Data Engineer

A text-based course on how databases work underneath: storage and page layout, indexing, transactions and ACID, concurrency control, partitioning and sharding, replication and consistency, consensus and leader election, recovery, and the main NoSQL families. It ends with case studies designing the databases behind Quora, TikTok, Airbnb and Snapchat. It suits engineers who want their database choices in system design answers to be defensible rather than hand-waved.

• System design
• Databases
• Backend
• Job search

A beginner friendly, text based course on relational database design: data modelling, entity relationship diagrams, keys and integrity constraints, functional dependencies and normalisation up to BCNF and beyond. It finishes with nine case studies, from hospital and bank systems to an Instagram style app, e-commerce and ticket booking. For developers and students who want to design schemas that stay pleasant to work with years later.

• Web
• Databases
• System design
• Backend

The free, open companion to the Grokking the System Design Interview course, published as a GitHub repository. It teaches a pattern-first approach: learn building blocks such as caching, sharding, replication and queues well enough that any design question becomes a matter of combining them. It includes a seven-step interview framework, 30 patterns, 59 question walkthroughs, company guides, cheat sheets and study roadmaps, all readable in the browser without an account.

• System design
• Job search

A SQL course for interviews that reviews the basics, order of execution, functions, subqueries, joins, set operations, DDL and DML, then works through 69 interview problems graded easy to hard plus 14 unguided test problems, run against a live database in the browser. It suits backend, data and analytics candidates whose SQL stops at a simple inner join, or who need a fast refresher before a SQL round.

• Job search
• Databases
• Backend

The revision companion to Sefism's data engineering roadmap: ten chapters on ingestion, analytical SQL and modelling, file formats and lakehouses, Spark, Airflow, dbt, cloud data platforms, streaming, data quality and infrastructure. Each concept gives what it is, why it matters and the classic mistake, with snippets where code helps. It comes in AWS, Google Cloud and Azure editions, and suits students and early-career engineers revising after each stage or before a technical round.

• Databases
• Programming
• Cloud

An 8-stage path for students and early-career engineers who know some Python and SQL and want to build data pipelines on AWS, Google Cloud or Azure. It covers ingestion, analytical SQL and dimensional modelling, Parquet and Spark, Airflow and dbt, your cloud's data platform, Kafka streaming, data quality, Terraform and CI, and the first-role job search. You finish with one end-to-end portfolio pipeline that is tested, observable and costed.

• Databases
• Cloud
• Programming
• Career