Big Data Engineering Full Course Part 1 | 17 Hours

Introduction

This tutorial aims to provide a comprehensive guide on Big Data Engineering, covering essential concepts and practical applications. Based on the "Big Data Engineering Full Course Part 1" from The Data Tech, you'll learn about key components of Big Data, including Hadoop, Hive, and Spark, along with their installation and usage.

Step 1: Understand Big Data Concepts

• Definition of Big Data: Familiarize yourself with what constitutes Big Data, including its characteristics (volume, velocity, variety, veracity, and value).
• Importance: Recognize the significance of Big Data in decision-making and analytics across various industries.

Step 2: Explore the Big Data Engineering Road Map

• Pathway Overview: Review the Big Data Engineering road map to understand the skills and tools required for a career in this field.
• Key Areas to Focus:
  • Data storage solutions (e.g., Hadoop, NoSQL)
  • Data processing frameworks (e.g., Spark, MapReduce)
  • Data integration tools (e.g., Apache Kafka, Sqoop)

Step 3: Set Up Hadoop

• Hadoop Overview: Learn about the Hadoop ecosystem and its components, specifically HDFS (Hadoop Distributed File System).
• Installation:
  • Follow the Hadoop Single-Node Installation Steps.
  • Consider advanced setups with Hadoop Multi-Node Cluster Installation.

Step 4: Manage Data with HDFS

• Understanding HDFS: Explore how HDFS stores large files across clusters.
• Usage: Learn to manage quotas and understand use cases for HDFS to optimize data storage.

Step 5: Learn MapReduce

• Concept: Understand the MapReduce paradigm for processing large data sets.
• Implementation: Review examples and best practices to implement MapReduce effectively.

Step 6: Introduction to Apache Hive

• What is Hive?: Understand Apache Hive as a data warehousing solution built on top of Hadoop.
• Installation:
  • Follow installation steps for Apache Hive 2 with MySQL.

Step 7: Work with Hive SQL

• Basic Commands: Learn to create, load, insert, and show tables in Hive.
• Understand Table Types:
  • Internal vs External Tables: Know the differences and when to use each.
  • Partitions: Learn about static vs dynamic partitioning in Hive.

Step 8: Optimize Data Storage with Buckets

• Bucketing in Hive: Understand how to use buckets for better data organization.
• Deciding Bucket Count: Review methods to determine the right bucket count based on your data.

Step 9: Explore Hive File Formats

• File Format Comparison: Learn the differences between Hive ORC and TextFile formats.
• ACID Tables: Understand how to implement ACID transactions in Hive.

Step 10: Introduction to Apache Spark

• Overview of Spark: Get acquainted with Apache Spark and its capabilities in Big Data processing.
• Installation: Follow the installation guide for Apache Spark.

Step 11: Implement Spark Programs

• Example Program: Write a simple word count program in Spark using Scala.

val textFile = sc.textFile("hdfs://path/to/file.txt")
val counts = textFile.flatMap(line => line.split(" "))
              .map(word => (word, 1))
              .reduceByKey(_ + _)
counts.saveAsTextFile("hdfs://path/to/output")

Conclusion

This tutorial provided a structured approach to understanding and implementing key Big Data Engineering concepts and tools. You learned about Hadoop setup, Hive SQL commands, and Spark programming. To deepen your knowledge, consider exploring Part 2 of the course and the additional resources linked throughout this guide. Happy learning!