Parallel Processing 2.0: Round Two, Ready Fight!

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Vectorized CPUs have been gestating for decades. Now they are emerging as the innovator’s choice as they use parallel CPU instructions when working on a single user’s workload.

Gartner, IDC, and others say analytics is the #1 or #2 priority for large and small corporations.  Underscoring all analytics is the need to cope with big data — 100s or 1000s of terabytes of storage. Why? Because more data means more accuracy and discovering hidden insights.  Some data warehouses exceed 50 petabytes; data lakes can exceed 200PB of storage.  But big data storage is only half the story. Big data also means horrible wait times for results. Anyone can attach 100 terabytes to a laptop. Analyzing 100 terabytes on a laptop could take weeks or months, which explains investments in massively parallel processing.

Massively parallel processin technology is the foundation of data warehouses, data lakes, NoSQL, data science, and a lot of artificial intelligence. MPP + analytics is a $67B worldwide market with 10% growth. Getting results fast means speeding up the time-to-value. And less boredom waiting for answers. It also means delivering results to managers and peers sooner.

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About Dan Graham and Chad Meley

With over 30 years in IT, Dan Graham has been a DBA, IBM’s Global BI Solutions Strategy Director, and General Manager of Teradata’s 6700 high-end servers.  His skills include MPP systems, data warehouses, big data, data lakes, graph analytics, benchmarking, and IoT. Dan is currently an independent consultant. Chad Meley is the CMO at Kinetica, the database for time and space. He has more than 20 years of experience in leadership roles centered around data and analytics marketing and information technology systems and encompassing all facets of management, strategy, planning, and operations for companies such as Teradata, Electronic Arts, Dell, and FedEx.

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