5.3 Release Notes

This page details the release notes for Cinchy v5.3

Table of Contents

For instructions on how to upgrade to the latest version of Cinchy, see here.

New Connector


We're continuing to improve our Connections offerings, and we now support Kafka as a data sync target in Connections.

Apache Kafka is an end-to-end event streaming platform that:

  • Publishes (writes) and subscribes to (reads) streams of events from sources like databases, cloud services, and software applications.

  • Stores these events durably and reliably for as long as you want.

  • Processes and reacts to the event streams in real-time and retrospectively.

Event streaming thus ensures a continuous flow and interpretation of data so that the right information is at the right place, at the right time for your key use cases.

For information on setting up data syncs with Kafka as a target, please review the documentation here.

New Inbound Data Format for Connections

Apache AVRO

We've also added support for Apache AVRO (inbound) as a data format and added integration with the Kafka Schema Registry, which helps enforce data governance within a Kafka architecture.

Avro is an open source data serialization system that helps with data exchange between systems, programming languages, and processing frameworks. Avro stores both the data definition and the data together in one message or file. Avro stores the data definition in JSON format making it easy to read and interpret; the data itself is stored in binary format making it compact and efficient.

Some of the benefits for using AVRO as a data format are:

  • It's compact;

  • It has a direct mapping to/from JSON;

  • It's fast;

  • It has bindings for a wide variety of programming languages.

For more about AVRO and Kafka, read the documentation here.

For information on configuring AVRO in your platform, review the documentation here.

Custom Results in the Network Map

Focus the results of your Network Map to show only the data that you really want to see with our new URL parameters.

You can now add Target Node, Depth Level, and Max Depth Level Parameters, if you choose.

Example: <base url>/apps/datanetworkvisualizer?targetNode=&maxDepth=&depthLevel=

  • Target Node: Using the Target Node parameter defines which of your nodes will be the central node from which all connections branch from.

    • Target Node uses the TableID number, which you can find in the URL of any table.

    • Example: <base url>/apps/datanetworkvisualizer?targetNode=8 will show TableID 8 as the central node

  • Max Depths: This parameter defines how many levels of network hierarchy you want to display.

    • Example: <base url>/apps/datanetworkvisualizer?maxDepth=2 will only show you two levels of connections.

  • Depth Level: Depth Level is a UI parameter that will highlight/focus on a certain depth of connections.

    • Example: <base url>/apps/datanetworkvisualizer?DepthLevel=1 will highlight all first level network connections, while the rest will appear muted.

The below example visualizer uses the following URL: <base url>/apps/datanetworkvisualizer?targetNode=8&maxDepth=2&depthLevel=1

  • It shows Table ID 8 ("Groups") as the central node.

  • It only displays the Max Depth of 2 connections from the central node.

  • It highlights the nodes that have a Depth Level of 1 from the central node.


  • We've increased the length of the [Parameters] field in the [Cinchy].[Execution Log] to 100,000 characters.

  • Two new parameters are now available to use in real time syncs that have a Cinchy Table as the target. @InsertedRecordIds() and @UpdatedRecordIds() can be used in post sync scripts to insert and update Record IDs respectively, with the desired value input as comma separated list format.

Bug Fixes

  • We've fixed a bug that was preventing some new SSO users belonging to existing active directory groups from seeing tables that they should have access to.

  • We've fixed a bug where a Webhook would return a 400 error if a JSON body was provided, and the key was in the query parameter of the HTTP request.


  • We continue to optimize our GraphQL beta capabilities by improving memory utilization and performance.

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