It’s never been easier to collect data and drop it in a database so you can analyze it. But what about getting to insights? Data visualization and reporting are hard - we're trying to make it easier.
Timing will never be perfect for introducing a new tool to your stack. If you're considering the addition of a data viz tool, this post will provide guidance on identifying key signs that indicate it's probably time to take the plunge.
My journey of how I've ended up embracing denormalized tables, how to use tools like dbt to bridge normalized and denormalized tables, and how denormalized data accelerates exploratory data analysis and self service analytics.
It comes in as a simple request: “we want custom colors for our charts in Glean”. It seems simple, but doing it well is more nuanced than you may expect.
It’s surprisingly common to not have any version control for BI assets. If you don’t have it, it’s a good idea to get it in place sooner rather than later. This post covers the basics of how to get started.
Big Data is cool - and so are infinitely scalable Data Warehouses. But sometimes, you just have not-so-big data. Or maybe you have a csv file and you don’t even know what a Data Warehouse is, or don’t care about the “Modern Data Stack” - like, what is that? That shouldn’t stop you from exploring your data in an intuitive, visual way in Glean.
We’re thrilled to announce the launch of our public Changelog and Product Roadmap! These two resources will provide an updated overview of all product changes, as well as a preview of items our team will be tackling in the future.
Sometimes you've got some data in postgres and you just want to start analyzing it. Here's the scrappy guide to get you jamming on analytics - a guide for founders, PM’s, engineers trying to hack something together quickly.
Allowing users to explore and experiment with your data is crucial for building a data-driven culture. But when a report has become a production system – a real product with real users that depend on it – it’s important to start treating it more like an application. DataOps lets you treat them that way.