Free, in-depth guides with tested code, real architecture and hundreds of interview questions, across six learning tracks.
Each track gathers every guide on one subject, from fundamentals to the patterns used in production.
See popular guidesPipelines, Spark, streaming, lakehouses and warehouses
17 guidesAWS and Azure services for data teams
19 guidesPython, machine learning and deep learning
2 guidesLLMs, prompting, RAG and AI engineering
1 guideGit, CI/CD, containers and Kubernetes
4 guidesHundreds of questions with model answers
2 guidesGood places to start, whichever role you are preparing for.
Joins, self-joins and hierarchies, NULL traps, window functions, DML, performance and classic puzzles.
SQLWindowsPuzzlesRead guideData EngineeringDataFrames, joins, tuning and salting, cluster sizing, Delta, Auto Loader, Lakeflow and production Databricks.
PySparkDatabricksTuningRead guideData EngineeringArchitecture styles, table formats, governance, security and lineage across clouds, plus implementing lakes, warehouses, lakehouses and data mesh, and modernising legacy systems.
ArchitectureGovernanceModernisationRead guideData EngineeringTransaction log, MERGE, time travel, schema evolution, OPTIMIZE/VACUUM and Change Data Feed.
DeltaACIDTime travelRead guideData EngineeringSQL transformations with tests and lineage: models, snapshots, incremental loads, contracts, and running dbt on Databricks with an end-to-end retail use case.
dbtELTDatabricksRead guideData EngineeringConceptual to physical models, star schemas, building a model in erwin, and running its DDL with PySpark on Databricks.
ModellingerwinDDLRead guideGenerative AILLMs, prompting, structured extraction, tool use and agents, embeddings, RAG, evaluation and cost.
LLMsRAGAgentsRead guideDevOpsArchitecture, kubectl, EKS pods and logs, scaling and probes, CronJobs, Spark on Kubernetes, Helm.
kubectlEKSHelmRead guideSQL traps and puzzles, Spark internals, streaming, cloud, governance and system design, each with a model answer.