21 hours (usually 3 days including breaks)
familiarity with either Java / Scala / Python language (our labs in Scala and Python)
basic understanding of Linux development environment (command line navigation / editing files using VI or nano)
This course will introduce Apache Spark. The students will learn how Spark fits into the Big Data ecosystem, and how to use Spark for data analysis. The course covers Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning and graphX.
Developers / Data Analysts
- A quick introduction to Scala
- Labs : Getting know Scala
- Background and history
- Spark and Hadoop
- Spark concepts and architecture
- Spark eco system (core, spark sql, mlib, streaming)
- Labs : Installing and running Spark
First Look at Spark
- Running Spark in local mode
- Spark web UI
- Spark shell
- Analyzing dataset – part 1
- Inspecting RDDs
- Labs: Spark shell exploration
- RDDs concepts
- RDD Operations / transformations
- RDD types
- Key-Value pair RDDs
- MapReduce on RDD
- Caching and persistence
- Labs : creating & inspecting RDDs; Caching RDDs
Spark API programming
- Introduction to Spark API / RDD API
- Submitting the first program to Spark
- Debugging / logging
- Configuration properties
- Labs : Programming in Spark API, Submitting jobs
- SQL support in Spark
- Defining tables and importing datasets
- Querying data frames using SQL
- Storage formats : JSON / Parquet
- Labs : Creating and querying data frames; evaluating data formats
- MLlib intro
- MLlib algorithms
- Labs : Writing MLib applications
- GraphX library overview
- GraphX APIs
- Labs : Processing graph data using Spark
- Streaming overview
- Evaluating Streaming platforms
- Streaming operations
- Sliding window operations
- Labs : Writing spark streaming applications
Spark and Hadoop
- Hadoop Intro (HDFS / YARN)
- Hadoop + Spark architecture
- Running Spark on Hadoop YARN
- Processing HDFS files using Spark
Spark Performance and Tuning
- Broadcast variables
- Memory management & caching
- Deploying Spark in production
- Sample deployment templates
We know a lot more about the whole environment.
Richard was very willing to digress when we wanted to ask semi-related questions about things not on the syllabus. Explanations were clear and he was up front about caveats in any advice he gave us.
Richard is very calm and methodical, with an analytic insight - exactly the qualities needed to present this sort of course.
Kieran Mac Kenna
The trainer made the class interesting and entertaining which helps quite a bit with all day training.
Ernesto did a great job explaining the high level concepts of using Spark and its various modules.
Small group (4 trainees) and we could progress together. Also the trainer could so help everybody.
ICE International Copyright Enterprise Germany GmbH
Ajay was very friendly, helpful and also knowledgable about the topic he was discussing.
Biniam Guulay - ICE International Copyright Enterprise Germany GmbH
Doing similar exercises different ways really help understanding what each component (Hadoop/Spark, standalone/cluster) can do on its own and together. It gave me ideas on how I should test my application on my local machine when I develop vs when it is deployed on a cluster.
Thomas Carcaud - IT Frankfurt GmbH
share concept diagram and also sample for hands dirty
Mark Yang - FMR
Applicable scenarios and cases
zhaopeng liu - Fmr
all parts of this session
Eric Han - Fmr
I think the trainer had an excellent style of combining humor and real life stories to make the subjects at hand very approachable. I would highly recommend this professor in the future.