Table of Contents

    ▶
  1. Data transformation
    2. ▶
  2. Data testing
    3. ▶
  3. Implementations + deployment
    4. ▶
  4. Documentation + metadata
    5. ▶
  5. The modern data stack
    6. ▶
  6. Data dream teams
Coalesce 2020 The modern data stack The future of the data warehouse

The future of the data warehouse

Arjun Narayan, Boris Jabes, Jennifer Li, and Jeremy Levy

Arjun was previously an engineer at Cockroach Labs. He holds a Ph.D. in Computer Science from the University of Pennsylvania.

Before founding Census, which helps data teams sync customer data and insights to external systems to drive business operations workflows, Boris was the CEO of Meldium, a password manager for teams. He loves building tools.

Jennifer is a partner at Andreessen Horowitz, focused on enterprise investments in data infrastructure and analytics, open source, developer tools, and collaboration applications.

Jeremy co-founded and heads Indicative, a customer analytics platform for product and marketing teams. He is a serial entrepreneur and a veteran of New York City’s Silicon Alley. Jeremy also co-founded Xtify, the first Mobile CRM for the Enterprise, acquired by IBM in 2013, and MeetMoi, a pioneering location-based dating service for mobile sold to Match.com in 2014.

Almost all of us are using our [data warehouse](https://docs.getdbt.com/terms/data-warehouse) to power our business intelligence. But what if we could use data warehouses do even more — to power internal tooling, machine learning, behavioral analytics, or even customer-facing products? Is this a future we're heading for, and if so, how do we get there?

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