This course covers the essentials of data engineering, from handling structured and unstructured data to designing scalable systems with Hadoop, Spark, and Kafka.

hdfs.tar.gz

pyspark

Kafka

Data engineering is the foundation of modern data infrastructure, focusing on building systems that collect, store, process, and analyze large datasets. Mastering it makes you a key player in modern data-driven businesses. As a data engineer, you’re responsible for making data accessible and reliable for analysts and scientists. 

In this course, you’ll begin by exploring how data flows through various systems and learn to fetch and manipulate structured data using SQL and Python. Next, you’ll handle unstructured and semi-structured data with NoSQL and MongoDB. You’ll then design scalable data systems using data warehouses and lakehouses. Finally, you’ll learn to use technologies like Hadoop, Spark, and Kafka to work with big data.

By the end of this course, you’ll be able to work with robust data pipelines, handle diverse data types, and utilize big data technologies.

Learn Data Engineering

Practice using SQL to join, aggregate, and sort operational data from a system log perspective.

Dive into Data Engineering

Talk to Data

Think Outside the Table

Explore Data Worlds!

Process and Manage Big Data Effectively

Clean It Up

Conclusion

Coding Exercise: Monitor Failed Job Counts by Server

Scenario

Dataset