In this program, you’ll develop the skills and knowledge you need to join the rapidly growing data engineering field. With the help of expert instructors and mentors, you’ll design data models, build data warehouses and data lakes, automate data pipelines, and work with Big Data. These skills are in high demand and companies are facing major shortages of data engineering talent. Upon completing the program, you’ll have the skills you need to become a data engineer. Data Engineering is the foundation for the new world of Big Data. Enroll now to build production-ready data infrastructure, an essential skill for advancing your data career.
To be successful in this program, you should have intermediate Python and SQL skills.See detailed requirements.
Learn to create relational and NoSQL data models to fit the diverse needs of data consumers. Use ETL to build databases in PostgreSQL and Apache Cassandra.
Data Modeling with PostgresData Modeling with Apache Cassandra
Cloud Data Warehouses
Sharpen your data warehousing skills and deepen your understanding of data infrastructure. Create cloud-based data warehouses on Amazon Web Services (AWS).
Build a Cloud Data Warehouse
Spark and Data Lakes
Understand the big data ecosystem and how to use Spark to work with massive datasets. Store big data in a data lake and query it with Spark.
Build a Data Lake
Data Pipelines with Airflow
Schedule, automate, and monitor data pipelines using Apache Airflow. Run data quality checks, track data lineage, and work with data pipelines in production.
Data Pipelines with Airflow
Combine what you've learned throughout the program to build your own data engineering portfolio project.
Data Engineering Capstone
Amanda Moran, Ben Goldberg, Sameh El-Ansary, Olli Iivonen, David Drummond, Judit Lantos, Juno Lee , Rodrigo G., Andrew M., Stanislav V., Eugenio C., Nitheesha T. and Jitesh S.
I'm working as dwh engineer and want to forward to check how data engineer stuff going on and I did some research about which course or programm I should start study on then I find udacity and few other programms and compare them by content difference , which tools they are using while course going on ,which technologies they are prefer to apply and how these are fits to real life scenarios and belive me the one of most important point is evaluation proccess of your projects and asking questions to MENTORS (till now I can't see any question not answered). I see udacity far best ! . I advice you to check udacity courses before you decide to start.
The project gave me a better knowledge about OLAP vs. OLTP, normalization, denormalization, and how to implement it into practice. I have been working in Data Engineering field for 3 years, but the program has given me a clear understanding about why we need those concepts in DE fields.
Everything looks good for a start and continue with Data Engineer. I expect to know the most about Data Engineer but my work is a bit busy lately. I will manage it. If you have more perspective about Data Engineering, please advise me.
I've just completed the first project. The project was great since it's what you'd expect to be able to do at work, and the reviewer's comments and guidance were helpful and informative!