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.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

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.

1

  • INTRODUCTION TO SCALA

  • Brief history and motivation

    Differences between Scala and Java

    Basic Scala syntax and constructs

    Scala's functional programming features

2

  • INTRODUCTION TO APACHE SPARK

  • Overview and history

    Spark components and architecture

    Spark ecosystem

    Comparing Spark with other big data frameworks

3

  • BASICS OF SPARK PROGRAMMING SPARKCONTEXT AND SPARKSESSION

  • Resilient Distributed Datasets (RDDs)

    Transformations and Actions

    Working with DataFrames

4

  • 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

5

  • BASIC RDD OPERATIONS

  • Creating and manipulating RDDs

    Common transformations and actions on RDDs

    Working with key-value data

6

  • BASIC DATAFRAME AND DATASET OPERATIONS

  • Creating and manipulating DataFrames and Datasets

    Column operations and functions

    Filtering, sorting, and aggregating data

7

  • INTRODUCTION TO SPARK STREAMING

  • Overview of Spark Streaming

    Discretized Stream (DStream) operations

    Windowed operations and stateful processing

8

  • PERFORMANCE OPTIMIZATION BASICS

  • Best practices for efficient Spark code

    Broadcast variables and accumulators

    Monitoring Spark applications

9

  • 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.

10

  • INTRODUCTION TO MACHINE LEARNING BASICS

  • Overview of machine learning

    Supervised and unsupervised learning

    Common algorithms and use cases

11

  • INTRODUCTION TO SPARK MLLIB

  • Overview of Spark MLlib

    MLlib's algorithms and utilities

    Data preparation and feature extraction

12

  • LINEAR REGRESSION AND CLASSIFICATION

  • Linear regression algorithm

    Logistic regression for classification

    Model evaluation and performance metrics

13

  • CLUSTERING ALGORITHMS

  • Overview of clustering algorithms

    K-means clustering

    Model evaluation and performance metrics

14

  • COLLABORATIVE FILTERING AND RECOMMENDATION SYSTEMS

  • Overview of recommendation systems

    Collaborative filtering techniques

    Implementing recommendations with Spark MLlib

15

  • 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

16

  • 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

17

  • INTRODUCTION TO SPARK NLP

  • Overview of Spark NLP Preprocessing text data

    Text classification and sentiment analysis

18

  • 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.

Audience

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.

Language

English

Prerequisites

We offer a wide variety of follow-on courses for next-level Spark, Scala, programming, AI / Generative AI / GPT, LLMs, machine learning, deep learning, data science skills and more. Please see our AI & Machine Learning Courses, Learning Journeys & Skills Roadmaps for options based on your specific role and goals.

$2,695

Length: 5.0 days (40 hours)

Level:

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