Cinchy Platform Documentation
Cinchy v5.6
Cinchy v5.6
  • Data Collaboration Overview
  • Release Notes
    • Release Notes
      • 5.0 Release Notes
      • 5.1 Release Notes
      • 5.2 Release Notes
      • 5.3 Release Notes
      • 5.4 Release Notes
      • 5.5 Release Notes
      • 5.6 Release Notes
  • Getting Help
  • Cinchy Glossary
  • Frequently Asked Questions
  • Deployment Guide
    • Deployment Installation Guides
      • Deployment Planning Overview and Checklist
        • Deployment Architecture Overview
          • Kubernetes Deployment Architecture
          • IIS Deployment Architecture
        • Deployment Prerequisites
          • Single Sign-On (SSO) Integration
            • Enabling TLS 1.2
            • Configuring ADFS
            • AD Group Integration
      • Kubernetes Deployment Installation
        • Disabling your Kubernetes Applications
        • Changing your File Storage Configuration
        • Configuring AWS IAM for Connections
        • Using Self-Signed SSL Certs (Kubernetes Deployments)
        • Deploying the CLI (Kubernetes)
      • IIS Deployment Platform Installation
    • Upgrade Guides
      • Upgrading Cinchy Versions
        • Cinchy Upgrade Utility
        • Kubernetes Upgrades
          • v5.1 (Kubernetes)
          • v5.2 (Kubernetes)
          • v5.3 (Kubernetes)
          • v5.4 (Kubernetes)
          • v5.5 (Kubernetes)
          • v5.6 (Kubernetes)
          • Upgrading AWS EKS Kubernetes Version
          • Updating the Kubernetes Image Registry
          • Upgrading AKS (Azure Kubernetes Service)
        • IIS Upgrades
          • v4.21 (IIS)
          • v4.x to v5.x (IIS)
          • v5.1 (IIS)
          • v5.2 (IIS)
          • v5.3 (IIS)
          • v5.4 (IIS)
          • v5.5 (IIS)
          • v5.6 (IIS)
      • Upgrading from v4 to v5
  • Guides for Using Cinchy
    • User Guides
      • Overview of the Data Browser
      • The Admin Panel
      • User Preferences
        • Personal Access Tokens
      • Table Features
      • Data Management
      • Queries
      • Version Management
        • Versioning Best Practices
      • Commentary
    • Builder Guides
      • Best Practices
      • Creating Tables
        • Attaching Files
        • Columns
        • Data Controls
          • Data Entitlements and Access Controls
          • Data Erasure
          • Data Compression
        • Formatting Rules
        • Indexing and Partitioning
        • Linking Data
        • Table and Column GUIDs
        • System Tables
      • Deleting Tables
        • Restoring Tables, Columns, and Rows
      • Saved Queries
      • CinchyDXD Utility
        • Building the Data Experience (CinchyDXD)
        • Packaging the Data Experience (CinchyDXD)
        • Installing the Data Experience (CinchyDXD)
        • Updating the Data Experience (CinchyDXD)
        • Repackaging the Data Experience (CinchyDXD)
        • Reinstalling the Data Experience (CinchyDXD)
      • Multi-Lingual Support
      • Integration Guides
    • Administrator Guide
    • Additional Guides
      • Monitoring and Logging on Kubernetes
        • Grafana
        • Opensearch Dashboards
          • Setting up Alerts
        • Monitoring via ArgoCD
      • Maintenance
      • System Properties
      • Enable Data At Rest Encryption
      • MDQE
      • Application Experiences
        • Network Map
          • Custom Node Results
          • Custom Results in the Network Map
        • Setting Up Experiences
  • API Guide
    • API Overview
      • API Authentication
      • API Saved Queries
      • ExecuteCQL
      • Webhook Ingestion
  • CQL
    • The Basics of CQL
      • CQL Examples
      • CQL Functions Master List
      • CQL Statements Overview
        • Cinchy DML Statements
        • Cinchy DDL Statements
      • Cinchy Supported Functions
        • Cinchy Functions
        • Cinchy System Values
        • Cinchy User Defined Functions
          • Table-Valued Functions
