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.