Description

Introduction
This course equips data engineers with the skills to leverage Scala and Apache Spark for building scalable, high-performance Big Data pipelines. Learners will explore functional programming concepts, advanced Spark features, real-time streaming, and integration with popular Big Data tools. By combining theory with hands-on exercises, participants will gain practical experience in designing and optimizing data workflows for large-scale datasets.

Prerequisites

• Basic knowledge of Scala and Java programming

• Understanding of SQL and relational data modeling

• Familiarity with Linux/Windows command-line operations

• Basic understanding of Big Data concepts and distributed systems

• Optional: prior exposure to Hadoop, Kafka, or Spark is helpful but not mandatory

Table of Contents

1. Scala Essentials for Data Engineering
 1.1 Functional Programming Concepts: Immutability, Pure Functions, Recursion
 1.2 Collections: Lists, Sets, Maps, and their transformations
 1.3 Tuples, Options, and Either for safer data handling
 1.4 Pattern Matching and Case Classes for structured data
 1.5 Higher-Order Functions, Lambdas, and Anonymous Functions
 1.6 Implicit Parameters and Conversions

2. Apache Spark Fundamentals
 2.1 Spark Architecture: Driver, Executors, and Cluster Managers
 2.2 RDDs: Creation, Transformations, Actions, and Persistence
 2.3 DataFrames and Datasets: Schema, Optimizations, and API usage
 2.4 Spark SQL: Querying, Joins, and Aggregations
 2.5 Handling Missing and Corrupt Data in Spark

3. Advanced Spark with Scala
 3.1 Partitioning, Shuffling, and Data Locality
 3.2 Caching and Persistence Strategies for Performance
 3.3 Broadcast Variables and Accumulators
 3.4 Performance Tuning: Memory, Serialization, and DAG Optimizations
 3.5 Debugging Spark Jobs and Error Handling

4. Big Data Processing and ETL
 4.1 Designing ETL Pipelines with Spark
 4.2 Batch Processing vs. Stream Processing
 4.3 Spark Streaming: DStreams, Structured Streaming, and Window Operations
 4.4 Integration with Kafka, HDFS, S3, and NoSQL Databases
 4.5 Handling Large-Scale Data Transformations and Aggregations

5. Functional Programming Patterns in Data Engineering
 5.1 Monads, Functors, and Option/Either Usage
 5.2 Error Handling and Data Validation Patterns
 5.3 Lazy Evaluation, Memoization, and Efficient Computations
 5.4 Combining FP with Spark for Clean and Scalable Pipelines

6. Real-Time Use Cases and Projects
 6.1 Building a Real-Time Analytics Pipeline
 6.2 Data Aggregation and Reporting Dashboards
 6.3 Predictive Analytics using Spark MLlib
 6.4 Project: End-to-End Big Data Pipeline with Scala & Spark
 6.5 Case Studies from Finance, E-commerce, and IoT

7. Best Practices and Conclusion
 7.1 Modular Code Design and Maintainability
 7.2 Testing Spark Jobs: Unit Testing and Integration Testing
 7.3 Optimizing for Scalability, Fault Tolerance, and Reliability
 7.4 Monitoring and Logging Spark Applications

By completing this course, learners will be proficient in building scalable, high-performance Big Data pipelines using Scala and Spark. They will understand advanced functional programming techniques, optimize Spark jobs, handle streaming and batch data, and implement real-world data engineering projects. This prepares participants to tackle complex Big Data challenges in professional environments.