what applications could benefit from machine learning—both new and old. Then they start the modernization journey of their old applications by migrating them to scale-out SQL platforms. Once on scale-out, they now have the foundation to store the volume of data needed for machine learning and they can build models and deploy them easily on the converged architecture. Nothing is better than trying it—for example, on cloud.”
More on the Edge
The growing presence of AI and machine learning “within traditional data environments will take on a large role in processing data and analytics at the edge,” agreed Irshad Raihan, director of cloud storage and data services at Red Hat. It has even been suggested that 75% of enterprise-generated data will be processed at the edge by 2025, turning this trend into a reality within only a short 5-year period. “But at this time, companies are taking their first steps in developing and/or implementing AI and machine-learning capabilities to harness deeper and real-time collaboration and processing across teams,” Raihan said.
Merging Open Source and Cloud
The coming year’s leading solutions will consist of a blend of the best of open source systems with cloud-based solutions. “We are seeing continued migrations into open source relational database solutions, non-relational database solutions, PaaS-based database solutions, and a combination thereof,” said Marc Caruso, chief architect for Syntax. The embrace of these solutions is being driven by the need to “reduce operating costs, whether they are being undertaken to reduce hefty support contracts from vendors, reduce headcount expense, or gain performance efficiencies by migrating to a more purpose-built database solution.” Data migration to cloud and open source platforms “is happening right now and at a large scale,” he added. “Migrations to PaaS solutions are being driven by the hyperscalers, with Oracle, AWS, and Microsoft all vying for market share.”