Data engineering is the discipline that makes data usable, trustworthy, repeatable, and available at scale. Before learning individual tools, you need to see the complete journey: where data starts, how it moves, where it lives, how it changes, how it is monitored and governed, and how it finally reaches BI and AI.
Complete 12 of 24 practices (50%) and enter your name to unlock the Certificate of Participation.
Data engineering builds reliable systems that collect, move, store, transform, protect, and deliver data so people and applications can use it.
Sources → pipelines → trusted data → decisions • Engineering focuses on repeatability, scale, reliability, and control • A dashboard is the end of a larger data journey
Sources
↓
Ingestion / Integration
↓
Storage
↓
Transformation
↓
Trusted Data
↓
BI / AI / ApplicationsThink in systems, not isolated tools: the job is to make data dependable from source to consumption.
A modern data platform is easier to understand as a flow: collect, ingest, store, transform, orchestrate, validate, govern, analyze, and act.
Each stage solves a different operational problem • The same dataset may pass through several stages • Failures upstream can affect everything downstream
Collect → Ingest → Store → Transform
→ Orchestrate → Validate
→ Govern → Analyze → ActEvery downstream result depends on what happened upstream, so the whole flow must be designed as one system.
ETL transforms data before loading it into the target system. ELT loads first and transforms inside the destination platform.
ETL: Extract → Transform → Load • ELT: Extract → Load → Transform • The best choice depends on platform, scale, governance, and workload
ETL: Extract → Transform → Load
ELT: Extract → Load → TransformETL and ELT are architectural choices; neither is automatically better in every environment.
Batch processing handles data in groups at intervals. Streaming processes events continuously or with very low latency.
Batch is ideal when immediate results are not required • Streaming is useful for events, telemetry, alerts, and near-real-time use cases • Many real systems combine both patterns
Batch: data chunks → scheduled runs
Streaming: events → continuous processingChoose latency based on the business need. Not every workload needs real-time processing.
Databases, warehouses, lakes, and lakehouses serve different purposes. The architecture should match how the data will be stored, queried, governed, and consumed.
Database: operational applications • Warehouse: curated analytical data • Lake: flexible raw/semi-structured data • Lakehouse: combines lake flexibility with warehouse-style analytics
Operational DB → applications
Warehouse → analytics
Data Lake → raw / flexible data
Lakehouse → lake + warehouse patternsThe best storage pattern depends on how data is written, governed, queried, and consumed.
SQL, Python, Spark, and dbt shape data. Airflow, Dagster, Prefect, triggers, and dependency graphs coordinate when and how work runs.
Transformation changes data • Orchestration coordinates work • A pipeline needs both logic and reliable execution
Transformation logic:
SQL | Python | Spark | dbt
Orchestration:
Airflow | Dagster | Prefect
Schedules | Dependencies | RetriesTransformation defines the work; orchestration makes sure the work runs reliably in the right order.
A pipeline is not complete simply because it runs. Teams must know whether data is correct, fresh, traceable, secure, and accessible to the right people.
Quality tests accuracy and consistency • Observability watches health, freshness, failures, and delays • Governance defines ownership, policies, lineage, access, and protection
Quality → Is the data correct?
Observability→ Is the pipeline healthy?
Governance → Who owns, sees and trusts it?A pipeline that runs but delivers stale, wrong, untraceable, or exposed data is not a successful pipeline.
The final goal is not moving data—it is creating useful outcomes. Trusted data feeds dashboards, semantic layers, search, vector databases, RAG systems, applications, and decisions.
BI turns trusted data into metrics and decisions • Semantic layers standardize definitions • Vector databases and RAG support AI retrieval workflows • Good AI depends on good data engineering
Trusted Data
├─→ BI / Dashboards
├─→ Semantic Layer
├─→ Vector DB / Search
├─→ RAG / AI
└─→ Applications / DecisionsThe final product of data engineering is trusted data that people, analytics, applications, and AI can actually use.
Open each item after answering it in your own words. The 24 interactive practices above drive certificate progress.
To build reliable systems that collect, move, store, transform, protect, and deliver usable data.
ETL transforms before loading; ELT loads first and transforms inside the destination platform.
When events must be processed continuously or with low latency.
Quality tests the data, observability monitors pipeline health and freshness, and governance defines ownership, access, lineage, and policies.
Because reliable AI and retrieval workflows need trusted, accessible, well-managed data.
| Need | Strong candidate |
|---|---|
| Move data from sources | Ingestion & Integration |
| Keep data for different use cases | Databases / Warehouse / Lake / Lakehouse |
| Clean, enrich, and model data | SQL / Python / Spark / dbt |
| Run work reliably in the right order | Orchestration & Automation |
| Turn trusted data into outcomes | BI / Semantic Layer / AI / RAG |
Complete at least 12 of the 24 practice cases (50%) and enter your name.
Data engineering is a system discipline: reliable value comes from connecting ingestion, storage, transformation, orchestration, quality, governance, and consumption rather than optimizing one isolated layer.