Building Apache Cassandra Databases Training

Cassandra Database Course Outline

• Introduction to Apache Cassandra

  NoSQL Overview

  • Justifying non-relational data stores
  • Listing the categories of NoSQL Data Stores

  Exploring Cassandra

  • Defining column family data stores
  • Surveying Cassandra
  • Dissecting the basic Cassandra architecture

  Querying Cassandra

  • Defining Cassandra Query Language, CQL
  • Enumerating CQL data types
  • Manipulating data from the cqlsh interface

• Representing Data in the Cassandra Data Model

  Leveraging Cassandra structures and types

  • Drawing comparisons with the relational model
  • Organising data with keyspaces, tables and columns
  • Creating collections and counters

  Modelling data based on queries

  • Designing tables around access patterns
  • Clustering with compound primary keys
  • Improving data distribution with composite partition Keys

• Configuring Data Consistency

  Detailing tunable consistency

  • Identifying consistency levels
  • Selecting appropriate read and write consistency levels
  • Distinguishing consistency repair features

  Balancing consistency and performance

  • Relating replication factor and consistency
  • Trading consistency for availability
  • Achieving linearizable consistency with Compare-And-Set

• Leveraging Cassandra Idioms and Programming Patterns

  Working with Cassandra collection types

  • Grouping elements in sets
  • Ordering elements in lists
  • Expressing relationships with maps
  • Nesting collections

  Storing data for easy retrieval

  • Mapping data to tuples and user defined types
  • Investigating the frozen keyword
  • Applying the Valueless Columns Pattern
  • Strategic implementation of clustering columns

  Controlling data life span

  • Expiring temporal data with time-to-live
  • Reviewing how tombstones achieve distributed deletes
  • Executing DELETEs and UPDATEs in the future

  Constructing materialised views and time series

  • Modelling time series data
  • Enhancing queries with materialised views
  • Materialised views maintained in the application
  • Driving analytics from materialised views

  Managing triggers

  • Creating triggers by implementing ITrigger
  • Attaching triggers to tables
  • Supporting materialised views with triggers

• Accessing Cassandra Programmatically

  Querying Cassandra data with the Datastax Java Driver

  • Connecting to a Cassandra cluster
  • Running CQL through the Java Driver
  • Batching prepared statements
  • Paginating large queries

  Persisting Java Objects with Kundera

  • Defining the Java Persistence Architecture, JPA
  • Configuring Kundera to work with Cassandra
  • Generating schemas automatically
  • Managing JPA transactions in Kundera

• Integrating Cassandra with Analytical Frameworks

  Leveraging built-in Cassandra connectors

  • Loading data into Hadoop MapReduce with the Cassandra InputFormat
  • Utilising the Cassandra Loader to create Pig relations
  • Converting a Cassandra table to a Hive table with the Casssandra serializer/deserializer (SerDe)