An automated big data modeling tool for Apache Cassandra that dramatically simplifies and streamlines database design.

KDM Intro

Using KDM for automated data modeling in Cassandra to support a digital library portal:

Online Retail Data

Using KDM for data modeling of online retail data (e.g., ebay). More complex data model with several advanced features:

Specifying Access Patterns

A simple data modeling example, explained in greater detail. Demonstrates, among other things, how to specify access patterns in KDM:

KDM follows the Chebotko data modeling methodology for Cassandra and ensures logically correct schema design.

Why use KDM?

The Kashliev Data Modeler (KDM) is a powerful big data modeling tool that automates schema design for Apache Cassandra, a distributed NoSQL database. KDM employs a novel query-driven approach to database design that significantly differs from the traditional methodology used with relational databases. Using its intuitive web-based GUI, KDM brings the user through the entire data modeling cycle, that starts with a conceptual data model and data access patterns, and ends with a physical data model, or a database schema. KDM automates the most complex, error-prone, and time-consuming data modeling tasks: conceptual-to-logical mapping, logical-to-physical mapping, physical optimization, and CQL script generation. KDM dramatically reduces time, simplifies, and streamlines Cassandra database design.