          • Scalar-Valued Functions
        • Conversion Functions
        • Date and Time Types and Functions
          • Return System Date and Time Values
          • Return Date and Time Parts
          • Return Date and Time Values From Their Parts
          • Return Date and Time Difference Values
          • Modify Date and Time Values
          • Validate Date and Time Values
        • Logical Functions
        • Mathematical Functions
        • String Functions
        • Geometry and Geography Data Type and Functions
          • OGC Methods on Geometry & Geography Instances
          • Extended Methods on Geometry & Geography Instances
        • Full Text Search Functions
        • Connections Functions
        • JSON Functions
  • Meta Forms
    • Introduction to Meta-Forms
    • Meta-Forms Deployment Installation Guide
      • Deploying Meta-Forms (Kubernetes)
      • Deploying Meta-Forms (IIS)
    • Forms Data Types
    • Meta-Forms Builders Guides
      • Creating a Dynamic Meta-Form (Using Tables)
      • Creating a Dynamic Meta-Form Example (Using Form Designer)
      • Adding Links to a Form
      • Rich Text Editing in Forms
  • Data Syncs
    • Getting Started with Data Syncs
    • Installation & Maintenance
      • Prerequisites
      • Installing Connections
      • Installing the Worker/Listener
      • Installing the CLI and the Maintenance CLI
    • Building Data Syncs
      • Types of Data Syncs
      • Common Design Patterns
      • Sync Behaviour
      • Columns and Mappings
        • Calculated Column Examples
      • Listener Configuration
      • Advanced Settings
        • Filters
        • Parameters
        • Auth Requests
        • Request Headers
        • Post Sync Scripts
        • Pagination
      • Batch Data Sync Example
      • Real-Time Sync Example
      • Scheduling a Data Sync
      • Connection Functions
    • CLI Commands List
    • Error Logging and Troubleshooting
    • Supported Data Sync Sources
      • Cinchy Event Broker/CDC
        • Cinchy Event Broker/CDC XML Config Example
      • Cinchy Table
        • Cinchy Table XML Config Example
      • Cinchy Query
        • Cinchy Query XML Config Example
      • Copper
      • DB2 (Query and Table)
      • Dynamics 2015
      • Dynamics
      • DynamoDB
      • File Based Sources
        • Binary File
        • Delimited File
        • Excel
        • Fixed Width File
        • Parquet
      • Kafka Topic
        • Kafka Topic Example Config
        • Apache AVRO Data Format
      • LDAP
      • MongoDB Collection
        • MongoDB Collection Source Example
      • MongoDB Collection (Cinchy Event Triggered)
      • MS SQL Server (Query and Table)
      • ODBC Query
      • Oracle (Query and Table)
      • Polling Event
        • Polling Event Example Config
      • REST API
      • REST API (Cinchy Event Triggered)
      • SAP SuccessFactors
      • Salesforce Object (Bulk API)
      • Salesforce Platform Event
      • Salesforce Push Topic
      • Snowflake
        • Snowflake Source Example Config
      • SOAP 1.2 Web Service
    • Supported Data Sync Destinations
      • Cinchy Table
      • DB2 Table
      • Dynamics
      • Kafka Topic
      • MongoDB Collection
      • MS SQL Server Table
      • Oracle Table
      • REST API
      • Salesforce Object
      • Snowflake Table
      • SOAP 1.2 Web Service
    • Supported Real-Time Sync Stream Sources
      • Cinchy Event Broker/CDC
      • Data Polling
      • Kafka Topic
      • MongoDB
      • Salesforce Push Topic
      • Salesforce Platform Event
  • Other Resources
    • Angular SDK
    • JavaScript SQK
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On this page
  • 1. Overview
  • 2. Building a Data Sync
  • 2.1. Installation and Prerequisites
  • 2.2. Define Your Data Sync Type
  • 2.3. Create a Data Sync Configuration
  • 2.4. Set Up a Listener Config (Real-Time Syncs)
  • 2.5 Test and Run
  • 3. Scheduling a Data Sync
  • 4. Monitoring a Data Sync

