This "skills-centric" course is about 50% hands-on lab and 50% lecture, designed to train attendees in core big data/ Spark development and use skills, coupling the most current, effective techniques with the soundest industry practices. Throughout the course students will be led through a series of progressively advanced topics, where each topic consists of lecture, group discussion, comprehensive hands-on lab exercises, and lab review.  

This course provides indoctrination in the practical use of the umbrella of technologies that are on the leading edge of data science development focused on Spark and related tools. Working in a hands-on learning environment, students will explore: 

• Spark Ecosystem  
• Spark Shell  
• Spark Data structures (RDD, DataFrame, Dataset)  
• Spark SQL  
• Modern data formats and Spark  
• Spark API  
• Spark & Hadoop & Hive  
• Spark ML overview  
• GraphX  
• Time-permitting: Spark Streaming  
• Time-permitting: Optional Capstone Workshop (Time-Permitting)  

Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate. We offer additional programming, Python / R, Spark, AI/Machine Learning/Deep Learning, data science, analytics and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals. 

This foundation-level course is geared for intermediate skilled, experienced Developers and Architects (with basic Python experience) who seek to be proficient in advanced, modern development skills working with Apache Spark in an enterprise data environment. 

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

Spark Introduction 

• Big data, Hadoop, Spark 
• Spark concepts and architecture 
• Spark components overview 
• Labs: installing and running Spark 

The first look at Spark 

• Spark shell 
• Spark web UIs 
• Analyzing dataset – part 1 
• Labs: Spark shell exploration 

Spark Data structures 

• Partitions 
• Distributed execution 
• Operations: transformations and actions 
• Labs: Unstructured data analytics using RDDs 

Caching 

• Caching overview 
• Various caching mechanisms available in Spark 
• In memory file systems 
• Caching use cases and best practices 
• Labs: Benchmark of caching performance 

DataFrames and Datasets 

• DataFrames Intro 
• Loading structured data (JSON, CSV) using DataFrames 
• Using schema 
• Specifying schema for DataFrames 
• Labs: DataFrames, Datasets, Schema 

Spark SQL 

• Spark SQL concepts and overview 
• Defining tables and importing datasets 
• Querying data using SQL 
• Handling various storage formats: JSON, Parquet, ORC 
• Labs: querying structured data using SQL; evaluating data formats 

Spark and Hadoop 

• Hadoop Primer: HDFS, YARN 
• Hadoop + Spark architecture 
• Running Spark on Hadoop YARN 
• Processing HDFS files using Spark 
• Spark & Hive 

Spark API 

• Overview of Spark APIs in Scala / Python 
• The lifecycle of a Spark application 
• Spark APIs 
• Deploying Spark applications on YARN 
• Labs: Developing and deploying a Spark application 

Spark ML Overview 

• Machine Learning primer 
• Machine Learning in Spark: MLib / ML 
• Spark ML overview (newer Spark2 version) 
• Algorithms overview: Clustering, Classifications, Recommendations 
• Labs: Writing ML applications in Spark 

GraphX 

• GraphX library overview 
• GraphX APIs 
• Create a Graph and navigating it 
• Shortest distance 
• Pregel API 
• Labs: Processing graph data using Spark 

Time Permitting Topics 

Spark Streaming 

• Streaming concepts 
• Evaluating Streaming platforms 
• Spark streaming library overview 
• Streaming operations 
• Sliding window operations 
• Structured Streaming 
• Continuous streaming 
• Spark & Kafka streaming 
• Labs: Writing spark streaming applications 

Workshop 

• Attendees will work on solving real-world data analysis problems using Spark