Tower and dltHub
Tower is a hassle-free platform for the next generation of Python-based data and AI apps. You can run any Python code on Tower, including dlt pipelines and dlt+ projects. dltHub provides a modular framework for running extraction, loading and transformation tasks in production.
When running dlt tasks on Tower, Tower is taking care of infrastructure, orchestration, observability, alerting and security. It is also deeply integrated with dlt’s environments, secrets, pipelines and projects.
Data platforms are shifting from 15-year-old distributed technologies to new architectural principles that benefit users through modularity and freedom of choice. dltHub's dlt+ modular framework for running extraction, loading, and transformation tasks in production is taking off fast. We are delighted that Tower is deeply integrating its runner with dlt+ and offering a serverless, fully managed environment.
Co-Founder & CEO, dltHub
Who is it for?
Businesses in industries where data processing is a critical component of their product, e.g. AI-based services, cybersecurity, financial services. Typical customers have a large number of data pipelines for ETL or Batch Inference and use a Python-oriented stack including dltHub and DataFrame libraries such as Polars, Apache DataFusion etc.
Case study
Taktile is the leading decision intelligence platform for FinTechs around the world. Taktile’s data team operates a Snowflake data warehouse and onboards data from a large number of data sources using the dlt library from dltHub.
Taktile's data team constantly gets requests to onboard new data sources. To make it easy for everyone across the org to contribute to the creation of high-quality data sets, they employed dltHub and Tower. Taktile went from a team of 2 data engineers capable of making changes to their data platform to pretty much everyone who knows a bit of Python. Taktile used dltHub for data extraction, loading and transformation, and Tower to simplify team collaboration and create environments with shared secrets and dependencies. Tower also served as a portable Python runtime that enabled smooth transition from local development to cloud testing and production.
