Mastering Scala with Apache Spark for the Modern Data Enterprise (TTSK7520)

5 Days

30 CPD hours

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.

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.

Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems.

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

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

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

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.

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

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

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

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

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.