transitioning from data analyst ➤ data engineer | contributor at @TDataScience | #learninginpublic
Nigeria
Joined February 2022
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I’m learning Data Engineering from scratch as a Data Analyst.
So I mapped out a 12-month self-study roadmap to guide the transition.
I just published it here @TDataScience
You can read it here
towardsdatascience.com/from-…
Ibrahim Salami (Ibby) retweeted
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I built a ETL pipeline that extracts articles from top data engineering blogs and sites, so I can easily stay up to date. I shared the entire process here
Building a pipeline is one milestone, but thinking like a data engineer is another. @ibbysalam shares how a production-ready ETL project changed the way he approaches data engineering.
towardsdatascience.com/i-bui…
Got my hands dirty with dbt recently. Fascinating how you don’t have to save files and manually transform using SQL. You can pretty much transform your data before it hits your data warehouse. I documented my learnings here
In the latest post documenting his data engineering journey, @ibbysalam learns what "analysis-ready" data actually means. towardsdatascience.com/i-tho…
I found a roadmap to transitioning to data engineering. To be honest, I didn't follow it entirely. I ended up following another path that suits me: learning by building mini projects. Read more about my experience here @TDataScience
After a month of data-engineering learning, @ibbysalam reflects on his achievements and challenges thus far, and offers insights for others embarking on similar professional journeys. towardsdatascience.com/one-m…
I built my first ETL pipeline as a complete beginner.
No Airflow.
No Spark.
No cloud infrastructure.
Just Python, pandas, and a GitHub API.
Recently shared the full story in @TDataScience.
Here's exactly what I learned 🧵👇
Ibrahim Salami (Ibby) retweeted
2026 Data Engineering Roadmap 📊
Stage 1: SQL
Stage 2: Python
Stage 3: Data Modeling
Stage 4: ETL Pipelines
Stage 5: PostgreSQL
Stage 6: Spark
Stage 7: Airflow
Stage 8: Data Warehousing
Stage 9: Cloud (AWS/GCP/Azure)
Stage 10: Streaming Systems
Stage 11: Data Quality + Monitoring
Stage 12: Production Pipelines
Data scientists get the spotlight.
Data engineers move the data.
Data Engineering work is mostly ETL (Extract, Transform, Load).
So to learn about ETL, I decided to build a basic ETL pipeline that extracts data from GitHub repositories and saves it as CSV
Read about the entire process @TDataScience
Using the GitHub API, @ibbysalam shows us the steps he took to build an extract, transform, load data pipeline from scratch — and as a complete beginner. towardsdatascience.com/i-bui…