AWS Startups Blog

Analytics is Simpler Than You Think with Metabase

Guest post by Sameer Al-Sakran, CEO, Metabase

Metabase AWS use caseIf you spend any time reading on the internet, you’ve probably noticed that the subjects of analytics and business intelligence tend to take on very complex overtones. Big data analytics. Stream processing. Real-time analytics. Machine learning. Machine learning to learn your machine learning parameters. Meanwhile, everyone else’s engineering blogs are giving you all the details about how they burned through a couple hundred grand or more in engineering time to set up some view counters on their home page. But sometimes you just want to pull some data out of a database. Maybe you want to let others in your company do so as well. Maybe you even want them to be able to make a chart and pass it around. For all the talk of “company transformation,” expensive conference booths, and six- or seven-figure sticker shock, at its heart analytics can be fairly simple and incremental.

Enter Metabase.

What exactly is Metabase?

Metabase is the simplest way to get data in front of anyone on your team. Using a simple graphical interface, anyone in your company can create dashboards, set up nightly emails, or ask simple questions on their own.

Metabase dashboard

For questions that are not quite as simple, analysts and engineers can run SQL questions as well.

Metabase dashboard

After a question has been added to Metabase, anyone with access can easily re-run it, tweak it, and share it.

Metabase is open source, installs in minutes, and works with a wide variety of databases including MySQL, PostgreSQL, and Amazon Redshift. You can run it in your own AWS account. No one else needs to see your data, and you can lock things down to your heart’s content.

What kinds of questions?

If you’ve been involved with analytics, you’ve probably jumped 10 steps ahead and are thinking about all the complicated things that need to get measured. We’re not talking about multichannel attribution, inventory forecasting, demand curve generation, or fine-grained Life Time Value or Cost of Acquiring a Customer (at least not yet).

Instead, we want to draw attention to what can be called the Dark Matter of analytics. These are the questions that typically make up 90% of the query volume at most companies that no one ever brings up in a blog post:

  • Which blog posts got the most views?
  • How many reservations do we have for next Tuesday?
  • Which accounts are up for renewal next month?
  • Did John Doe have any credit card chargebacks when he opened this support case?
  • How many 5-star reviews does the average merchant have on our site?

While some subset of these tend to be built into your product, most aren’t. For all the glamour that top-down KPIs and data science get, it is the answers to these micro-questions throughout the day that make everyone in your company better informed, more aware of context, and more efficient in their day-to-day jobs.

Why now, can’t you see I’m busy?

Even before you think you need a Business Intelligence application or a data science stack, there are lots of little places where widespread access to data helps.

As you build your application, it is useful to have a place to allow restricted access to the databases in your application.

You can double check staging environment data, perform quick checks on production databases to verify that things look right, and create quick and dirty dashboards to see how things are working or not working.

As you find bugs or data that looks off, you can create canary cards. These let you inspect and verify data that seems questionable. For example, “Is the new invite code system properly marking invite codes as used?”

As you release new versions, you might create dashboards that measure activity in those features. Rather than guessing, or hoping that some of your core metrics move, you can easily instrument new changes. For example, “How many invite codes got redeemed within 24 hours of being sent?”

During launch, Metabase lets you flexibly define and redefine metrics, reports, and dashboards. By providing a simple interface on top of your data, it allows anyone to modify reports and keep tabs on the data they need without having to route it through engineers or analysts.

Metabase ease of use interface

At some point, you’ll start solidifying the things you measure (and if you’re a certain kind of person, you might even call them KPIs). The dashboards that each team needs will have taken shape, and it becomes time to prune the reports that aren’t useful any more. You’ll have built up a BI and reporting system incrementally, and in response to actual day-to-day questions. If you were gifted with a specific kind of constructive laziness, you might have even laid down the groundwork (such as SQL views and ETL) to let your end users create their own, and leave you to more interesting work.

Out of the box, Metabase lets your coworkers run simple queries on their own. As your needs grow, you can annotate and customize the data model that we generate, and create metrics, segments, and SQL views to capture exactly the things that you want to measure.

How can you try Metabase?

We recommend trying it out on AWS Elastic Beanstalk. To get started, go to and choose Launch Metabase on AWS.

For more with Al-Sakran, check out his appearance AWS’s 2017 SF Summit: