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Open-Source BI solution

Historically BI was an expensive solution, but within the last decade, more and more engineers were joining the data world, boosting the technological stack in this area. Nowadays, plenty of not-so-expensive instruments are available on the market, including free, open-source ones. The stack we offer over here has proven itself to be robust, cheap, and efficient in many cases for many companies.  Our experience with these instruments and in analytics overall allows to implement this stack quickly and for a reasonable price.

Technical Stack

The majority of the products we use are either open-source or free, except the data warehouses, which are normally Cloud SaaS solutions

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Meltano

The Meltano open-source framework has hundreds connectors to different systems allowing pulling data from Facebook, Google Ads, GA4, LinkedIn and saving into the BigQuery, Snowflake and many other targets 

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DBT

DBT is an open-source framework with rich functionality to do the transformation, implement auto-tests, generate documentation. It has a huge community, and has become almost a standard in the data engineering world

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GitHub

GitHub is the most famous Code Version Control system but it also has a module called GitHubActions which allows to configure scheduled pipelines. 

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BigQuery

It's a DWH and a data platform by Google that support SQL Standard and specifically designed for analytcal purposes. It's not free but has a free tier

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Snowflake

Another DWH for the analytical purposes. The main difference of which is a separation between the storage and the computational resources controlled by users

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Looker Data Studio

Free Visualization tool by Google. Not so powerful like PowerBI, Tableau, or Looker but it's free and good enough to start with

How does it work?

Meltano extracts data from the sources and saves the data into the DWH. Github Actions are used to run this process according to some schedule

DBT cleans and joins the data, producing tables and views that can be consumed by the visualization tool or sent to the downstream systems. It also ensures data quality by running auto-tests and can generate documentation. 

Visual Data Studio / Google Sheets or any other visualization platform pulls already prepared data from DWH and visualize it on dashboards

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