- Introduction
-
Getting Started
- Creating an Account in Hevo
- Subscribing to Hevo via AWS Marketplace
- Subscribing to Hevo via Snowflake Marketplace
- Connection Options
- Familiarizing with the UI
- Creating your First Pipeline
- Data Loss Prevention and Recovery
- Upgrading Pipeline from Standard to Edge
-
Data Ingestion
- Types of Data Synchronization
- Ingestion Modes and Query Modes for Database Sources
- Ingestion and Loading Frequency
- Data Ingestion Statuses
- Deferred Data Ingestion
- Handling of Primary Keys
- Handling of Updates
- Handling of Deletes
- Hevo-generated Metadata
- Best Practices to Avoid Reaching Source API Rate Limits
-
Edge
- Data Ingestion
- Core Concepts
-
Pipelines
- Familiarizing with the Pipelines UI
- Creating an Edge Pipeline
- Working with Edge Pipelines
- Pipeline Job History
- Pipeline Overview
- Needs Attention
- Object and Schema Management
- Activity Log
-
Sources
- Connect AI
- PostgreSQL
- Oracle
- MySQL
- SQL Server
- Troubleshooting Database Sources
- CockroachDB
- MongoDB
- SharePoint
- Salesforce Bulk API V2
- Ordergroove
- BambooHR
- Stripe
- NetSuite SuiteAnalytics
- Shopify
- Slack
- ClickUp
- Monday.com
- Pipedrive
- Workable
- Fathom
- HubSpot
- Salesforce Marketing Cloud
- Google Analytics 4
- Google Ads
- Facebook Ads
- Microsoft Ads
- LinkedIn Ads
- Xero
- Instagram Business
- Amazon Selling Partner
- TikTok Ads
- StackAdapt
- Amazon Ads
- Pinterest Organic
- Zendesk Support
- Snapchat Ads
- TikTok Organic
- Google Search Console
- Klaviyo v2
- Braintree Payments
- Facebook Pages
- Tempo
- PayPal
- Auth0
- Pinterest Ads
- Apple Search Ads
- Permit.io
- Amplitude Analytics
- Customer.io
- Paddle
- Jira Cloud
- Fullstory
- GitHub
- WorkOS
- ActiveCampaign
- Campaign Monitor
- Infobip
- Mailgun Send
- Metaplane
- Notion
- Recharge
- sevdesk
- Statuspage
- Teamwork.com
- Workday RaaS
- Yotpo
- TikTok Shop Partner Center
- Linear
- Microsoft Dynamics 365 Business Central
- Checkout.com
- Pylon
- Increase
- Microsoft Teams
- Rollbar
- Toggl Track
- QuickBooks Online
- Zoho Desk
- Livestorm
- Attentive
- SendGrid
- Bitly
- Shortcut
- Buzzsprout
- Persona
- Mailjet
- Open Exchange Rates
- PagerDuty
- CircleCI
- SonarQube
- Chargebee
- Freshdesk
- Bing Webmaster Tools
- Rundeck
- Cal.com
- Mixpanel
- Matomo
- Walmart Marketplace
- Xray
- Reddit Ads
- Zoho Recruit
- Freshchat
- Zoho Books
- Marketo
- LaunchDarkly
- Outreach
- Airtable
- Attio
- Okta
- Zendesk Chat
- Bigin by Zoho CRM
- Naming Conventions for Source Data Entities
- Destinations
- Transformations
- Alerts
- Activate
- Custom Connectors
-
Data Loading
- Loading Data in a Database Destination
- Loading Data to a Data Warehouse
- Optimizing Data Loading for a Destination Warehouse
- Deduplicating Data in a Data Warehouse Destination
- Manually Triggering the Loading of Events
- Scheduling Data Load for a Destination
- Loading Events in Batches
- Data Loading Statuses
- Data Spike Alerts
- Name Sanitization
- Table and Column Name Compression
- Parsing Nested JSON Fields in Events
-
Pipelines
- Data Flow in a Pipeline
- Familiarizing with the Pipelines UI
- Working with Pipelines
- Managing Objects in Pipelines
- Pipeline Jobs
-
Transformations
-
Python Code-Based Transformations
- Supported Python Modules and Functions
-
Transformation Methods in the Event Class
- Create an Event
- Retrieve the Event Name
- Rename an Event
- Retrieve the Properties of an Event
- Modify the Properties for an Event
- Fetch the Primary Keys of an Event
- Modify the Primary Keys of an Event
- Fetch the Data Type of a Field
- Check if the Field is a String
- Check if the Field is a Number
- Check if the Field is Boolean
- Check if the Field is a Date
- Check if the Field is a Time Value
- Check if the Field is a Timestamp
-
TimeUtils
- Convert Date String to Required Format
- Convert Date to Required Format
- Convert Datetime String to Required Format
- Convert Epoch Time to a Date
- Convert Epoch Time to a Datetime
- Convert Epoch to Required Format
- Convert Epoch to a Time
- Get Time Difference
- Parse Date String to Date
- Parse Date String to Datetime Format
- Parse Date String to Time
- Utils
- Examples of Python Code-based Transformations
-
Drag and Drop Transformations
- Special Keywords
-
