Design efficient data pipelines. Empower your company to analyze data. Be at the center of the data job market.
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Data engineering 101
Begin by laying the foundation for your data engineering course. Familiarize yourself with the core approach of the program, delve into Python, and grasp the rudiments of data engineering in the cloud.
Docker
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Python
GitHub
GCP
Database fundamentals
Uncover the core aspects of SQL databases, gaining a solid understanding of both transactional and analytical paradigms. Develop your skills with PostgreSQL and BigQuery, two essential tools for any data engineer.
Big Query
PostegreSQL
Fivetran
Airbyte
Data storage and Batch Pipelines
Discover data storage strategies and understand the transformative shift from Docker to Kubernetes, providing a practical understanding of batch pipeline development.
Kubernetes
SQL
dbt
Airflow
Data at scale
Explore the challenges and solutions involved in handling large-scale data workloads. Learn to manage the transfer, processing, and tracking of large datasets, acquiring the skills necessary to maintain data consistency and accuracy.
Kafka
Pub/Sub
Apache Spark
Dataflow
You will learn Data Engineering through 5 modules, either full-time for 5 weeks or part-time for 6 months.
Kickstart your journey into Data Engineering with a deep dive into core concepts and tools, setting a strong foundation for your growth in this field from using Python and CI/CD best practices to learning Docker.
Set up your own virtual machine with Visual Studio Code
Perform data transformations and load data into the lake using Python
Apply CI/CD techniques using Ruff, Pylint, Github, and Poetry
Deploy a FastAPI app into production using Docker
Work on the central piece of your modern data stack: the data warehouse. Elevate your skills in SQL, Postgres and use BigQuery as a Data Engineer. Also, discover Docker Compose for handling multi-container Docker applications.
Create a data warehouse with BigQuery a set up access for your team
Import data using advanced SQL skills, Fivetran & Airbyte
Set up a Postgres instance on a compute engine and explore Cloud SQL
Utilize Docker Compose for local setup and testing of complex setups
Deepen your understanding of ETL, ELT, and ETLT processes with Airflow and DBT. Prepare your Data for various data visualization tools and orchestrate your Docker-created containers with Kubernetes.
Implement and optimize ETL workflows using Airflow
Build and manage data pipelines with DBT, with a focus on modularity, testing, and version control
Get introduced to Kubernetes and start working with key design patterns like sidecar
Learn to manage larger workloads and data transfers, explore the realm of streaming data at scale, and grasp the essential aspects of logging and monitoring.
Leverage PySpark for transforming massive amounts of data
Implement data streaming with Kafka and Pub/Sub
Transform streaming data in real-time, with a special emphasis on IoT data
Gain experience with various monitoring tools as your load scales up
Carry out a data engineering project from A to Z using the modern data stack with a team. Design your data architecture, create your data warehouse and set your data pipelines !
You'll also discover:
Build your own data engineering project end to end
Join our next Data Engineering bootcamp
Understand the goal of the bootcamp
Get our syllabus week by week
Understand our methodology
Download our Data Engineering syllabus
Join a live workshop and take your first steps into web development, data analytics with Python, JavaScript, SQL, and more!
Looking to advance your data engineering skills? Join us for this intensive workshop where we dive into the fascinating world of graph databases with Neo4j. Whether you're a developer, data scientist, or tech enthusiast, you'll discover how graph databases differ from traditional databases, and how to harness them for a unique culinary experience!
Bruno Lajoie
Selim Youssry
Nicolas Bancel
Yacine Alami
