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Reporting & Writeback Matrix Docs
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  • Advanced topics
    • Audit log
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  • Infobridge
    • 1. Create bridge
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    • 3. Bridge Interface
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    • 4. Manage sources
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    • 10. Integrating forecasts
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  • Accessibility shortcut keys
  • Inforiver Reporting Matrix FAQs
  • Inforiver Writeback Matrix FAQs
  • Video tutorials
  • Resources
  • Release Notes
    • Inforiver Writeback Matrix May 2025 - v4.5 - Audit redesign
    • Inforiver Writeback Matrix May 2025 - v4.4.3
    • Inforiver Writeback Matrix April 2025 - v4.4.2
    • Inforiver Writeback Matrix April 2025 - v4.4.
    • Inforiver Writeback Matrix March 2025 - v4.3 - Time series formulae
    • Inforiver Writeback Matrix - March 2025 - v4.2
    • Inforiver Writeback Matrix March 2025 - v4.1
    • Inforiver Reporting Matrix March 2025 - V3.3.3 -New formatting and export options
    • Inforiver Writeback Matrix Feb 2025 - v4.0 - User permissions, High-performance mode, Input rules
    • Inforiver Writeback Matrix January 2025 - v3.9 - Fabric support, upgraded variables interface
    • Inforiver Writeback Matrix December 2024- v3.8 - Improved Scheduler Performance & Other Enhancements
    • Inforiver Writeback Matrix December 2024 - v3.7 - Merge rows, new rounding functions & More
    • Inforiver Writeback Matrix November 2024 - v3.6.2 - Import from Google Sheets & More
    • Inforiver Writeback Matrix October 2024 - v3.6 - Approval Workflows & Other Enhancements
    • Inforiver Writeback Matrix October 2024 - v3.5 - Lookup visuals & more
    • Inforiver Reporting Matrix Nov 2024 - v3.3 - Variables
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  1. Working with Inforiver
  2. 12. Data writeback

Destinations

Setting up writeback destinations in Inforiver Writeback Matrix.

PreviousAdvanced SettingsNextAzure SQL (Managed DB)

Last updated 5 months ago

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Inforiver supports fast writeback setup and execution for several types of data destinations

  • Database/ Data warehouses/ Data lake: Azure SQL, SQL Server, Synapse Analytics Dedicated SQL Pool, Azure Data Lake, Fabric lakehouse, Fabric warehouse, Dataverse, Databricks, Snowflake, BigQuery, Amazon Redshift, SAP HANA, Oracle, SingleStore, PostgreSQL, MySQL,

  • File Destinations: OneDrive, SharePoint

  • Webhook URLs: Can be used to trigger workflows in iPaaS such as Power Automate and Logic Apps)

Configuring writeback destination(s) is straightforward and involves the same procedure (picture below) for any destination.

A list of destinations will be displayed for the users to select from.

Configuring destinations for writeback is a straightforward process that will be covered in the upcoming sections.

Generic destination settings

1. Client-managed and Inforiver-managed destination

After configuring the destination, you can choose whether the destination should be client-managed or Inforiver-managed. If you wish to restrict access to the database, opt for client-managed destinations.

  • Enable automatic writeback management: For Inforiver-managed destinations, Inforiver will automatically create the writeback table. However, Inforiver needs to be provided with the required permissions on the table.

  • For client-managed destinations, Inforiver will generate the scripts. You can manually execute them against the database.

2. Column config

  • Decimal Precision

Inforiver rounds off all numeric values including percentages to a specified number of decimal points as per your configuration instructions. When you create the first connection, you can specify decimal precision for all connections in that report.

Inforiver supports a maximum precision of 10 digits.

When you apply the decimal precision shown in the image above, Inforiver writes back values (in this example, to Snowflake) rounded off to 5 decimal places.

  • Text length

When you add destinations, you can restrict text fields to 512 characters or allow writeback up to the maximum limit supported by the backend.

3. Batch writeback

When you configure individual writeback destinations, you'll notice an option for batched writeback wherever applicable (batched writeback is not possible for destinations like Sharepoint, Rest API, OneDrive, etc). If your writeback payload exceeds 50k records, Inforiver can split the payload into multiple chunks and write them back batch-by-batch.

You can specify whether you will use a temporary table to hold the batched data.

  • To use this feature, enable the checkbox as shown in the image above.

  • You can specify a custom table name or use the default table created by Inforiver to store batched data.

You can analyze the writeback log, which will capture how the payload has been split into multiple chunks and processed in parallel for a performance boost.

Navigating to Inforiver writeback destination setup
Destinations organized by category
Inforiver managed destinations
Client managed destination
Setting the decimal precision for destinations
Writeback percentage values
Setting the text length
Temporary staging table configuration
Writeback logs for batched data