Transformation Blocks and Properties
- Add a Field
- Change Datetime Field Values
- Change Field Values
- Drop Events
- Drop Fields
- Find & Replace
- Flatten JSON
- Format Date to String
- Format Number to String
- Hash Fields
- If-Else
- Mask Fields
- Modify Text Casing
- Parse Date from String
- Parse JSON from String
- Parse Number from String
- Rename Events
- Rename Fields
- Round-off Decimal Fields
- Split Fields
- Examples of Drag and Drop Transformations
- Effect of Transformations on the Destination Table Structure
- Transformation Reference
- Transformation FAQs
-
Python Code-Based Transformations
-
Schema Mapper
- Using Schema Mapper
- Mapping Statuses
- Auto Mapping Event Types
- Manually Mapping Event Types
- Modifying Schema Mapping for Event Types
- Schema Mapper Actions
- Fixing Unmapped Fields
- Resolving Incompatible Schema Mappings
- Resizing String Columns in the Destination
- Changing the Data Type of a Destination Table Column
- Schema Mapper Compatibility Table
- Limits on the Number of Destination Columns
- File Log
- Troubleshooting Failed Events in a Pipeline
- Mismatch in Events Count in Source and Destination
- Audit Tables
- Activity Log
-
Pipeline FAQs
- Can multiple Sources connect to one Destination?
- What happens if I re-create a deleted Pipeline?
- Why is there a delay in my Pipeline?
- Can I change the Destination post-Pipeline creation?
- Why is my billable Events high with Delta Timestamp mode?
- Can I drop multiple Destination tables in a Pipeline at once?
- How does Run Now affect scheduled ingestion frequency?
- Will pausing some objects increase the ingestion speed?
- Can I see the historical load progress?
- Why is my Historical Load Progress still at 0%?
- Why is historical data not getting ingested?
- How do I set a field as a primary key?
- How do I ensure that records are loaded only once?
- Why can't I see my Pipelines after logging in?
- Events Usage
-
Sources
- Free Sources
-
Databases and File Systems
- Data Warehouses
-
Databases
- Connecting to a Local Database
- Amazon DocumentDB
- Amazon DynamoDB
- Elasticsearch
-
MongoDB
- Generic MongoDB
- MongoDB Atlas
- Support for Multiple Data Types for the _id Field
- Example - Merge Collections Feature
-
Troubleshooting MongoDB
-
Errors During Pipeline Creation
- Error 1001 - Incorrect credentials
- Error 1005 - Connection timeout
- Error 1006 - Invalid database hostname
- Error 1007 - SSH connection failed
- Error 1008 - Database unreachable
- Error 1011 - Insufficient access
- Error 1028 - Primary/Master host needed for OpLog
- Error 1029 - Version not supported for Change Streams
- SSL 1009 - SSL Connection Failure
- Troubleshooting MongoDB Change Streams Connection
- Troubleshooting MongoDB OpLog Connection
-
Errors During Pipeline Creation
- SQL Server
-
MySQL
- Amazon Aurora MySQL
- Amazon RDS MySQL
- Azure MySQL
- Generic MySQL
- Google Cloud MySQL
- MariaDB MySQL
-
Troubleshooting MySQL
-
Errors During Pipeline Creation
- Error 1003 - Connection to host failed
- Error 1006 - Connection to host failed
- Error 1007 - SSH connection failed
- Error 1011 - Access denied
- Error 1012 - Replication access denied
- Error 1017 - Connection to host failed
- Error 1026 - Failed to connect to database
- Error 1027 - Unsupported BinLog format
- Failed to determine binlog filename/position
- Schema 'xyz' is not tracked via bin logs
- Errors Post-Pipeline Creation
-
Errors During Pipeline Creation
- MySQL FAQs
- Oracle
-
PostgreSQL
- Amazon Aurora PostgreSQL
- Amazon RDS PostgreSQL
- Azure PostgreSQL
- Generic PostgreSQL
- Google Cloud PostgreSQL
- Heroku PostgreSQL
- Upgrading Pipelines with PostgreSQL Sources to Use the pgoutput Plugin
-
Troubleshooting PostgreSQL
-
Errors during Pipeline creation
- Error 1003 - Authentication failure
- Error 1006 - Connection settings errors
- Error 1011 - Access role issue for logical replication
- Error 1012 - Access role issue for logical replication
- Error 1014 - Database does not exist
- Error 1017 - Connection settings errors
- Error 1023 - No pg_hba.conf entry
- Error 1024 - Number of requested standby connections
- Errors Post-Pipeline Creation
-
Errors during Pipeline creation
-
PostgreSQL FAQs
- Can I track updates to existing records in PostgreSQL?
