Inflow supports customers with sensitive data workloads that must remain on-premises, leveraging a diverse ecosystem of data tools, including Sling Data, dbt, Pentaho, and others, to choose the best fit for each use case. Tower’s self-hosted runners allowed us to consolidate a subset of our customers into a single managed environment, providing a true single pane of glass for observability and a simple, reliable way to deploy and update pipelines.

Docker for data? If you go to a docker conference you will hear "portability", "fast onboarding", "lower cognitive load", "implicit access", "microservices", "decentralisation". Who does this for data? Check out dlthub.com/blog/tower

I have long suffered at the hands of both the Docker and Python ecosystems. Tower solves both. Plus, it plays nicely with scheduling, orchestration, and open table formats! If you're working with Python, you should try it.

dltHub and Tower partner on delivering data movement and transformation. Tower’s data compute infrastructure is tightly integrated with dltHub’s pipelines and notebooks. Users get an end-to-end solution and don’t have to waste time on integrations.

In the age of ChatGPT, it’s no longer the lack of Python or SQL knowledge that’s holding people back from working with data, it’s the sheer pain of setting up local environments for data access. Tower’s customizable and portable Python runtime is a true game changer for data driven organizations. You can stop fixing secrets and dependencies and start creating value from data in no time.

Migrating to Tower didn’t just streamline our infrastructure — it gave us a competitive edge. With lower costs, faster pipelines, and higher forecast accuracy, a-Gnostics can deliver even more value to customers in energy markets where every percentage point matters.