This series details the design and implementation of an end-to-end data product for personal LinkedIn analytics, with the goal of demonstrating data engineering principles to aspiring and experienced data professional alike.
Orginally published on Medium on 8 July 2026 A side quest from my “Build Your Own LinkedIn Analytics” project Status note: At the time of writing, this post describes three contributions: Issue #1497 and PR #1498 to Graphify core (both open, under …
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In the 12th and final post of the series, I release the open-source repository that implements the LinkedIn analytics pipelines, and discuss future plans.
In the 11th and penultimate post of the series, I look back on what has been achieved, what can be done better and what has been learned.
In the 10th post of the series, I show how to set up observability on our data pipeline to monitor its condition and act as necessary.
In the 9th post of the series, I use a combination of Git and Databricks Asset Bundles to make the data pipeline easily deployable and maintainable.
In the 8th post of the series, I convert the scattered pieces of data ingestion, processing and dashboarding into an orchestrated and automated data pipeline.
In the 7th post of the series, I build a dashbord on Databricks for the ingested LinkedIn data.
In the 6h post of the series, I explore the approaches to modelling the LinkedIn data in the gold layer.
In the 5th article in the series, I cover the process of cleaning and transforming the LinkedIn data into a Single Source of Truth (SSOT) in the silver layer.
In the 4th article in the series, I deep dive into the ingestion process into bronze layer and highlight relevant industry practices along the way.