InQuery - The fully managed data lake house

InQuery provides performance tuning, monitoring, and management for your lake house's most demanding data workloads.

TLDR: InQuery is a Snowflake alternative built on open-source technologies, like Trino and Iceberg, that makes it easy for companies to build large-scale data platforms for a fraction of the cost. With InQuery, you can process PB scale data at 50% of the cost of traditional cloud data warehouses.

Hey everyone, we’re Erick and Khalil and we’re building InQuery.

🔍 Problem
Large companies often need to store, process, and analyze anywhere from 10s of TB to multiple PB of data. At this scale, the cost of storing and processing data can easily balloon to multiple millions of dollars per year.

The largest companies have traditionally solved this problem by building their own data “lake houses” powered by a zoo of open-source technologies, but this is a herculean undertaking that exposes your business to setup, integration, and management challenges at each layer of the stack.

🦾 Our Solution

InQuery helps you build your data lake house by simplifying your platform’s operational challenges. We make it easy to set up and optimize your data lakehouse through:

  • Automatic resource and capacity management
  • Recommendations for optimized SQL
  • Table metadata management
  • Workload prediction and intelligent query routing

At InQuery, our goal is to give you the effortless feel of a fully managed data platform using tools that scale with your business and protect you from vendor lock-in.

👋 About us:
Before starting InQuery, we’ve been best friends since living in the same dorm during our freshman year studying Computer Science at Stanford.

Our Ask
If your organization is processing > 20TB of data, we’d love to chat!

Even if our product isn’t the right fit for you yet, we’d love to learn about what’s working well and what’s broken at your organization and share what we’ve learned from talking to 60+ data platform engineers across a range of large businesses.