Working in a hands-on learning environment led by our expert instructor you'll:  

• Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. 
• Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. 
• Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. 
• Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. 
• Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. 
• Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. 

If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals. 

This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs.  

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We'll work with you to tune this course and level of coverage to target the skills you need most. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience skill level, interests and participation. 

Getting Started with Scala and Spark  

Introduction to Scala  

• Brief history and motivation 
• Differences between Scala and Java 
• Basic Scala syntax and constructs 
• Scala's functional programming features 

Introduction to Apache Spark  

• Overview and history 
• Spark components and architecture 
• Spark ecosystem 
• Comparing Spark with other big data frameworks 
• Lab: Practice basic Scala syntax and functional programming concepts using the REPL.  
• Setting up the Development Environment  

Basics of Spark Programming SparkContext and SparkSession 

• Resilient Distributed Datasets (RDDs) 
• Transformations and Actions 
• Working with DataFrames 

Spark SQL and Data Sources  

• Spark SQL library and its advantages 
• Structured and semi-structured data sources 
• Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) 
• Data manipulation using SQL queries 
• Lab: Setting up the Environment and Running a Simple Spark Application.  
• Lab: Load and query data from different data sources using Spark SQL. 

Data Processing and Spark Programming 

Basic RDD Operations  

• Creating and manipulating RDDs 
• Common transformations and actions on RDDs 
• Working with key-value data 

Basic DataFrame and Dataset Operations 

• Creating and manipulating DataFrames and Datasets 
• Column operations and functions 
• Filtering, sorting, and aggregating data 
• Lab: RDD and DataFrame Operations 

Introduction to Spark Streaming  

• Overview of Spark Streaming 
• Discretized Stream (DStream) operations 
• Windowed operations and stateful processing  

Performance Optimization Basics 

• Best practices for efficient Spark code 
• Broadcast variables and accumulators 
• Monitoring Spark applications 

Integrating External Libraries and Tools, Spark Streaming 

• Using popular external libraries, such as Hadoop and HBase 
• Integrating with cloud platforms: AWS, Azure, GCP 
• Connecting to data storage systems: HDFS, S3, Cassandra, etc. 
• Lab: Building an End-to-End Spark Application: Create a Spark application to process a large dataset, perform aggregations, and save the results.  
• Lab: Implement a simple Spark Streaming application to process real-time data.  

Machine Learning Basics with Spark MLlib  

Introduction to Machine Learning Basics 

• Overview of machine learning 
• Supervised and unsupervised learning 
• Common algorithms and use cases 

Introduction to Spark MLlib  

• Overview of Spark MLlib 
• MLlib's algorithms and utilities 
• Data preparation and feature extraction 
• Lab: Data Preparation with Spark MLlib  

Linear Regression and Classification  

• Linear regression algorithm 
• Logistic regression for classification 
• Model evaluation and performance metrics 

Clustering Algorithms 

• Overview of clustering algorithms 
• K-means clustering 
• Model evaluation and performance metrics 

Collaborative Filtering and Recommendation Systems 

• Overview of recommendation systems 
• Collaborative filtering techniques 
• Implementing recommendations with Spark MLlib 
• Lab: Basic Machine Learning with Spark MLlib 
• Lab: Implementing a Recommendation System  

Graph Processing and Generative AI Technologies  

Introduction to Graph Processing 

• Overview of graph processing 
• Use cases and applications of graph processing 
• Graph representations and operations 
• Introduction to Spark GraphX  
• Overview of GraphX 
• Creating and transforming graphs 
• Graph algorithms in GraphX 
• Lab: Graph Processing with GraphX  

Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala  

• Overview of generative AI technologies 
• Integrating GPT with Spark and Scala 
• Practical applications and use cases 

Bonus Topics / Time Permitting 

Introduction to Spark NLP  

• Overview of Spark NLP 
• Preprocessing text data 
• Text classification and sentiment analysis 
• Lab: Generative AI Technologies and Spark NLP: Integrate GPT for text generation in a Spark application and explore basic NLP tasks using Spark NLP.  
• Lab: Text Classification with Spark NLP  

Putting It All Together 

• Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.