Study planner
Data Engineering concepts tracker
Foundations and reliable pipeline practices, linked to the existing ETL/ELT, warehousing and Python lessons. 30 topics. Open a topic to study it, then tick your progress.
DE Concepts studied0 of 30
Studied, Practised, Rev 1 and a confidence score (1–5) for each topic, as in the planner spreadsheet. A topic counts as complete once it is marked studied.
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| # | Topic | Category | Level | Studied | Practised | Rev 1 | Confidence |
|---|---|---|---|---|---|---|---|
| 1 | Data lifecycle and ownership | Data Engineering Concepts | Beginner | ||||
| 2 | OLTP and OLAP workloads | Data Engineering Concepts | Beginner | ||||
| 3 | Batch versus streaming | Data Engineering Concepts | Beginner | ||||
| 4 | ETL versus ELT | Data Engineering Concepts | Beginner | ||||
| 5 | Storage layers and raw landing | Data Engineering Concepts | Beginner | ||||
| 6 | CSV and JSON trade-offs | Data Engineering Concepts | Beginner | ||||
| 7 | Parquet and columnar storage | Data Engineering Concepts | Beginner | ||||
| 8 | Avro and schema evolution | Data Engineering Concepts | Beginner | ||||
| 9 | Partitioning and data layout | Data Engineering Concepts | Beginner | ||||
| 10 | Warehouse grain and star schemas | Data Engineering Concepts | Beginner | ||||
| 11 | Fact and dimension tables | Data Engineering Concepts | Intermediate | ||||
| 12 | Slowly changing dimensions | Data Engineering Concepts | Intermediate | ||||
| 13 | Incremental loading and watermarks | Data Engineering Concepts | Intermediate | ||||
| 14 | Idempotent pipeline writes | Data Engineering Concepts | Intermediate | ||||
| 15 | Change data capture patterns | Data Engineering Concepts | Intermediate | ||||
| 16 | Data quality checks and contracts | Data Engineering Concepts | Intermediate | ||||
| 17 | Testing pipelines and CI/CD | Data Engineering Concepts | Intermediate | ||||
| 18 | Retries and failure recovery | Data Engineering Concepts | Intermediate | ||||
| 19 | Pipeline observability | Data Engineering Concepts | Intermediate | ||||
| 20 | Late data and backfills | Data Engineering Concepts | Intermediate | ||||
| 21 | Python functions and reusable code | Data Engineering Concepts | Intermediate | ||||
| 22 | Python iterators and generators | Data Engineering Concepts | Intermediate | ||||
| 23 | Python API pagination | Data Engineering Concepts | Intermediate | ||||
| 24 | Python CSV validation | Data Engineering Concepts | Intermediate | ||||
| 25 | Data normalization trade-offs | Data Engineering Concepts | Intermediate | ||||
| 26 | Lake, warehouse and lakehouse | Data Engineering Concepts | Intermediate | ||||
| 27 | Data modeling methods | Data Engineering Concepts | Intermediate | ||||
| 28 | Secure data access and PII | Data Engineering Concepts | Intermediate | ||||
| 29 | Replay-safe event processing | Data Engineering Concepts | Intermediate | ||||
| 30 | Cost and freshness trade-offs | Data Engineering Concepts | Intermediate |
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