What's new on Hex

The modern analytics platform for data teams

New
September 02, 2021

📝 Dynamic Markdown

One of the top request features has arrived: Dynamic Markdown! You can now use variables directly in markdown cells!

You may also notice that the formatting has gotten a glow up. Header fonts are bigger, there are more header options, and fonts are now consistent between Logic and App views.

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New
September 02, 2021

🛋️ Multi Select Cells

Sometime you just need to move a block of cells, duplicate them, or copy them to a different section of your project. We've added the ability to select multiple cells (shift + arrow) and adjust them as needed, all from the comfort of your keyboard!

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New
September 02, 2021

🔢 Cell Line Numbers

We know some of you have some meaty queries and code cells (definitely not looking at you, you thousand-line-query-writers!). We've added cell line numbers to make navigating those beasts a little easier.

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Fix
September 02, 2021

🐛 Bugs Squashed

  • When setting up a new BigQuery connection, schema fetching is now much faster and won't time out.
  • We fixed an issue with Button Input parameters that caused them to appear to be always running.
  • If you were very quick on the draw and managed to click "Restore" twice on the same historical Hex version, you could have gotten into a weird state. No more!
  • We've added a tooltip to display long project names on both the project homepage and in Logic view.
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New
August 31, 2021

💪 SOC2 Type II

Maybe not glamorous, but security is fundamental to how we are building Hex. We're excited to announce that we now have a SOC2 Type II report!

The tl;dr — we take security seriously, so much so that an external auditor agrees that our systems and processes are secure, and that we adhere to them in practice. Read more details on our blog.

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New
August 18, 2021

🚨 Dataframe SQL

Yep, we went there. You can now select "Dataframes" as a data source for SQL cells, and query any dataframe in a project like a table in a database. Queries are fast, in-memory, and use a well-featured flavor of PostgreSQL.

You can use Dataframe SQL to break up large queries into modules that are "chained" together, join tables across databases and CSV sources, or just pop back into SQL halfway through an analysis to do a simple filter or case statement. You can mix and match SQL and Python on the same data as many times as you'd like!

There's a lot to say about this one, so we wrote an entire blog post with the "why" behind the feature. Just in the mood to get started? Check out the docs for the full details and jump right in.

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