In the age of big data and data-driven AI, many companies start to realize the importance of establishing data engineering best practices. As a result, the demand for data engineering has been growing rapidly. Currently there is a huge supply and demand mismatch in the talent market. One reason for the imbalance is that modern data engineering requires new tools/skills and traditional learning environments such as universities, colleges, and bootcamps don’t keep up with the trends. Another reason is that data engineering is hard to teach! Curriculums need to be extremely hands-on, and it requires very seasoned instructors who work in the fields to teach in the most practical way.
In this 12-week part-time course you will learn how to build complex data pipelines for various real-world use cases. The core skills you will develop in this course include: Docker and Kubernetes, Spark, Spark Streaming, Delta Lake, Data Lakehouse, Data Pipelines, Data Ingestioon, Data Integration, Data Governance.
Data engineers are usually harder to train and source because the program needs to be very practical/hands-on and there is not much theory to teach. The open-source communities are also pushing out new tools and platforms on a regular basis which makes teaching data engineering challenging because materials need to be updated rapidly to keep up with the latest trends. At WeCloudData, we have heard from many hiring managers and recruiting agencies say that while the demand for data engineers is great, data engineer talents are even harder to find compared to data scientists.
The Big Data Integration & ETL course is a sequel to the Data Engineering Fundamentals course. In 12 weeks, you will learn how to apply data engineering skills to solve data migration, integration, real-time streaming data processing, as well as data governance, and big data. Completing this course will give you the skills and confidence you need to get a modern data engineer job.
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