With data management increasingly moving to object storage and cloud data warehouses, organizations naturally expect that the BI applications also benefit from the scale of data and real-time analytics. However, traditional BI architecture in this cloud-native environment surfaces not-so-obvious challenges: While object storage scales for you to store large amounts of data, the analytics can only be done on subsets. While the data can be ingested in real time, analytics has to wait for data warehouse batch ETL cycles before it can be used. It’s time to invest in an Agile, cloud-native architecture for BI that doesn’t rely on outdated analytic patterns of traditional BI.
Priyank Patel explains how the application of traditional BI to cloud-native data management and warehousing can result in a “frankenstack”—which many users have tried to simplify in the first place by moving to the cloud. Priyank then reviews service-oriented cloud design (storage, compute, catalog, security, SQL service) and outlines why BI should be a native service in this environment.
This session is sponsored by Arcadia Data.
Priyank Patel is the cofounder and chief product officer at Arcadia Data, where he leads the team’s charter in building visually beautiful and highly scalable analytical products and guides customers through their successful adoption. Previously, Priyank was part of the founding engineering team at Aster Data, where he designed core components of the Aster Database. He holds a master’s degree in computer science from Stanford University.
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