- 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
- Bing Webmaster Tools
- Rundeck
- Cal.com
- Mixpanel
- Matomo
- Walmart Marketplace
- Xray
- Reddit Ads
- Zoho Recruit
- Freshchat
- Zoho Books
- Outreach
- Airtable
- Attio
- Okta
- 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
Airtable (Edge)
Airtable is a cloud platform that teams use to build relational databases, called bases, with a spreadsheet-like interface. Each base contains tables of records that can link to records in other tables.
Hevo uses the Airtable Web API to replicate the records of the tables in your Airtable bases to the Destination of your choice. Hevo connects to Airtable using OAuth 2.0. To ingest data, you must authorize Hevo to read the bases that you want to replicate.
Supported Features
| Feature Name | Supported |
|---|---|
| Capture deletes | Yes |
| History mode | Yes, for the tables that have a Last modified time field that tracks all the fields of the table |
| Custom data (user-configured tables & fields) | Yes. All the tables and fields of your bases are custom |
| Data blocking (skip objects and fields) | Yes |
| Resync (objects and Pipelines) | Yes |
| API configurable | Yes |
Prerequisites
-
An active Airtable account exists with access to the bases from which data is to be ingested.
-
You are assigned the Creator or Owner permission level in the bases that you want to replicate, so that Hevo can create a webhook in each base. Hevo uses the webhook to detect the records that are created, changed, or deleted.
Scopes Required by Hevo
When you authorize Hevo, Airtable asks you to grant the following scopes:
| Scope | Purpose |
|---|---|
| data.records:read | Read the records of the tables in your bases. |
| schema.bases:read | Read the structure of your bases, such as the names of tables and the types of fields. |
| webhook:manage | Create and read the webhook that Hevo uses in each base to detect the records that are created, changed, or deleted. Hevo does not change your records. |
| user.email:read | Read the email address of the Airtable user who authorizes Hevo, which is displayed as Account Added in the Source configuration. |
Configure Airtable as a Source in your Pipeline
Perform the following steps to configure your Airtable Source:
-
Click Pipelines in the Navigation Bar.
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Click + Create Pipeline in the Pipelines List View.
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On the Select Source Type page, select Airtable.
-
On the Select Destination Type page, select the type of Destination you want to use.
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In the Configure Source screen, specify the following:

-
Source Name: A unique name for your Source, not exceeding 255 characters. For example, Airtable Source.
-
In the Connect to Airtable section, click Authorize Airtable, and log in to your Airtable account if prompted. After you grant access, Hevo displays the email address of your Airtable user as Account Added.
-
-
In the Airtable window, select the bases that Hevo can access, and then click Grant access:
-
Everything you have access to: Hevo can access all your current and future bases.
-
Custom access: Hevo can access only the bases, interfaces, and workspaces that you select.

-
-
Click Test & Continue to test the connection to your Airtable Source. Once the test is successful, you can proceed to set up your Destination.
You can change the bases that Hevo can access later from the Integrations section of your Airtable account. If you remove a base from the access granted to Hevo, Hevo marks the tables of that base as inaccessible and continues to ingest the tables of the other bases. If you revoke all the access granted to Hevo, Hevo cannot read your Airtable data, and the jobs of the Pipeline fail. To resume data replication, modify the Source configuration in the Pipeline and authorize Hevo again.
Data Replication
Hevo replicates the data of all the tables selected on the Configure Objects page during Pipeline creation. Hevo lists the tables of each base that you granted access to as objects. By default, all the tables and their fields are selected. However, you can modify this selection while creating or editing the Pipeline.
Hevo ingests data as follows:
-
Historical Data: In the first run of the Pipeline, Hevo ingests all the records of the selected tables.
-
Incremental Data: In the subsequent runs, as per the sync frequency, Hevo ingests the records that were created, changed, or deleted since the previous run. Hevo detects these records using a webhook that it creates in each base.