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  1. Data Syncs

Getting Started with Data Syncs

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Last updated 1 year ago

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1. Overview

What are Data Syncs?

The Cinchy platform is purpose-built to enable a dramatic reduction in integration costs and complexity for business outcomes requiring heavy integration. To enable this further, the platform leverages a set of configurable pre-built data sync connectors that are able to bi-directionally read and move your data between various sources and targets. The Data Sync Connections Experience is a core functionality of data collaboration and is available with every Cinchy platform install.

Why are Data Syncs powerful?

Instead of keeping track of your siloed systems and the labour and financial pain that they cause, you can utilize the Data Sync Connections Experience to link everything together via Cinchy. You no longer need to worry about having your data locked behind disparate platforms or softwares---after a simple connfiguration process to define your use case(s), you can start moving your data via batch or real time syncs. Additionally, you can drill down and filter exactly what data you want to sync.

The Cinchy platform comes with a robust list of source and destination connectors and is working to expand the list over time. These connectors can be easily configured and avoid the need to create complex ETLs, APIs, and reconciliations to move data from one system to the other.

All data that is connected to the network is protected through Universal Access Controls and is instantly available for use in future projects, without the need for integration. The end result is a dramatic reduction in the number of copies and the associated integration cost and risk.

In a nutshell: building a data sync allows you to bring your data in and out of systems easily and without adding on to your integration tax.

2. Building a Data Sync

The following sections outline the steps needed for building data syncs in Cinchy. Follow the attached links to learn more.

2.1. Installation and Prerequisites

Before you can harness the power of Cinchy's Data Sync capabilities, you will need to install the following:

  • Connections

  • Event Worker/Listener

In a Kubernetes deployment of the Cinchy Platform, the above installations are bundled in, there is no manual install required.

Once installed, ensure that you have access to the following Cinchy Tables on your platform:

  • Data Sync Configurations

  • Execution Log

  • Execution Errors

  • Listener Config

2.2. Define Your Data Sync Type

There are two types of data syncs available in Cinchy: Batch and Real-Time.

Creating a Batch data sync vs. real-time data sync are similar processes. The main difference is that real-time data sync has some additional steps in the setup and does not need to be manually executed, where a Batch data sync does need to be manually executed.

Not every Connector will support both types of sync. This will be noted in the Overview section of the Connector's page.

2.2.1. Connections Options

You can choose whether to sync data one-way in, one way out, or bi-directionally into your Data Network.

For more information on the different types of connections in Cinchy, review the following pages:

2.3. Create a Data Sync Configuration

Whether you are setting up a real-time or a batch sync, you will need to create your data sync configuration. The data sync configuration defines the source, the mapping to the target, and synchronization behavior.

To set up a data sync, you can use the Connections UI or manually input a Data Sync XML into the Data Sync Configuration table. For more information, review the documentation here.

2.4. Set Up a Listener Config (Real-Time Syncs)

If you are setting up a real-time sync, you will need to first set up your listener configuration. This is done in the Listener Config table.

2.5 Test and Run

Once the data sync config is created and added to the table in Cinchy, you can test your data sync:

  • For batch syncs: run your job in the Connections UI.

  • For real-time syncs: You can test by making changes in the source system and seeing if the target updates.

3. Scheduling a Data Sync

You can set up a schedule for batch syncs using a task/job scheduling application.

4. Monitoring a Data Sync

To review documentation on how to monitor your data syncs for errors, click here.

work by processing a group or a ‘batch’ of data all together rather than each piece of data individually. Batch sync can either be run as a one-time data load operation, or it can be scheduled to run periodically.

push individual events in real-time to the target system through the Cinchy listener and worker process. Each piece of data is processed as soon as it is collected or edited, therefore, producing immediate results.

For instructions on subscribing to your event stream,

Supported Data Sync Sources
Supported Data Sync Destinations
Supported Sync Stream Sources (for real-time syncs)
review the documentation here.
Batch syncs
Real-time syncs