Data Engineering Bootcamp Certificate
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Bootcamp Mastery Certificate B05
Primary spine: DataTalksClub Data Engineering Zoomcamp. The current path moves from infrastructure and orchestration through ingestion, warehousing, analytics engineering, modern data platforms, batch processing and streaming.
Benchmark: DataTalksClub makes Data Engineering Zoomcamp free for self-paced study. Its current course page states that provider certificates are tied to eligible live cohorts, scored work and peer review; self-paced completion alone does not imply a DataTalksClub certificate. Zuyini completion uses the independent build-and-proof rubric above.
Core Video Sequence
01 — Complete canonical lecture sequence
02 — 2026 orientation, roadmap + final project
03 — Docker, Postgres + infrastructure foundations
04 — Workflow orchestration with Kestra
05 — AI-assisted ingestion with dlt
06 — BigQuery warehousing, partitioning + cost
07 — dbt models, tests, docs + analytics engineering
08 — Unified data platform, quality + orchestration
09 — Spark batch processing + distributed data
10 — Kafka/Flink streaming + fault tolerance
Build-Along Checkpoints
- Checkpoint 1: Containerize a reproducible local stack with Postgres and define cloud/IaC choices for the target environment.
- Checkpoint 2: Ingest a real API/file source with dlt or equivalent; support schema evolution and an incremental load.
- Checkpoint 3: Orchestrate scheduled pipelines with Kestra or equivalent; demonstrate retries, backfills and parameterized runs.
- Checkpoint 4: Model data in BigQuery/warehouse layers and build dbt staging, intermediate and mart models with tests and docs.
- Checkpoint 5: Process a larger partitioned dataset with Spark and justify partitioning, joins, persistence and cost/performance choices.
- Checkpoint 6: Build a Kafka/Flink streaming path, capture late/out-of-order behavior and prove one checkpoint/recovery scenario.
7-Day Independent Capstone
- Day 1: Choose a real data domain; define consumers, source contracts, freshness SLOs, privacy constraints and architecture.
- Day 2: Build ingestion and raw/bronze storage; add incremental loading, schema handling and reproducible local/cloud setup.
- Day 3: Add orchestration with schedules, backfills, retries and one intentionally failed run that you recover cleanly.
- Day 4: Build warehouse/lakehouse models plus dbt tests, documentation and a decision-ready dashboard or downstream dataset.
- Day 5: Add a Spark batch job and measure at least one performance or cost tradeoff with realistic data volume.
- Day 6: Add a streaming path; test delivery semantics, late events or restart recovery and record operational evidence.
- Day 7: Publish repo, lineage/architecture, data-quality results, SLO evidence, runbook, demo video and lessons learned.
Data Engineering - Build, Operate & Prove
Build an end-to-end data platform that ingests, orchestrates, transforms, serves and monitors real data. Completion requires batch and streaming evidence, quality controls, reproducibility and production-style operations.
Proof Rubric — What Must Be Shown
- INGESTION: Real source, incremental loads, schema handling
- ORCHESTRATION: Schedules, retries, backfills, recovery Partitioned storage + defensible model design dbt models, tests, docs + serving layer
- WAREHOUSE:
- ANALYTICS: Spark job + performance evidence Kafka/Flink path + recovery semantics
- BATCH:
- STREAMING:
- QUALITY: Freshness, validation, lineage + governance
- OPERATIONS: SLOs, logs, runbook, cost + demo