You can now understand how your apps connect to form a larger pipeline using Flows Graph in Tower.

We finally put down Claude Code long enough to pick up Figma, and we are excited to announce that the Tower.dev website has officially been updated!

Some data workloads are too sensitive, costly, or require strict data sovereignty, so they can’t run in public clouds.

Imagine if data pipelines could detect new data as soon as it arrived, rather than checking for it on a fixed schedule.

Earlier in December, we gave two talks and shared the Tower story at the booths of two Python data events in Boston and Warsaw.

When we evaluated Iceberg REST catalog options, Lakekeeper immediately stood out.

Why you should choose a lakehouse for your business

Launching Tower Catalog, a REST catalog for Apache Iceberg tables that is fully managed by Tower.

It is 2026, and there are more ETL tools in the world than one person can mentally map.

In our initial release of Tower we offered data teams the ability to deploy and run batch job- or pipeline-like data applications.

We spent a week in San Francisco at the Small Data SF event. As Joe Reis quoted William Gibson, “the future is here, just not very equally distributed.”

Extract, Transform, and Load (ETL) is the process of extracting data from operational databases and loading it into a data warehouse or another database.

In an era where vibe-coded apps seem to fall apart at the slightest nudge, we wanted to make life easier for anyone looking to build robust apps

​Data Lakehouse vs Data Warehouse vs Data Lake: Key Differences

Not just another house on a lake

Data infrastructure is hard to get right

Isolated environments - or “don’t break prod by accident, ever”

Over the weekend, the data community woke up to surprising news: Fivetran is planning to buy dbt Labs.

Multiple schedules per app, centralized management, and more!

Accurate electricity consumption forecasting requires up-to-date and trustworthy weather data.

When data engineers need to figure out where to deploy their scripts, they often start with whatever is already at hand.

Every data engineer has war stories. Pipelines that silently failed for weeks. “Small” Spark jobs take forever.

Large Language Models (LLMs) are transforming the way developers build apps

If you’ve ever tried to integrate Large Language Model (LLM) inference into your application, you’ve likely run into some recurring headaches.

Every data team knows this story: Some team member wrote some Python script that moves some data.

Don’t choose between good tooling and good infra

We just added support for Multi-Factor Authentication (MFA) using one-time passwords (OTPs)!

We’re excited to announce support for data engineering AI Agents in Tower!

Imagine trusting an AI chatbot for legal research, only to be fined $5,000 and having your case dismissed because it fabricated case law.

We are very excited to launch support for Cloudflare R2 object storage and R2 Iceberg catalogs in Tower today.

Tower just added automatic app health monitoring and alerts.

Today, we are announcing support for orchestration in Tower apps.

Today, we are announcing support for Iceberg catalogs inside Tower and releasing the Tower SDK

A few weeks ago, we demoed Tower.dev at the AI Launchpad stage of the Data Council.

We attended seven user presentations at the 2025 Iceberg Summit and summarized our takeaways, so you don't have to.

In this post we will show how Tower makes it possible to develop apps that use local GPUs for inference

We at Tower are big fans of Python as the language of data and AI.

We at Tower believe that the future will see a shift in how data engineers and their internal data customers collaborate. 

You've most likely come across the news that major tech companies have stopped hiring developers and that major-league CEOs believe that AI will replace engineers starting right about this year.

A growing number of data teams are bringing (post-)modern Python frameworks such as Polars, DataFusion, and dltHub to production.

This is the final post of our series "Building an Open, Multi-Engine Data Lakehouse with Python and S3"

We are continuing our series of posts exploring building an open, multi-engine lakehouse based on Apache Iceberg

In this post, we will do something critical for building a lakehouse. We will create storage buckets in S3!

To use Snowflake as an Iceberg catalog or Not to use: that is the question.

How many cacti does it take to make an Iceberg?

Pythons just wanna have fun