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Building Apache Cassandra Databases

Duration: 3 Days
Course Price: $2,650

The large volume and variety of data that today's businesses process require the need for a highly available, low latency database. Apache Cassandra provides this solution by permitting high-speed reads and writes across a replicated, distributed system. This Apache Cassandra training course provides data modeling experience to take advantage of the linearly scalable peer-to-peer design of Cassandra.

You Will Learn How To

  • Architect Cassandra databases and implement commonly used design patterns
  • Model data in Cassandra based on query patterns
  • Access Cassandra databases using CQL and Java
  • Create a balance between read/write speed and data consistency
  • Integrate Cassandra with Hadoop, Pig, and Hive

The large volume and variety of data that today's businesses process require the need for a highly available, low latency database. Apache Cassandra provides this solution by permitting high-speed reads and writes across a replicated, distributed system. This Apache Cassandra training course provides data modeling experience to take advantage of the linearly scalable peer-to-peer design of Cassandra.

You Will Learn How To

  • Architect Cassandra databases and implement commonly used design patterns
  • Model data in Cassandra based on query patterns
  • Access Cassandra databases using CQL and Java
  • Create a balance between read/write speed and data consistency
  • Integrate Cassandra with Hadoop, Pig, and Hive

Recommended Experience:

  • Knowledge of databases and SQL
  • Java programming

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
  • Organizing data with keyspaces, tables and columns
  • Creating collections and counters

Modeling 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 materialized views and time series

  • Modeling time series data
  • Enhancing queries with materialized views
  • Materialized views maintained in the application
  • Driving analytics from materialized views

Managing triggers

  • Creating triggers by implementing ITrigger
  • Attaching triggers to tables
  • Supporting materialized 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
  • Utilizing the Cassandra Loader to create Pig relations
  • Converting a Cassandra table to a Hive table with the Casssandra serializer/deserializer (SerDe)
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