Case Studies
Trusted by leading teams
Learn how our customers transform legacy systems, cut costs and empower their teams to achieve data democracy.
Grocery sensation Erewhon turns cultural buzz into business growth
A solo data team upskills into more advanced data engineering and finds a robust, reliable solution to data ingestion, building an “enterprise-grade” data operation.
Remerge's journey from manual processes to streamlined pipelines
Learn how Remerge moved away from manual spreadsheets by centralizing their data, creating a reliable single source of truth.
Artsy moves data faster
Artsy transforms their 10-year-old legacy system into a streamlined, customizable solution, dramatically reducing data extraction times.
Flatiron Health accelerates privacy-enhancing data processing
Learn how Flatiron Health cut 50% of their cost of ingestion and transformation pipelines using dlt (data load tool).
How insurance company Dentolo democratizes data access
Dentolo transforms its data ingestion process, empowers the team with a composable data stack and democratizes data access across the organization.
PostHog offers their users a scalable and inexpensive one-click data warehouse
PostHog builds a scalable, customizable data warehouse that seamlessly handles large datasets, and empowers their team to deliver a flexible and high-performing solution for users.
How Harness transformed 14 data pipelines in 14 days
Harness chooses dlt (data load tool) + sqlmesh to create an end-to-end next generation data platform.
Fintech Taktile builds a compliant data platform
How Taktile uses dlt (data load tool) + Snowflake for custom data needs and empowers all software engineers.
The current machine learning revolution has been enabled by the Cambrian explosion of Python open-source tools that have become so accessible that a wide range of practitioners can use them. As a simple-to-use Python library, dlt is the first tool that this new wave of people can use. By leveraging this library, we can extend the machine learning revolution into enterprise data.
