Data Engineering Master Course: Spark/Hadoop/Kafka/MongoDB

Master Data Engineering with Hadoop, Spark, Kafka, and MongoDB

This comprehensive course equips you with the skills to build robust data pipelines and gain expertise in leading big data technologies.

Hadoop Distributed File System (HDFS) & Sqoop

• HDFS Fundamentals: Begin by understanding the core concepts of the Hadoop Distributed File System (HDFS) and essential Hadoop commands for efficient file system management.
• Sqoop Import: Learn to seamlessly migrate data using Sqoop Import. Understand the data migration lifecycle and effectively transfer data from MySQL to HDFS and MySQL to Hive.
• Data Import Techniques: Master various techniques including handling different file formats, compression, file delimiters, WHERE clauses, and complex queries during data import. Explore split-by and boundary queries for optimized data extraction.
• Incremental Data Migration: Implement incremental mode for efficient and continuous data migration from MySQL to HDFS.
• Sqoop Export: Discover Sqoop Export for migrating data. Learn to move data from HDFS to MySQL and from Hive to MySQL.

Apache Flume: Data Ingestion & Streaming

• Flume Architecture: Gain a deep understanding of the Apache Flume architecture.
• Data Ingestion with Flume: Ingest data from diverse sources like Twitter, Netcat, and exec and store it in HDFS using Flume.
• Flume Interceptors: Learn about Flume interceptors and how to customize data flow using practical examples.
• Flume Agents: Explore Flume multiple agents and Flume consolidation strategies for scalable data ingestion.

Apache Hive: Data Warehousing & Analysis

• Hive Fundamentals: Get introduced to Apache Hive, including external and managed tables.
• File Format Handling: Work with various file formats like Parquet and Avro within Hive.
• Data Compression: Utilize compression techniques to optimize storage and performance.
• Hive Functions: Leverage powerful Hive string functions and Hive date functions for data manipulation.
• Data Organization: Implement partitioning and bucketing strategies for efficient data querying.

Apache Spark: Big Data Processing & Analytics

• Spark Introduction: Start with a comprehensive introduction to Apache Spark and its core components.
• Spark Architecture: Understand the cluster overview, RDDs, DAGs, stages, and tasks within Spark.
• Transformations & Actions: Master transformation and action examples for data manipulation.
• Spark DataFrames: Work with Spark DataFrames and understand how to handle different file formats and compression. Explore the DataFrames API's.
• Spark SQL: Utilize Spark SQL for querying structured data.
• Spark Integration: Learn to integrate Spark with Cassandra.
• Spark Deployment: Run Spark on IntelliJ IDEA and EMR (Elastic MapReduce).

Apache Kafka: Real-time Data Streaming

• Kafka Architecture: Explore the Kafka architecture and its key components.
• Kafka Fundamentals: Understand partitions and offsets, Kafka producers and consumers, and Kafka SerDes.
• Kafka Messages: Learn about Kafka messages and how to handle them effectively.
• Kafka Connect: Utilize Kafka Connect for data integration.
• Data Ingestion with Kafka Connect: Ingest data using Kafka Connect.

MongoDB: NoSQL Database

• MongoDB Use Cases: Discover common MongoDB use cases.
• CRUD Operations: Perform CRUD (Create, Read, Update, Delete) operations in MongoDB.
• MongoDB Operators: Utilize various MongoDB operators for data manipulation.
• Working with Arrays: Learn how to effectively work with arrays in MongoDB.
• MongoDB with Spark: Integrate MongoDB with Spark for data processing.

Data Engineering Interview Preparation

• Interview Questions: Prepare for data engineering interviews with targeted Sqoop interview questions, Hive interview questions, Spark interview questions, and common data engineering questions.
• Real-world Projects: Gain insights into data engineering real project questions.