Case Studies
Learn how our customers benefit from dlt
How grocery sensation Erewhon turned cultural buzz into business growth with dlt
A solo data team upskilling into more advanced data engineering used dlt and found a robust, reliable solution to data ingestion and built an “enterprise-grade” data operation.
Remerge's journey from manual processes to streamlined dlt pipelines
Learn how Remerge moved away from manual spreadsheets by centralizing their data with dlt, creating a reliable single source of truth.
Artsy moves data faster with dlt
Artsy transforms their 10-year-old legacy system into a streamlined, customizable solution with dlt, dramatically reducing data extraction times.
How Flatiron Health used dlt to accelerate privacy-enhancing data processing
Learn how Flatiron Health cut 50% of their cost of ingestion and transformation pipelines using dlt.
How insurance company Dentolo democratizes data access using dlt
How Dentolo transformed its data ingestion process by adopting dlt empowering the team with a composable data stack and democratizing data access across the organization.
PostHog leverages dlt to offer their users a scalable and inexpensive one-click data warehouse
How PostHog adopted dlt to build a scalable, customizable data warehouse that seamlessly handles large datasets, empowering their team to deliver a flexible and high-performing solution for users.
How Harness transformed 14 data pipelines in 14 days
How Harness chooses dlt + sqlmesh to create an end-to-end next generation data platform.
Fintech Taktile builds a compliant data platform with Snowflake + dlt
How Taktile uses dlt + 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.
