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Learn Data Engineering

Build the pipelines that move, clean and organize data reliably.

Free to start. Built from a single prompt. No credit card.

Data engineering is the work of building systems that collect data from many sources, clean and transform it, and store it where analysts and models can use it reliably. The job is built on strong SQL and Python, plus data modeling, batch and streaming pipelines, cloud data warehouses, orchestration and data quality testing. Tools change often, so the lasting skills are concepts like idempotent loads, incremental processing, partitioning and dimensional modeling. Learnr builds a personalized data engineering course and teaches those concepts through short interactive lessons, tied to the tools you are likely to meet.

How to learn Data Engineering, step by step

Get excellent at SQL. Go beyond SELECT and JOIN to CTEs, window functions, MERGE or upsert patterns and reading query plans. SQL is the language data engineers use most, and interviewers test it heavily.
Learn Python for moving data. Write scripts that call APIs, handle pagination and retries, read and write files, and load data into a database. Add logging, error handling and tests from the start, because pipelines run unattended.
Study data modeling. Learn Kimball-style dimensional modeling with fact and dimension tables, star schemas, grain and slowly changing dimensions. A clear model is what makes the data useful to analysts after you deliver it.
Build an end-to-end batch pipeline. Extract data from a public API, land the raw files in storage, transform them with SQL or dbt into modeled tables, and make every run idempotent so rerunning the same day never duplicates rows.
Add orchestration, quality checks and scale. Schedule the pipeline with Airflow, Dagster or a similar tool, add tests for nulls, uniqueness and row counts, and learn when distributed engines like Spark and columnar formats like Parquet become necessary.
Learn cloud basics and keep concepts fresh. Get familiar with one cloud provider's storage, compute and warehouse services, plus Docker for packaging. Short daily Learnr lessons help keep modeling and pipeline concepts sharp while you build projects.

What you'll learn

Write advanced SQL with joins, window functions and CTEs
Design dimensional data models with fact and dimension tables
Build ETL and ELT pipelines in Python that are idempotent and incremental
Schedule and monitor pipelines with an orchestrator such as Airflow
Explain warehouses, data lakes, columnar formats like Parquet and distributed processing with Spark

What's inside your Data Engineering course

A sample path Learnr builds for Data Engineering. Your real course adapts to your goal and level.

1. What data engineers build and why
2. Advanced SQL and Python for pipelines
3. Data modeling for analytics
4. Batch pipelines, ETL and ELT
5. Warehouses, lakes, file formats and Spark
6. Orchestration, streaming and data quality

How long does it take to learn Data Engineering?

Coming from data analysis or software development, the core of data engineering takes about 4 to 8 months at 6 to 10 hours a week. Starting from scratch, including learning SQL and Python, expect 9 to 18 months before you can build production-quality pipelines.

What you need before you start

Solid SQL and working Python are the essentials, plus comfort with the command line and Git. Familiarity with how databases store and index data helps.

Common mistakes to avoid

Chasing every new tool instead of mastering SQL, data modeling and pipeline fundamentals that every tool relies on.
Writing pipelines that append data on every run, so a retry after a failure creates duplicates. Design loads to be idempotent.
Skipping data quality tests, so broken upstream data flows silently into dashboards before anyone notices.

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Data Engineering questions, answered

What is the difference between ETL and ELT?

ETL transforms data before loading it into the destination. ELT loads raw data into a warehouse first and transforms it there with SQL. ELT has become common because cloud warehouses can handle the transformation work at scale.

Is data engineering harder than data science?

It is different rather than harder. Data engineering leans toward software engineering, systems and reliability, while data science leans toward statistics and modeling. People who enjoy building dependable systems often prefer engineering.