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Articles

How to Use Metabase to Optimize Decision-Making

BY Katia Bonella, Andrea Cesti, Valentin Popov

At BOOM, the data team’s mission is to support the company’s data-driven approach, and selecting the right tools to carry out projects is a key part of it. When it came to choosing a power visualization data tool, the team tested a couple, and ultimately picked Metabase,  an open-source business intelligence tool

What does Metabase provide exactly?

To put it simply, Metabase answers questions about data, displaying them in graphs or detailed tables. The program saves the questions or groups them inside dashboards. 

Early on, Metabase proved to be useful to onboard new employees quickly, thanks to Google Authentication, and made it a perfect tool for GDPR compliance, thanks to a very intuitive group-based approach to setting permissions of data and collections. 

Metabase offers two options to choose from: the free and the enterprise version. 

BOOM installed a free Metabase cluster (2 instances) in a Kubernetes cluster on Amazon Cloud, which relies on the MySql database (For installation details read here). 

One of the main advantages of Metabase is that all charts can be dragged and dropped and the use of SQL is optional during dashboard development.

Using Metabase in our Data Pipeline

At BOOM, we integrate heterogeneous data sources in a data warehouse adding a historicization of transaction systems. Then we clean and arrange all data and present them to our stakeholders using Metabase.

Example of BOOM’s Data Pipeline:

BOOM’s data analysts found it useful to divide widgets and dashboards into separate folders. Every team has two principal folders: one with only their own dashboards and one with widgets organized with sub-folders - one for every dashboard - which collect all the widgets that make it up.
This way, not only is it simple to find a specific dashboard but moving, dating, or sharing data from one group to another is a breeze.
Additionally, data analysts recommend personalized documentation, which features a series of short video tutorials about folder organization, chart creation, and howMetabase is structured for BOOM’s various requirements. 

From a DevOps’ perspective, BOOM’s Metabase runs on Kubernetes, including two particular instances in high availability, to always have at least one active, in case of maintenance or reboots. A DevOps knows that when it comes to database instances, connection limits must always be taken into consideration. How did BOOM handle that? Well, by optimizing our deployment and managing the application restart in  Kubernetes, to have a maximum of two simultaneously active machines, and a maximum of one down.
Finally, it is important to adapt the Metabase service to generate logs in json format, in order to be able to read from Datadog and monitor it with other logs.
Interested in finding our customized Docker image with those modifications? Just click here!

Find out more about Metabase:

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