Certified Data Engineering Professional

Why should you attend?

This course offers a comprehensive exploration of data engineering concepts, focusing on the practical application of SQL and PostgreSQL to build fluency in database management. Participants will learn to create relational data models and understand the principles of normalization, providing a solid foundation for efficient data handling. The curriculum delves into data modeling, contrasting SQL with NoSQL data models, and guides learners through implementing denormalized schemas such as STAR and Snowflake. Additionally, students will gain hands-on experience creating NoSQL databases using Apache Cassandra. Business intelligence and data warehousing are covered extensively, with modules on implementing data warehouses on AWS and building multi-dimensional cubes using Pentaho. The course also introduces SparkSQL, DataFrames, and Datasets, emphasizing their use over traditional RDDs and exploring Spark MLLib for machine learning applications. Learners will explore the power of Spark in managing data lakes, including techniques for debugging and optimization. The course highlights the importance of modernizing data lakes and warehouses to enhance business operations through successful data pipelines. Automation is another key focus area, where participants will create data pipelines with Apache Airflow while ensuring data quality and tracking lineage. The fundamentals of big data are addressed through topics like HDFS, MapReduce in Hadoop, and working with various Hadoop ecosystem components such as Hive and HBase. Finally, the course covers working with Cassandra and other common NoSQL databases like Riak, Redis, Neo4j, and Elasticsearch. It concludes with an introduction to MapReduce architecture, detailing its phases and benefits.