Introduction
Data engineering is at the heart of Big Data solutions, and mastering tools like Hadoop and Apache Spark is essential for modern data engineers. This course offers an in-depth exploration of data engineering concepts with a focus on Cloudera’s platform, covering Apache Hadoop for distributed storage and processing, as well as Apache Spark for real-time data processing and analytics. You’ll gain the knowledge needed to build, optimize, and scale data pipelines in a distributed environment. By the end of the course, you will be proficient in designing and managing robust, high-performance data engineering solutions using Hadoop and Spark.

Prerequisites

1. Basic understanding of Big Data concepts and technologies.
2. Familiarity with programming languages (preferably Python, Java, or Scala).
3. Basic knowledge of SQL and data modeling.
4. No prior experience with Cloudera or Spark is required but is beneficial.

Table of Contents

1. Introduction to Data Engineering with Cloudera
   1.1 What is Data Engineering?
   1.2 Key Components of the Cloudera Platform
   1.3 Overview of Hadoop and Spark in Data Engineering
   1.4 Setting Up a Cloudera Environment for Data Engineering
2. Overview of Hadoop for Data Engineering
   2.1 Hadoop Architecture and Core Components
   2.2 Understanding HDFS for Data Storage
   2.3 Distributed Computing with MapReduce
   2.4 Using YARN for Resource Management
   2.5 Data Processing Models in Hadoop
3. Introduction to Apache Spark
   3.1 What is Apache Spark?
   3.2 Spark’s Architecture and Ecosystem
   3.3 Differences Between Spark and Hadoop MapReduce
   3.4 Setting Up Spark on Cloudera’s Platform
   3.5 Spark DataFrames and RDDs: Understanding the Core Concepts
4. Data Ingestion with Hadoop and Spark
   4.1 Ingesting Data into HDFS: Tools and Best Practices
   4.2 Working with Flume, Kafka, and Sqoop for Data Ingestion
   4.3 Data Streaming with Apache Kafka
   4.4 Spark Streaming for Real-Time Data Ingestion and Processing
   4.5 Handling Structured and Unstructured Data
5. Data Transformation and Processing with Apache Spark
   5.1 Working with RDDs and DataFrames for Data Transformation
   5.2 Using Spark SQL for Querying Data
   5.3 Advanced Data Transformation Techniques in Spark
   5.4 Using Spark MLlib for Machine Learning Pipelines
   5.5 Handling Joins, Aggregations, and Window Functions in Spark
6. Optimizing Spark for Performance
   6.1 Understanding Spark’s Execution Engine(Ref: Cloudera Essentials: Introduction to Big Data and Hadoop)
   6.2 Tuning Spark for Better Performance
   6.3 Caching and Persistence in Spark
   6.4 Partitioning Data for Optimal Performance
   6.5 Debugging and Troubleshooting Spark Applications
7. Data Pipelines with Apache NiFi
   7.1 Introduction to Apache NiFi for Data Flow Automation
   7.2 Creating and Managing Data Pipelines in NiFi
   7.3 Integrating NiFi with Hadoop and Spark
   7.4 Using NiFi for Data Ingestion, Transformation, and Routing
   7.5 Best Practices for Managing Data Pipelines
8. Batch Processing vs. Stream Processing
   8.1 Differences Between Batch and Stream Processing
   8.2 When to Use Batch vs. Stream Processing in Data Engineering
   8.3 Apache Spark for Batch Processing
   8.4 Spark Streaming for Real-Time Stream Processing
   8.5 Combining Batch and Stream Processing for Hybrid Workflows
9. Data Storage Solutions in Hadoop and Spark
   9.1 Working with HDFS for Scalable Data Storage
   9.2 Managing Large Datasets and Data Lakes in Hadoop
   9.3 Integrating Apache HBase for NoSQL Data Storage
   9.4 Using Parquet and ORC for Efficient Columnar Storage
   9.5 Data Compression and File Formats for Performance
10. Big Data Analytics with Apache Spark
    10.1 Advanced Analytics in Spark
    10.2 Using Spark for Data Exploration and Visualization
    10.3 Implementing Predictive Analytics and Machine Learning in Spark
    10.4 Spark GraphX for Graph Analytics
    10.5 Real-Time Analytics with Spark Streaming
11. Data Security and Compliance in Cloudera
    11.1 Data Security in Hadoop and Spark
    11.2 Using Kerberos for Authentication in Hadoop
    11.3 Implementing Encryption and Data Masking
    11.4 Managing Access Control with Apache Ranger
    11.5 Compliance with GDPR and Other Regulations
12. Monitoring and Managing Data Pipelines
    12.1 Using Cloudera Manager for Cluster Monitoring
    12.2 Monitoring Spark Jobs and Performance
    12.3 Setting Up Alerts and Logging in Spark and Hadoop
    12.4 Troubleshooting Common Data Engineering Issues
    12.5 Best Practices for Data Pipeline Monitoring
13. Real-World Applications and Use Cases
    13.1 Data Engineering in E-Commerce: Personalization and Recommendations
    13.2 Real-Time Analytics in Financial Services
    13.3 Using Hadoop and Spark in Healthcare Data Processing
    13.4 Social Media Analytics with Big Data Tools
    13.5 Case Study: Building a Scalable Data Pipeline for an Online Retailer
14. Future Trends in Data Engineering
    14.1 The Role of AI and Machine Learning in Data Engineering
    14.2 The Evolution of Cloud-based Data Engineering Solutions
    14.3 Exploring Serverless Architectures for Big Data
    14.4 The Future of Spark and Hadoop in Data Engineering
    14.5 Preparing for the Next Generation of Data Engineering Tools

Conclusion
Mastering data engineering with Hadoop and Apache Spark is essential for building high-performance, scalable, and robust data pipelines in today’s data-driven world. This course has provided you with the core skills to navigate the Cloudera ecosystem, from data ingestion and transformation to real-time processing and analytics. By leveraging the full power of Hadoop and Spark, you are well-equipped to tackle complex data engineering challenges and help organizations unlock the potential of Big Data. As the field continues to evolve, staying up-to-date with new tools and techniques will keep you at the forefront of data engineering.

Reference