- How can I migrate a Pipeline created with one PostgreSQL Source variant to another variant?
- How can I prevent data loss when migrating or upgrading my PostgreSQL database?
- Why do FLOAT4 and FLOAT8 values in PostgreSQL show additional decimal places when loaded to BigQuery?
- Why is data not being ingested from PostgreSQL Source objects?
- Troubleshooting Database Sources
- Database Source FAQs
- File Storage
- Engineering Analytics
- Finance & Accounting Analytics
-
Marketing Analytics
- ActiveCampaign
- AdRoll
- Amazon Ads
- Apple Search Ads
- AppsFlyer
- CleverTap
- Criteo
- Drip
- Facebook Ads
- Facebook Page Insights
- Firebase Analytics
- Freshsales
- Google Ads
- Google Analytics 4
- Google Analytics 360
- Google Play Console
- Google Search Console
- HubSpot
- Instagram Business
- Klaviyo v2
- Lemlist
- LinkedIn Ads
- Mailchimp
- Mailshake
- Marketo
- Microsoft Ads
- Onfleet
- Outbrain
- Pardot
- Pinterest Ads
- Pipedrive
- Recharge
- Segment
- SendGrid Webhook
- SendGrid
- Salesforce Marketing Cloud
- Snapchat Ads
- SurveyMonkey
- Taboola
- TikTok Ads
- Twitter Ads
- Typeform
- YouTube Analytics
- Product Analytics
- Sales & Support Analytics
- Source FAQs
-
Destinations
- Familiarizing with the Destinations UI
- Cloud Storage-Based
- Databases
-
Data Warehouses
- Amazon Redshift
- Amazon Redshift Serverless
- Azure Synapse Analytics
- Databricks
-
Google BigQuery
- Clustering in BigQuery
- Partitioning in BigQuery
- Structure of Data in the Google BigQuery Data Warehouse
- Loading Data to a Google BigQuery Data Warehouse
- Near Real-time Data Loading using Streaming
- Modifying BigQuery Destinations to Use Service Account Authentication
- Troubleshooting Google BigQuery
- Google BigQuery FAQs
- Hevo Managed Google BigQuery
- Snowflake
- Troubleshooting Data Warehouse Destinations
-
Destination FAQs
- Can I change the primary key in my Destination table?
- Can I change the Destination table name after creating the Pipeline?
- How can I change or delete the Destination table prefix?
- Why does my Destination have deleted Source records?
- How do I filter deleted Events from the Destination?
- Does a data load regenerate deleted Hevo metadata columns?
- How do I filter out specific fields before loading data?
