Bigtable has been a core piece of application infrastructure for Google and companies such as Snap, Spotify, and many other massive platforms for over 20 years. In this session, we’ll discuss the fundamental changes to Bigtable processing capabilities made available via SQL that will let you bring more data transformations directly into Bigtable – enabling extract, load, and transform (ELT) capabilities taking advantage of Bigtable’s flexible schema to achieve increased data freshness – and that will reduce the time and costs of running other data processing services to prepare data for your real-time application.

When your data outgrows the confines of traditional relational models, Google Cloud's databases offer the solution. This session will explore how to harness the power of distributed architectures and flexible schemas to handle massive datasets. We'll cover real-world use cases where unique databases like Spanner and nonrelational databases, including Bigtable, Firestore, and Memorystore, provide unmatched scalability and performance. In addition, Shopify will discuss how it’s using Bigtable for business critical use cases.

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