DS220: Data Modeling with DataStax Enterprise

Description: The Cassandra data model looks similar to the legacy relational model, but is different in important ways. This course contains a thorough treatment of the Cassandra data model, and presents the Chebotko method of translating a real-world domain model into a running Cassandra schema. The data modeling techniques presented in this course are essential to a successful DataStax Enterprise deployment.
Length: 3 days online/2 days in person
Prerequisites: Completion of the Apache Cassandra: Core Concepts, Skills, and Tools course, or equivalent practical experience with Apache Cassandra.
Audience: Data architects, database designers, database administrators and database developers seeking to gain proficiency in data modeling and schema design for Apache Cassandra.
Environment: Virtual Machine pre-configured with Apache Cassandra, related tools, and exercise files.

Learning Objectives

Review of the Cassandra Data Model and CQL

  • Review CQL tables
  • Review CQL Data Definition Language
  • Review CQL querying capabilities

Conceptual Data Modeling

  • Overview conceptual data modeling techniques
  • Understand entity-relationship model

Logical Data Modeling

  • Introduce Chebotko Diagrams
  • Understand Cassandra data modeling principles
  • Introduce query-driven data modeling methodology
  • Master mapping rules
  • Master mapping patterns

Analysis and Validation of Logical Design

  • Review logical design analysis
  • Understand partition size limitations
  • Understand the cost of data redundancy and data consistency
  • Understand the cost of application-side joins and referential integrity constraints
  • Describe considerations for transactions and data aggregates

Physical Data Modeling and Optimization Techniques

  • Describe key design techniques
  • Describe table design optimizations
  • Understand secondary index use cases
  • Understand techniques for concurrent access to data

Selected Use Cases

  • Describe common Cassandra use cases
  • Model sensor data
  • Model messaging data

For more information, contact us.