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If you’re looking to elevate your data engineering skills, the Data Engineering Master Course on Udemy is an exceptional choice. This course offers an in-depth exploration of essential big data tools and technologies such as Hadoop, Spark, Kafka, Flume, and MongoDB. It begins with foundational topics like Hadoop Distributed File System (HDFS) and Hadoop commands, making it accessible for beginners. The course then dives into practical data migration techniques using Sqoop, covering data import/export from MySQL to HDFS, Hive, and vice versa, with detailed instructions on handling different file formats, compression, and query optimization.
Moving forward, you’ll learn about Apache Flume, focusing on data ingestion from sources like Twitter and Netcat, with a comprehensive understanding of Flume architecture, interceptors, and multi-agent setups. The course also guides you through Apache Hive, covering table management, file formats like Parquet and Avro, and analytical functions, enabling efficient data querying and analysis.
A significant portion is dedicated to Apache Spark, introducing its architecture, data frames, SQL integration, and real-world applications like working with Cassandra and running Spark on cloud platforms such as EMR. The Kafka module provides insights into message brokers, including producer and consumer setups, message architecture, and data ingestion via Kafka connectors. Finally, the course explores MongoDB, focusing on CRUD operations, operators, working with arrays, and integrating with Spark for seamless data workflows.
Throughout the course, practical examples and interview preparation questions equip you with the skills needed for real-world data engineering roles. Whether you’re a budding data engineer or looking to expand your existing expertise, this course is a comprehensive resource that covers all critical aspects of modern data engineering.
Enroll Course: https://www.udemy.com/course/big-data-ingestion-using-sqoop-and-flume-cca-and-hdpcd/