Databricks Data Engineer Professional study plan
Six weeks at 8–10 hours a week. Ten sections is a lot of surface, so this plan groups them rather than treating each separately.
You need a Databricks workspace with enough data to make performance visible. Several sections — optimisation, debugging, monitoring — cannot be learned on toy datasets where everything is instant.
Hold the Associate already? See the compressed plan at the end.
Weeks 1 and 2: developing code (22%)
The largest section, two weeks.
Week 1 — reading and reasoning about code:
- PySpark DataFrame operations: selects, filters, joins, aggregations, window functions.
- The same logic expressed in SQL, and when each is preferable.
- Lazy evaluation and what actually triggers work.
- Reading a snippet and predicting its output — the core exam skill.
Week 2 — writing it well:
- Structuring reusable, testable transformation code.
- Common inefficiencies: unnecessary shuffles, collecting to the driver, row-by-row operations.
- User-defined functions and why they are usually the slow option.
- Handling errors inside transformations.
Week 3: cost and performance optimisation (13%)
The second largest section and the one that most distinguishes this exam.
- Reading the Spark UI: stages, tasks, shuffle volume, spill.
- Data skew — recognising it and redistributing.
- Small files — how they arise and how compaction fixes them.
- Partitioning and file layout: choosing columns, avoiding over-partitioning.
- Join strategies, including broadcasting small tables.
- Caching — when it helps and when it wastes memory.
- Cluster sizing, autoscaling, and terminating idle compute.
Week 4: ingestion (7%), transformation and quality (10%), modelling (6%)
Three related sections, 23% together.
- Ingestion and acquisition at Professional level: incremental patterns, handling source changes, reprocessing safely.
- Transformation, cleansing and quality: deduplication, null strategies, late-arriving data, and enforcing quality expectations rather than assuming them.
- Data modelling: dimensional modelling on the lakehouse, slowly changing dimensions, and choosing grain.
Week 5: monitoring (10%), debugging and deploying (10%), security (10%), governance (7%), sharing (5%)
Five sections, 42% together. Cover each properly but briskly.
- Monitoring and alerting: what to instrument, meaningful thresholds, detecting a pipeline that succeeds while producing wrong data.
- Debugging and deploying: diagnosing failures from logs and the Spark UI, deployment patterns, rollback.
- Security and compliance: encryption, access control, sensitive data handling, audit.
- Governance: Unity Catalog at depth — lineage, ownership, policy.
- Sharing and federation: sharing data across organisations, and querying external sources without copying.
Week 6: practice and repair
- Full practice exam under real conditions: 120 minutes, 59 questions.
- Sort mistakes by section. With ten sections, look for clusters rather than individual misses.
- Rebuild the weakest two, then take a second practice exam.
Where the hours go
| Week | Focus | Weight | Hours |
|---|---|---|---|
| 1–2 | Developing code | 22% | 16–20 |
| 3 | Cost and performance | 13% | 8–10 |
| 4 | Ingestion, quality, modelling | 23% | 8–10 |
| 5 | Monitoring, deploying, security, governance, sharing | 42% | 8–10 |
| 6 | Practice and repair | — | 8–10 |
Compressed plan for Associate holders
Three to four weeks:
- Weeks 1–2: developing code. Entirely new emphasis.
- Week 3: cost and performance optimisation, plus data modelling and data sharing and federation.
- Week 4: practice exam, then repair whatever the score report flags.
Do not restudy ingestion, governance and basic transformation — verify with practice questions and move on.
The small sections are the trap. Five of them are 7% or less, which feels negligible until you notice they total 25% of the exam. Give each an hour; give none of them zero.