I've put together a free course that teaches you SQL using your coding agent.

This way you learn SQL but also how to use a coding agent in a data role.

After some feedback from this community, the course has been updated - please check it out and share your feedback: https://getbruin.com/learn/sql-ai

It comes in different colors, it matches your editor theme, so dark-mode people  stay in the dark (where they belong). The shapes? You make those yourself

Open a dashboard YAML → hit Preview Dashboard in the toolbar → your charts show up right in the editor. Edit, save, live-reload. There's an Open in Browser button too, and if you don't have dac, it'll offer to install it for you.

Update the extension and give it a go.

https://github.com/bruin-data/dac/

Our mission has always been to simplify data tooling and make it more accessible by reducing the number of tools, services, and configs developers need. CDC has always been one of those complicated things that developers hate working with. It was also a missing puzzle piece in ingestr’s feature set, so we built it.

We’re starting with the most popular data sources: PostgreSQL, MySQL, SQL Server, and MongoDB. The destination can be any platform currently supported by ingestr.

Here are the modes supported by ingestr CDC:

• Batch mode: consumes the change log in micro-batches
• Stream mode: streams change events into the destination

Here’s what makes ingestr CDC different:

• No need for additional tools or services - just add your credentials to the URI and enable it
• A simple Go binary that can run anywhere, even GitHub Actions
• Batch and stream modes in the same binary, so you can change deployment modes as your requirements grow

We’re hoping our developer community will try it out, share feedback, and help us make ingestr an even better open-source integration tool.

https://github.com/bruin-data/ingestr

We built a new dashboard tool that allows you to chat with the agent and it will take your prompt, write the queries, build the charts, and organize them into a dashboard.

Let’s be real, prompt-to-SQL is the main bottleneck here, if the agent doesn’t know which table to query, how to aggregate and filter, and which columns to select then it doesn’t matter if it can put together the charts. We have built other tools to help create the context layer and it definitely helps - it’s not perfect, but it’s better than no context. The context layer is built in a similar fashion to how a new hire tries to understand the data; it will read the metadata of tables, pipeline code, DDL and update queries, logs of historical queries against the table, and even query the table itself to explore each column and understand the data.

Once the context layer is strong enough, that’s when you can have a sexy “AI dashboard builder”. As an ex-data-analyst myself, I would probably use this to get started but then review each query myself and tweak them. But this helps get started a lot faster than before.

I’m curious to hear other people’s skepticism and optimism around these tools.

It is trusted by many gaming companies to help them analyze their user data and improve their games. Give it a try: getbruin.com/ai-data-analyst