View SnappyData Row and Column Tables Together

SnappyData fuses Apache Spark with an in-memory store, mixing transactional row tables and analytical column tables on one cluster. DbSchema draws both on a single canvas and runs your Spark SQL from a desktop editor.

Row tables and column tables together on one diagram
Execute Spark SQL over JDBC — window functions, joins, aggregations
Snapshot the cluster schema as a shareable HTML report
Preview DDL before anything executes against the live cluster

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DbSchema visualizing a SnappyData cluster schema as an ER diagram

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A Schema Scattered Across
Notebooks and Spark Jobs

On a SnappyData cluster the schema moves fast: data engineers add row tables for ingestion here, column tables for scans there, and the resulting structure ends up recorded only inside the jobs that use it.

DbSchema reads the catalog over JDBC and puts the whole cluster schema on one canvas — table types, columns, and the relationships between them — visible before the next Spark job runs.

Read, Query & Document
a SnappyData Cluster

Desktop tooling for the data layer Spark writes into.

Load SnappyData row and column table definitions into a diagram

Row and Column Tables, One Canvas

Connect to the cluster and DbSchema loads row-store and column-store table definitions into a diagram that shows the data architecture at a glance.

Run Spark SQL against SnappyData from the DbSchema SQL editor

An Editor That Speaks Spark SQL

SnappyData's JDBC interface accepts Spark SQL — window functions, aggregations, joins across row and column tables — and DbSchema executes it interactively with results inline.

Generate HTML documentation from a SnappyData cluster schema

A Snapshot Reviewers Can Open

Produce a point-in-time HTML report of all tables, column definitions, and storage types — reviewable and shareable outside the cluster environment.

Cluster Versions Come and Go
Git Keeps the Schema's Story

The design model is a file in your repository. Commit it before an upgrade, diff it after, and every structural change between cluster versions has a reviewable history — the same branch-and-pull-request flow the rest of your code follows.

SnappyData schema tracked in Git through the DbSchema design model

Read Git Workflow Docs

SnappyData Connection Setup — JAR, Port 1527, URL

Fetch the SnappyData JDBC driver JAR from the SnappyData GitHub releases — matching the server version you run — and register it in DbSchema's driver manager; the driver class is io.snappydata.jdbc.ClientDriver. Then connect to the cluster host on port 1527, the JDBC server's default, with a URL of jdbc:snappydata://host:1527/.

The cluster's lead node must be running and the JDBC server component started before the connection succeeds. Setup notes are on the SnappyData JDBC driver page.