Hevo also ingests all the records of each table once every seven days. This brings the values of formula, rollup, and lookup fields up to date, because these fields can change without the record itself being changed, for example, when a linked record changes or when a formula uses the NOW() or TODAY() function. These values are also updated whenever their own record changes or Hevo ingests all the records of the table for another reason. Contact Hevo Support to change this interval for your Pipeline.
Hevo also ingests all the records of a table in the following cases:
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A field is added to or removed from the table, or the type of a field changes. Renaming a field does not cause a full read.
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Hevo cannot read the changes from the webhook, for example, when the Pipeline does not run for more than seven days and Airtable discards the changes.
Airtable allows 5 API requests per second for a base. Read Rate Limits. Hevo sends up to 5 requests per second to a base. If Airtable returns a rate limit error, Hevo waits and retries the request. To understand how Hevo handles such scenarios, read Handling Rate Limit Exceptions.
Pipelines with this Source support the Merge load mode. For the tables that have a Last modified time field that tracks all the fields of the table, you can also use the History Tracking load mode. Read History Mode.
Schema and Primary Keys
Hevo creates a schema in the Destination for each Airtable base. The name of the schema is made of the Destination prefix that you specify while creating the Pipeline, the name of the base, and the base ID. For example, for the base Project tracker with the ID app3UrkQfKNZMMnw7 and the prefix airtable, Hevo creates the schema airtable_project_tracker_app_3_urk_qf_knzmmnw_7. Hevo converts the name to lowercase and separates words, letters, and digits with underscores.
Each table of the base is loaded into a table with the name of the Airtable table. The primary key of each table is the id field, which contains the Airtable record ID.
Data Model
The tables and fields that Hevo creates in the Destination depend on the tables and fields of your Airtable bases:
| Airtable | Destination |
|---|---|
| Base | A schema, named as described in Schema and Primary Keys. |
| Table | A table in the schema of the base. If you rename a table in Airtable, Hevo loads it into a new table with the new name, and marks the records of the old table as deleted. |
| Field | A column of the table. Hevo creates a column for every field, even if the field has no data. The following fields are loaded as JSON: fields that hold multiple values, such as linked records, attachments, collaborators, and multiple select fields, and the single collaborator, created by, last modified by, button, barcode, and AI text fields. Number, currency, and percent fields are loaded as FLOAT. A field named id is not ingested, because the id column contains the record ID. |
| Record | A row of the table. The id column contains the Airtable record ID. |
Read Airtable field model for the types of fields in Airtable.
Additional Information
Read the detailed Hevo documentation for the following related topics:
Handling of Deletes
Hevo marks a record as deleted by setting the value of the metadata column __hevo__marked_deleted to True. Hevo captures deletes as follows:
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In the incremental runs, the webhook of the base reports the records deleted in Airtable, and Hevo marks them as deleted in the Destination.
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When Hevo ingests all the records of a table, records that are no longer returned by Airtable are marked as deleted in the Destination.
Source Considerations
-
Hevo creates a webhook in each base that it replicates. Airtable allows up to 10 webhooks in a base, and up to 2 webhooks for each app in a base. If Hevo cannot create a webhook in a base, Hevo ingests all the records of its tables in each run, and no warning is displayed in the Pipeline.
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Hevo does not delete its webhook from a base when you delete the Pipeline. Airtable deletes the webhook after seven days.
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If you rename a field in Airtable, Hevo adds a column with the new name and loads the field into it as records are changed. The column with the old name keeps its values.
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If you delete a table in Airtable, Hevo marks its records as deleted in the next run. If you delete a field, Hevo ingests all the records of the table again. If you give Hevo access to a new base, Hevo lists its tables as objects in the next run and creates a schema for the base.
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If Hevo cannot read a table, for example, because you no longer have access to it, Hevo marks the object as inaccessible and continues to ingest the other tables.
Revision History
Refer to the following table for the list of key updates made to this page:
| Date | Release | Description of Change |
|---|---|---|
| Nov-13-2026 | 2.54 | New document. |