Description

Duration

5 Days

30 CPD hours

This course is intended for

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.

Overview

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.

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

By Nexus Human

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