Every analytics dashboard and every machine learning model depends on data arriving clean, on time, and in the right place. Data engineers build the systems that make that happen. The Data Engineer BootCamp teaches the core of that work: SQL and Python for data, distributed processing with Spark, pipelines and orchestration, streaming with Kafka, modern data platforms, and deploying it all to the cloud.
This guide covers what’s in the program, who it suits, and where its limits are.
The data engineer bootcamp at a glance
| Total hours | 80 hours of instructor-led online training |
| Modules | 10 |
| Format | Hands-on: you build pipelines with industry tools throughout |
| Capstone | A production data platform covering ingestion, transformation, warehouse, streaming, and CI/CD deployment |
| Assessment | No certification exam. The program ends with a final assessment and a final project |
| Background | Basic programming and database knowledge recommended; DevOps experience helps |
| If you fall behind | Lifetime access to course content and mentor catch-up sessions, per the program FAQ |
How the ten modules fit together
The modules follow the path data takes through a company: get the foundations right, process data at scale, move it reliably, then ship and run it in the cloud.
Number of modules in each part of the Data Engineer BootCamp
Source: AgileFever Data Engineer BootCamp curriculum, October 2026. Grouping is ours.
| Module | What you learn | Tools |
|---|---|---|
| Python for data engineering | Data structures, functions, reading and writing CSV, JSON, and text files | Python, VS Code, Jupyter |
| SQL for data engineering | Joins, grouping, window functions, CTEs, writing efficient analytical queries | MySQL, PostgreSQL |
| Spark and PySpark | Spark architecture, DataFrames, transformations and actions, Spark SQL | PySpark, Databricks |
| Git and GitHub | Branching, merging, resolving conflicts in data projects | Git, GitHub |
| Pipelines and orchestration | ETL vs ELT, error handling, retries, and alerts | Apache Airflow, Databricks Workflows, Azure Data Factory |
| Data platforms | Notebooks and clusters, Databricks SQL, scheduling, Snowflake basics | Databricks, Snowflake |
| Kafka and streaming | Topics, partitions, brokers, producers, and consumers | Kafka, Confluent Cloud, Redpanda |
| CI/CD for data engineering | Automated testing and linting, triggering deployments and data jobs | GitHub Actions, Docker |
| Cloud platform | Cloud storage, IAM and access control, deploying compute jobs | Google Cloud |
| Data architecture | Warehouses, data lakes, and lakehouses, and when to use each | Databricks Lakehouse, Snowflake |
What the capstone proves
The capstone is a full data platform: data comes in, gets transformed, lands in a warehouse, streams where it needs to, and deploys through CI/CD. That’s the shape of the system most data engineering interviews ask you to describe, so having built one end to end gives you something concrete to talk through.
Who it’s for
| You are… | What you’ll get from it |
|---|---|
| A software or backend engineer | The data side of systems you already build: pipelines, warehouses, and streaming at scale |
| An ETL developer | Modern, cloud-based versions of what you already do: Spark, Databricks, Snowflake, and Airflow |
| A data analyst | The engineering behind the data you query, and a route into engineering roles |
| A graduate with Python and SQL | A structured path into a first data engineering role |
Who should choose something else
- If you want to train and deploy machine learning models, the MLOps BootCamp or the AI and ML BootCamp is a closer fit
- If you’ve never written SQL or Python, start with the basics first. The program assumes basic programming and database knowledge
- If your company runs mainly on AWS or Azure, note that the cloud module focuses on Google Cloud. The concepts carry over, but you’ll need to learn your provider’s services separately
Dates and pricing
Upcoming dates and pricing weren’t listed on the program page when this was written. Contact the team for the next batch and current fees.
Additional discounts are available. Contact us to know more.
FAQ
Do I need prior data engineering experience?
No, but basic programming and database knowledge is recommended.
Does it cover data security?
It touches on governance, access control, and the data lifecycle, and the cloud module covers IAM.
Does the certificate expire?
No. It doesn’t need renewing.
What to read next
For a wider view of the career, read A complete roadmap to becoming an AI data engineer. If you’re deciding between data engineering and MLOps, see Data Engineer BootCamp vs MLOps BootCamp.