- Transform
- Alerts
- Account Management
- Activate
- Glossary
-
Releases-
2026 Releases
- Release 2.53 (August 24 – September 21, 2026)
- Release 2.52 (July 27 – August 24, 2026)
- Release 2.51 (Jun 29 – July 27 , 2026)
- Release 2.50 (Jun 01–29, 2026)
- Release 2.49 (May 04-Jun 01, 2026)
- Release 2.48 (Apr 06-May 04, 2026)
- Release 2.47 (Mar 09-Apr 06, 2026)
- Release 2.46 (Feb 09-Mar 09, 2026)
- Release 2.45 (Jan 12-Feb 09, 2026)
-
2025 Releases
- Release 2.44 (Dec 01, 2025-Jan 12, 2026)
- Release 2.43 (Nov 03-Dec 01, 2025)
- Release 2.42 (Oct 06-Nov 03, 2025)
- Release 2.41 (Sep 08-Oct 06, 2025)
- Release 2.40 (Aug 11-Sep 08, 2025)
- Release 2.39 (Jul 07-Aug 11, 2025)
- Release 2.38 (Jun 09-Jul 07, 2025)
- Release 2.37 (May 12-Jun 09, 2025)
- Release 2.36 (Apr 14-May 12, 2025)
- Release 2.35 (Mar 17-Apr 14, 2025)
- Release 2.34 (Feb 17-Mar 17, 2025)
- Release 2.33 (Jan 20-Feb 17, 2025)
-
2024 Releases
- Release 2.32 (Dec 16 2024-Jan 20, 2025)
- Release 2.31 (Nov 18-Dec 16, 2024)
- Release 2.30 (Oct 21-Nov 18, 2024)
- Release 2.29 (Sep 30-Oct 22, 2024)
- Release 2.28 (Sep 02-30, 2024)
- Release 2.27 (Aug 05-Sep 02, 2024)
- Release 2.26 (Jul 08-Aug 05, 2024)
- Release 2.25 (Jun 10-Jul 08, 2024)
- Release 2.24 (May 06-Jun 10, 2024)
- Release 2.23 (Apr 08-May 06, 2024)
- Release 2.22 (Mar 11-Apr 08, 2024)
- Release 2.21 (Feb 12-Mar 11, 2024)
- Release 2.20 (Jan 15-Feb 12, 2024)
-
2023 Releases
- Release 2.19 (Dec 04, 2023-Jan 15, 2024)
- Release Version 2.18
- Release Version 2.17
- Release Version 2.16 (with breaking changes)
- Release Version 2.15 (with breaking changes)
- Release Version 2.14
- Release Version 2.13
- Release Version 2.12
- Release Version 2.11
- Release Version 2.10
- Release Version 2.09
- Release Version 2.08
- Release Version 2.07
- Release Version 2.06
-
2022 Releases
- Release Version 2.05
- Release Version 2.04
- Release Version 2.03
- Release Version 2.02
- Release Version 2.01
- Release Version 2.00
- Release Version 1.99
- Release Version 1.98
- Release Version 1.97
- Release Version 1.96
- Release Version 1.95
- Release Version 1.93 & 1.94
- Release Version 1.92
- Release Version 1.91
- Release Version 1.90
- Release Version 1.89
- Release Version 1.88
- Release Version 1.87
- Release Version 1.86
- Release Version 1.84 & 1.85
- Release Version 1.83
- Release Version 1.82
- Release Version 1.81
- Release Version 1.80 (Jan-24-2022)
- Release Version 1.79 (Jan-03-2022)
-
2021 Releases
- Release Version 1.78 (Dec-20-2021)
- Release Version 1.77 (Dec-06-2021)
- Release Version 1.76 (Nov-22-2021)
- Release Version 1.75 (Nov-09-2021)
- Release Version 1.74 (Oct-25-2021)
- Release Version 1.73 (Oct-04-2021)
- Release Version 1.72 (Sep-20-2021)
- Release Version 1.71 (Sep-09-2021)
- Release Version 1.70 (Aug-23-2021)
- Release Version 1.69 (Aug-09-2021)
- Release Version 1.68 (Jul-26-2021)
- Release Version 1.67 (Jul-12-2021)
- Release Version 1.66 (Jun-28-2021)
- Release Version 1.65 (Jun-14-2021)
- Release Version 1.64 (Jun-01-2021)
- Release Version 1.63 (May-19-2021)
- Release Version 1.62 (May-05-2021)
- Release Version 1.61 (Apr-20-2021)
- Release Version 1.60 (Apr-06-2021)
- Release Version 1.59 (Mar-23-2021)
- Release Version 1.58 (Mar-09-2021)
- Release Version 1.57 (Feb-22-2021)
- Release Version 1.56 (Feb-09-2021)
- Release Version 1.55 (Jan-25-2021)
- Release Version 1.54 (Jan-12-2021)
-
2020 Releases
- Release Version 1.53 (Dec-22-2020)
- Release Version 1.52 (Dec-03-2020)
- Release Version 1.51 (Nov-10-2020)
- Release Version 1.50 (Oct-19-2020)
- Release Version 1.49 (Sep-28-2020)
- Release Version 1.48 (Sep-01-2020)
- Release Version 1.47 (Aug-06-2020)
- Release Version 1.46 (Jul-21-2020)
- Release Version 1.45 (Jul-02-2020)
- Release Version 1.44 (Jun-11-2020)
- Release Version 1.43 (May-15-2020)
- Release Version 1.42 (Apr-30-2020)
- Release Version 1.41 (Apr-2020)
- Release Version 1.40 (Mar-2020)
- Release Version 1.39 (Feb-2020)
- Release Version 1.38 (Jan-2020)
-
2026 Releases
- Early Access New
LaunchDarkly (Edge)
LaunchDarkly is a feature management platform that teams use to release features with feature flags, target them to specific users and segments, and run experiments.
