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Developing with Spark for Big Data (TTSK7505)

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Course Description:

Learn advanced Big Data and Spark skills to access disparate databases, integrate Machine Learning (ML), and establish streaming solutions.

Apache Spark is an important component in the Hadoop Ecosystem as a cluster computing engine used for Big Data. Building on top of the Hadoop YARN and HDFS ecosystem, Spark offers faster in-memory processing for computing tasks when compared to Map/Reduce. It can be programmed in Java, Scala, Python, and R along with SQL-based front-ends.

With advanced libraries like Mahout and MLib for Machine Learning, GraphX, or Neo4J for rich data graph processing, as well as access to other NoSQL data stores, Rule engines, and components, Spark is a lynchpin in modern Big Data and Data Science computing.

This course introduces you to enterprise-grade Spark programming and the components to craft complete data science solutions. You’ll learn core big data and Spark development techniques and industry practices. This course is offered in Java, and with some alterations, Python, Scala, and R.

Format

Instructor-Led
Topic

Length

Course Outline

Spark Overview

  • Hadoop Ecosystem
  • Hadoop YARN vs. Mesos
  • Spark vs. Map/Reduce
  • Spark with Map/Reduce: Lambda Architecture
  • Spark in the Enterprise Data Science Architecture

Spark Component Overview

  • Spark Shell
  • RDDs: Resilient Distributed Datasets
  • Data Frames
  • Spark 2 Unified DataFrames
  • Spark Sessions
  • Functional Programming
  • Spark SQL
  • MLib
  • Structured Streaming
  • Spark R
  • Spark and Python

RDDs: Resilient Distributed Datasets

  • Coding with RDDs
  • Transformations
  • Actions
  • Lazy Evaluation and Optimization
  • RDDs in Map/Reduce

DataFrames

  • RDDs vs. DataFrames
  • Unified Dataframes (UDF) in Spark 2.0
  • Partitioning

Spark Applications

  • Spark Sessions
  • Running Applications
  • Logging

DataFrame Persistence

  • RDD Persistence
  • DataFrame and Unified DataFrame Persistence

Spark Streaming

  • Streaming Overview
  • Streams
  • Structured Streaming
  • DStreams and Apache Kafka

Accessing NOSQL Data

  • Ingesting data
  • Parquet Files
  • Relational Databases
  • Graph Databases (Neo4J and GraphX)
  • Interacting with Hive
  • Accessing Cassandra Data
  • Document Databases (MongoDB and CouchDB)

Enterprise Integration

  • Map/Reduce and Lambda Integration
  • Camel Integration
  • Drools and Spark

Algorithms and Patterns

  • MLib and Mahout
  • Classification
  • Clustering
  • Decision Trees
  • Decompositions
  • Pipelines
  • Spark Packages

Spark SQL

  • Spark SQL
  • SQL and DataFrames
  • Spark SQL and Hive
  • Spark SQL and JDBC

GraphX

  • Graph APIs
  • GraphX
  • ETL in GraphX
  • Exploratory Analysis
  • Graph computation
  • Pregel API Overview
  • GraphX Algorithms
  • Neo4J as an alternative

Alternate Languages

  • Using Web Notebooks (Zeppelin and Jupyter)
  • R on Spark
  • Python on Spark
  • Scala on Spark

Clustering Spark for Developers

  • Parallelizing Spark Applications
  • Clustering concerns for Developers

Performance and Tuning

  • Monitoring Spark Performance
  • Tuning Memory
  • Tuning CPU
  • Tuning Data Locality
  • Troubleshooting

Target Audience

Experienced Developers and Architects who seek proficiency in working with Apache Spark in an enterprise data environment.

What You'll Learn

Join an engaging hands-on learning environment, where you’ll learn:

  • The essentials of Spark architecture and applications
  • How to execute Spark Programs
  • How to create and manipulate both RDDs (Resilient Distributed Datasets) and UDFs (Unified Data Frames)
  • How to persist and restore data frames
  • Essential NOSQL access
  • How to integrate machine learning into Spark applications
  • How to use Spark Streaming and Kafka to create streaming applications

Prerequisites

Before attending this course, you should have:

  • Java programming experience
  • Python programming experience
  • Basic understanding of SQL
  • Comfort with navigating the Linux command line
  • Basic knowledge of Linux editors (such as VI/nano) for editing code

Inclusions

With CCS Learning Academy, you’ll receive:

  • Instructor-led training
  • Training Seminar Student Handbook
  • Collaboration with classmates (not currently available for self-paced course)
  • Real-world learning activities and scenarios
  • Exam scheduling support*
  • Enjoy job placement assistance for the first 12 months after course completion.
  • This course is eligible for CCS Learning Academy’s Learn and Earn Program: get a tuition fee refund of up to 50% if you are placed in a job through CCS Global Tech’s Placement Division*
  • Government and Private pricing available.*

*For more details call: 858-208-4141 or email: training@ccslearningacademy.com; sales@ccslearningacademy.com

 

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