Apache Cassandra 2.x Core Internals

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Overview

This is a fast-paced, vendor agnostic, technical course that focuses on the key aspects of the technology for developers and system operations staff, covering core internal and distributed architecture fundamentals of Apache Cassandra, including Spark / Cassandra integration.

Duration

3 days

Who is the course for

Developers, System Operations staff, Software Engineers, Data Scientists, Network Engineers, Technology Managers.

Prerequisites

No prior knowledge of databases or programming is assumed, although having some basic experience with relational/SQL databases and Java will help.

What you will learn

  • Identifying the correct use cases for Cassandra
  • Introducing attendees to the core concepts of the distributed architecture of the Cassandra database
  • Deep diving into the internal architecture of the read/write paths of Cassandra: bloom filters, block indexes, commit-log, memtables, sstables, compaction, etc.
  • The fundamentals of how to write Java code to interact with Cassandra
  • Data modelling using CQL using the newest features of Cassandra 2.x and how to apply these concepts to build real applications on top of Cassandra
  • Spark/Cassandra integration

Course Outline

Introduction to Cassandra and its Architecture

  • NoSQL ecosystem overview
  • Review of Database families and data models
  • Cassandra origins: Amazon Dynamo, Google BigTable and Cassandra at Facebook
  • Cassandra use cases
  • Cassandra ecosystems and distributions
  • Cassandra distributed architecture fundamentals
  • Cassandra configuration

Cassandra Storage Internals and Data Model

  • Introduction to LSM-tree
  • Implementation details: bloom filters, in-memory caching, compression, off-heap data structures
  • Detailed study of the read/write path
  • JVM tuning and troubleshooting

Data Modelling using CQL in Cassandra

  • CQL language fundamentals
  • CQL use cases
  • Mapping between logical CQL data model and internal low level storage engine
  • CQL collections
  • Atomic batches

Spark/Cassandra Integration

  • Spark Overview – Introduction – What is Spark?
  • Spark compared to Hadoop
  • Use Case Showcase (Teradata / Netflix)
  • Architecture related to Cassandra (HA Cassandra via DSE etc.)
  • Why? Topic will give an intro to Spark and why it works well with Cassandra. A high level overview of how the two technologies work well with each other will help developers understand why Spark and Cassandra were designed in such a way that allows them to scale horizontally.

Spark Integration

  • Accessing Cassandra from a Spark application
  • Configuration (Specific to Cassandra)
  • Why? The above topic will showcase code and a guide as to how a Java / Scala (yet to be decided) application can connect to Cassandra and access the scalable storage system.

Spark Best Practices

  • When to use Spark
  • Common Anti-patterns
  • Why? The topic will show some patterns that can help avoid hindering performance as well as important configuration settings to allow users to squeeze the maximum performance from their hardware.

Hands-on Labs

  • Each student will be given a 3 node Cassandra cluster in Rackspace to run through the hands-on labs
  • Lab 1: Install Cassandra 2.x on a single node in the cloud
  • Lab 2: Run Cassandra commands and explore operations management concepts
  • Lab 3: Grow the cluster size to 3 nodes
  • Lab 4: Advanced Cassandra commands
  • Lab 5: Java API lab
  • Lab 6: Advanced Java API lab
  • Lab 7: Spark Integration lab

Format

  • 50% Lectures
  • 50% Labs

Additional Information

The course content can be customised to cover any specialised material you may require for your specific training needs.

For more information, please contact us at [email protected]

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september

11 - 13septembersep 119:00 amsep 13Apache Cassandra 2.x Core InternalsLondon, UK9:00 am - 5:00 pm (13) GMT QA International House

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