Hevo uses the LaunchDarkly REST API to replicate the projects, environments, feature flags, segments, metrics, teams, and audit log of your LaunchDarkly account to the Destination of your choice. To ingest data, you must provide Hevo with an API access token of your LaunchDarkly account.
Supported Features
| Feature Name | Supported |
|---|---|
| Capture deletes | Yes, for the Environment, Feature Flag, Flag Default Project, Metric, Project, Segment, and Team objects |
| History mode | No |
| Custom data (user-configured tables & fields) | No |
| Data blocking (skip objects and fields) | Yes |
| Resync (objects and Pipelines) | Yes |
| API configurable | Yes |
Prerequisites
-
An active LaunchDarkly account exists from which data is to be ingested.
-
You are a member of your LaunchDarkly account. Any member can create a personal access token with the Reader role. If your LaunchDarkly plan includes service tokens, Hevo recommends that you create a service token.
-
An API access token is created in LaunchDarkly and is available.
Create an API Access Token
Hevo reads your LaunchDarkly data with an API access token. Read API access tokens.
Perform the following steps to create an access token:
-
Log in to your LaunchDarkly account.
-
In the bottom left corner of the left navigation pane, click the Settings icon.

-
In the Settings sidebar, under Security, click Authorization.

-
On the Tokens tab, click Create token.

-
In the Create access token dialog, specify the following, and then click Save token:

-
Name: A name for the token, for example, Hevo.
-
Role: Reader. This role is selected by default and is sufficient for Hevo, as Hevo only reads data from LaunchDarkly.
-
Expiration: No expiration. If you select an expiration date, you must update the Pipeline with a new token before the token expires.
-
API version: The default version.
-
This is a service token: If your LaunchDarkly plan includes service tokens, select this check box, so that the token is not tied to your member account and keeps working if you leave the organization.
-
-
Click the copy icon next to the token, and save it securely like any other password. LaunchDarkly does not show the token again after you close the dialog.

-
Click Close.
Configure LaunchDarkly as a Source in your Pipeline
Perform the following steps to configure your LaunchDarkly Source:
-
Click Pipelines in the Navigation Bar.
-
Click + Create Pipeline in the Pipelines List View.
-
On the Select Source Type page, select LaunchDarkly.
-
On the Select Destination Type page, select the type of Destination you want to use.
-
In the Configure Source screen, specify the following:

-
Source Name: A unique name for your Source, not exceeding 255 characters. For example, LaunchDarkly Source.
-
In the Connect to LaunchDarkly section:
- Access Token: The API access token that you created in LaunchDarkly.
-
-
Click Test & Continue to test the connection to your LaunchDarkly Source. Once the test is successful, you can proceed to set up your Destination.
When you click this button, Hevo checks that LaunchDarkly accepts the access token.
Data Replication
Hevo replicates data for all the objects selected on the Configure Objects page during Pipeline creation. By default, all supported objects and their available fields are selected. However, you can modify this selection while creating or editing the Pipeline. The Data Model lists the child objects of each parent object. If you select a child object without its parent object, Hevo reads the parent object to ingest the child object, but does not load the parent object to the Destination.
In the first run of the Pipeline, Hevo ingests all the data available for the selected objects in your LaunchDarkly account, including all the entries of the audit log that LaunchDarkly retains. In the subsequent runs, as per the sync frequency, Hevo ingests data as follows:
| Ingestion | Parent Objects |
|---|---|
| The audit log entries created since the previous run. Changes are identified using the date of each entry. | Audit Log |
| All the records, in each run | All the other parent objects |
Hevo ingests the child objects of a parent object along with it.
Hevo reads the environments, feature flags, flag defaults, metrics, and segments of each project in your account, and the maintainers and roles of each team.
LaunchDarkly limits the number of API requests that an access token can make. Read Rate limiting. If LaunchDarkly returns a rate limit error, Hevo waits and retries the request up to three times. If the limit persists, Hevo ingests the data in the next run. To understand how Hevo handles such scenarios, read Handling Rate Limit Exceptions.
