Scala for Big Data

Description

Scala and Apache Spark are a common pairing for processing large datasets, and this course covers both in fourteen PDF lessons. It begins with getting started and the functional programming ideas Scala relies on, then moves to Spark fundamentals and RDDs.

Middle lessons deal with key-value pairs on distributed data, reduction operations, partitioning, and cutting down data shuffling and network traffic. You then read about Spark SQL, DataFrames and Datasets, performance tuning, building data pipelines and connecting to cloud platforms. A capstone lesson puts an end-to-end data solution together, and the PDFs contain many exercises.

Who it is for: developers and data engineers who want to learn Scala for Spark-based data processing.

What’s included
14 PDF lessons · 136 pages · about 30,500 words
One ZIP download that is yours to keep — read on any device, no subscription or app needed.

Course programme

1. Getting Started with Scala for Big Data
2. Understanding Functional Programming Concepts
3. Apache Spark Fundamentals and RDDs
4. Working with Distributed Key-Value Pairs
5. Data Reduction Operations and Transformations
6. Partitioning Strategies for Performance
7. Optimizing Data Shuffling and Network Communication
8. Spark SQL and Structured Data Processing
9. DataFrames and Datasets for Efficient Analysis
10. Real-world Big Data Processing Projects
11. Performance Tuning and Best Practices
12. Building Scalable Data Pipelines
13. Integration with Cloud Platforms
14. Capstone Project: End-to-End Data Solution