Written by the AgileFever editorial team. Program details were checked against the live program pages in October 2026. We run this program. Module names, tools and projects come from its published curriculum, and we say where it stops.
Data engineering courses often read like tool lists. What matters is the order in which the tools appear and what you build with each one. This walkthrough takes the ten modules in sequence, names the tools and the project attached to each, and says plainly where an experienced engineer would be revising and where they would be learning something new.
The ten modules
| # | Module | Topics | Tools | Project |
|---|---|---|---|---|
| 1 | Python foundations | Data structures, functions, CSV, JSON and text files | Python, VS Code, Jupyter | File processing engine |
| 2 | SQL | Joins, window functions (ROW_NUMBER, RANK, LAG), CTEs | MySQL, PostgreSQL | SQL analytics project |
| 3 | Spark and PySpark | Spark architecture, RDDs and DataFrames, Spark SQL | Databricks, PySpark | Distributed processing job on a sales dataset |
| 4 | Git and GitHub | Branching, merging, resolving conflicts | Git, GitHub | Version-controlled pipeline |
| 5 | Pipelines and orchestration | ETL vs ELT, logging, error handling, retries and alerts | Airflow, Databricks Workflows, Azure Data Factory | End-to-end ETL pipeline |
| 6 | Data platforms | Notebooks, clusters, workflow scheduling, warehouse basics | Databricks, Snowflake | Lakehouse implementation |
| 7 | Kafka and streaming | Topics, partitions, brokers, producers and consumers | Confluent Cloud, Redpanda, Kafka | Real-time streaming project |
| 8 | CI/CD for data engineering | Automated testing and linting, triggered deployments | GitHub Actions, Docker, MLflow, DagsHub | CI/CD automation project |
| 9 | Cloud (Google Cloud focus) | Cloud Storage, IAM and access control, compute jobs | GCS, IAM, Compute | Cloud data deployment |
| 10 | Data architecture fundamentals | Warehouse, lake and lakehouse, and when to use each | Databricks Lakehouse, Snowflake | Architecture design blueprint |
Count of the 10 modules
The page does not publish hours per module, so we have counted modules, not time.
How the modules build on each other
The first four modules are the toolkit: Python and SQL for the work itself, Spark for data too large for one machine, and Git for working with a team. Modules 5 and 6 are where the toolkit turns into a pipeline. You build an ETL job, schedule it with retries and alerts, then run the same kind of workload on Databricks and Snowflake. Module 7 changes the shape of the problem from batch to streaming. Modules 8 and 9 cover how the pipeline gets tested, deployed and secured on Google Cloud. Module 10 steps back and asks which architecture the whole thing should have been.
The capstone ties the sequence together. You build a data platform that covers ingestion, transformation, a warehouse, streaming and CI/CD deployment, using Python, SQL, Spark, Kafka, Airflow, Google Cloud and GitHub.
Where experienced engineers would find review or new material
| If you already | Probably review | Probably new |
|---|---|---|
| Write backend code and know SQL | Modules 1, 2 and 4 | Spark, Kafka and the lakehouse modules (3, 6, 7, 10) |
| Work as an ETL developer on an older stack | Modules 2 and 5 in concept | Cloud platforms, streaming and CI/CD for data jobs |
| Work as a data analyst | Module 2 in part | Modules 3 to 10; the engineering side is the jump |
| Run DevOps pipelines | Modules 4 and 8 | Spark, Kafka and warehouse design |
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What the program does not cover
- Machine learning models and LLMs. The module list has none. If you want those, look at the AI and ML BootCamp or the MLOps, LLMOps and Agentic AI Ops BootCamp.
- Deep work on Azure or AWS. Cloud coverage in module 9 is Google Cloud. Azure Data Factory appears only as one orchestration tool in module 5.
- Data governance as its own topic. The FAQ says access control and data lifecycle are touched on, and the module list has no separate governance module.
Assessment, prerequisites and cost
| Item | What the page says |
|---|---|
| Assessment | No separate certification exam. A final assessment and a final project, and passing both earns the AgileFever Data Engineering Certification |
| Prerequisite | Basic programming and database knowledge recommended. Hands-on DevOps experience helps but is not required |
| Format | 80 hours, live and instructor-led, with recordings of live sessions |
| Cost | No list price is shown on the page. Monthly EMI is listed as low as $125. Ask an advisor for the current fee and cohort dates |
If you are earlier in your career, the page suggests building fundamentals first with Python for AI, a free 6-hour course. For how this program compares with the MLOps option, read Data Engineer BootCamp vs MLOps BootCamp, and for backend developers specifically, is the Data Engineer BootCamp right for backend developers. The program page is the Data Engineer BootCamp.
Questions people ask
Is there one exam at the end?
No. The page says there is no separate certification exam. You complete a final assessment and a final project.
Which cloud does it use?
Module 9 is built on Google Cloud, covering Cloud Storage, IAM and a compute job.
Are the sessions recorded?
Yes. Live sessions are recorded.
Do I need to know Spark before I start?
No. Module 3 starts from Spark architecture. You do need basic programming and database knowledge.
