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.