Schema and Primary Keys
Hevo uses the following schema to upload the records in the Destination. For a detailed view of the objects, fields, and relationships, click the ERD.
Data Model
The following is the list of tables (objects) that are created at the Destination when you run the Pipeline:
| Object | Description |
|---|---|
| Audit Log | Contains the entries of the audit log of your account, such as the change made, the member who made it, and the resource that was changed. It includes the following child objects: Audit Log Access and Audit Log Target Resource. |
| Environment | Contains the environments of each project, such as Production and Test, with settings such as the approval requirements. It includes the following child objects: Required Approval Tag and Tags. |
| Feature Flag | Contains the feature flags of each project, such as the name, kind, maintainer, and creation date of each flag. It includes the following child objects: Feature Flag Environment, Feature Flag Environment Action Allowed, Feature Flag Environment Action Denied, Feature Flag Environment Context Target, Feature Flag Environment Evaluation, Feature Flag Environment Fallthrough Rollout Variation, Feature Flag Environment Prerequisite, Feature Flag Environment Rule, Feature Flag Environment Rule Clause, Feature Flag Environment Rule Rollout Variation, Feature Flag Environment Target, Feature Flag Experiment, and Feature Flag Experiment Environment. |
| Feature Flag Status | Contains the status of each feature flag in each environment, such as active, inactive, or launched, and the time at which the flag was last requested. |
| Feature Flag Tag | Contains the tags assigned to the feature flags. |
| Feature Flag Variation | Contains the variations of each feature flag, such as the name and value of each variation. |
| Flag Default Project | Contains the default settings that each project applies to new feature flags. It includes a child object, Flag Default Project Tag. |
| Maintainer | Contains the maintainers of each team. |
| Metric | Contains the metrics of each project that experiments measure, such as the kind, event key, and success criteria of each metric. It includes the following child objects: Metric Access, Metric Randomization Unit, and Metric Tag. |
| Project | Contains the projects of your account. It includes a child object, Project Tag. |
| Role | Contains the custom roles assigned to each team. |
| Segment | Contains the segments of each project and environment, such as the name and the contexts included or excluded. It includes the following child objects: Rule Clause, Segment Access, Segment Excluded Contexts, Segment Included Contexts, Segment Rule, Segment Tags, and Segment Target. |
| Segment Flag | Contains the feature flags that use each segment. |
| Team | Contains the teams of your account, such as the name, description, and member count of each team. It includes the following child objects: Role Project, Team Access, and Team Project. |
Additional Information
Read the detailed Hevo documentation for the following related topics:
Handling of Deletes
LaunchDarkly does not mark deleted records in its API responses. Hevo handles deletes as follows:
-
Environment, Feature Flag, Flag Default Project, Metric, Project, Segment, and Team objects: Hevo ingests all the records of these objects in each run. If a record that exists in the Destination is not returned by LaunchDarkly, Hevo marks it as deleted by setting the value of the metadata column, __hevo__marked_deleted, to True. LaunchDarkly does not return archived feature flags, so Hevo marks an archived feature flag as deleted.
-
Child objects of Audit Log: Each time Hevo ingests an audit log entry, it replaces the child records of that entry in the Destination.
-
All the other objects: In each run, Hevo ingests all the records of these objects and replaces the Destination table with them. A record that is deleted in LaunchDarkly is removed from the Destination. For example, if you delete a feature flag, Hevo removes its variations from the Destination, instead of marking them as deleted.
-
Audit Log object: Hevo does not capture deletes for this object. Audit log entries that LaunchDarkly removes after its retention period remain in the Destination.
Source Considerations
-
Hevo ingests the audit log entries that LaunchDarkly retains for your account. The retention period depends on your LaunchDarkly plan. Entries that LaunchDarkly removed before the first run of the Pipeline are not ingested.
-
If your LaunchDarkly plan does not include teams, or your access token cannot read teams, Hevo marks the Maintainer, Role, Role Project, Team, Team Access, and Team Project objects as inaccessible and ingests the other objects.
-
Hevo creates a table in the Destination only for the objects that have data. For example, if your account does not use experiments, the Feature Flag Experiment table is not created.
Revision History
Refer to the following table for the list of key updates made to this page:
| Date | Release | Description of Change |
|---|---|---|
| Nov-12-2026 | 2.54 | New document. |