Integrate.io Release Notes & Product Updates

September's release cycle went into keeping data flowing when part of a load goes wrong, and into clean recovery when replication sources are interrupted. Ad platform coverage and initial-sync data fidelity took the rest of the month.

The Snowflake Destination Keeps Healthy Tables Loading When One Fails

One table's failed load into Snowflake used to stop the whole pipeline, so a single bad table held up every healthy one behind it. With a per-pipeline setting, the Snowflake destination keeps the pipeline running while that table is set aside, and a Destination Load Failed notification names the table that needs attention. Failed tables retry on their own, so a transient problem clears without anyone stepping in. The rest of your warehouse stays current while the one problem gets fixed, instead of the whole sync waiting on it.

The OneTouch Health Source Puts Care Operations Data in the Warehouse

Home care teams running on OneTouch Health have had client, carer, and scheduling data locked inside the care management system, reachable only through exports. The OneTouch Health source replicates operational, care, client, scheduling, financial, and residential data into your warehouse. Scheduling, billing, and staffing questions get answered alongside payroll and finance data instead of in a spreadsheet stitched together each month. Operations and finance teams work from the same numbers, refreshed on every sync.

New Features

• Facebook Ads Lookback Windows: Facebook Ads pipelines can re-read the last 1, 7, 14, or 28 days of Insights on every sync. Conversions Meta attributes after the original sync land in the warehouse instead of leaving spend and return figures short.
• Source Change Time for DB2 CDC: DB2 CDC pipelines can carry an _iio_changed_at column holding each row's change time at the source. Ordering and freshness checks use when a change happened instead of when it loaded, and our team enables it on request.

Improvements

• Facebook Pages connect with page-level access, so a connection no longer needs business portfolio administrator permissions.
• The Facebook connector's schemas follow Meta's new API version, so streams keep loading as Meta retires older fields.
• Facebook Ads adds daily_budget, lifetime_budget, and budget_remaining on campaigns, and bid_amount and bid_strategy on ad sets.
• Snapchat discovers every organization and ad account a connection can reach and syncs them all, instead of one organization at a time.
• Initial syncs of pipelines with many tables allow schema setup time in proportion to the table count, so wide pipelines start instead of timing out.
• Snowflake connection validation checks only the configured warehouse, database, and schema, so roles with many grants validate instead of timing out.
• Redshift and Snowflake destinations never run two load cycles against the same table at once, so catch-up after a delay finishes without duplicate work.

Fixes

• MySQL change tracking resumes after a source restart instead of stalling silently while reporting as healthy.
• DB2 sources write _iio_start without a container time zone shift, and an unreadable source change time falls back instead of failing the row.
• Oracle syncs of large tables keep their connection alive through long idle stretches instead of failing on a dropped connection.
• Oracle unconstrained NUMBER columns keep fractional values as decimals instead of truncating them.
• Oracle CDC reads text values that look like numbers instead of failing to parse them.
• BigQuery pipelines treat a transient error as transient instead of failing with a permissions error.
• PostgreSQL ARRAY columns load into Snowflake as text, and existing affected tables need a resync to pick up the corrected type.
• MySQL JSON columns load into Redshift during initial syncs that use parallel chunk workers.
• Snowflake initial syncs complete on tables holding zero dates or text longer than 16 MB.

Performance Enhancements

• Oracle initial syncs of large tables without a numeric primary key chunk on a suitable indexed column, so the first load runs in parallel instead of as one stream.
• BigQuery destinations share one schema-propagation wait across tables, so a column change across many tables costs one 5-minute wait instead of 5 minutes per table.

Tell us how table-level failure handling holds up on your busiest Snowflake pipelines, and which ad platforms you want covered more deeply next.

August's release cycle went into Oracle replication depth and keeping destination schemas current as sources change. Security hardening and large-volume load reliability took the rest of the month.

Automatic Column Detection Keeps Destination Schemas Current

A column added at the source stayed invisible until someone noticed a report was missing it, worked out which pipeline was responsible, and edited the schema by hand. Singer-based connectors detect new source fields and carry them into the destination on the next sync, so the warehouse tracks the source instead of drifting from it. The gap between a change upstream and a change in your tables becomes one sync interval instead of however long it takes someone to notice. Past rows keep an empty value for a new column, so a backfill still needs an explicit resync.

Parallel Chunk Workers and History Mode Deepen Oracle Replication

A large Oracle initial sync ran as one extraction stream, and a record's earlier state vanished the moment it changed. Splitting extraction across parallel chunk workers shortens a large first load, and a failed setup releases its database sessions instead of leaving them for a DBA to clean up. History Mode keeps prior versions of a row with the source's change times, so a point-in-time question about what a record looked like last quarter has an answer in the warehouse instead of in a backup. Replication also drops rolled-back changes and replays partially acknowledged ones after an interruption, so updates and deletes stop going missing quietly.

New Features

• Configurable Salesforce Lookback: Each Salesforce pipeline can re-check recent changes on a window from thirty minutes up to twenty-eight days. Records that land after their original sync come through on the re-check instead of going missing, and the setting stays off until you enable it.
• Initial Sync Only Override: A pipeline can complete its initial snapshot, load the staged data into the warehouse, and stop before continuous replication starts, configured per pipeline by our team. One-time migrations finish without a CDC stream you then have to turn off.
• Validation Overrides: Pipeline creation and edits can proceed without validating the source and destination, and our team can apply an account-specific override for an overly strict Snowflake check. Bypassed checks stay visible and auditable, while connection, credential, and provisioning failures still block deployment.

Improvements

• TikTok Ads syncs ingest reservation campaigns, including TopView and Reach & Frequency buys, alongside the auction campaigns already covered.
• TikTok Ads tracks report progress separately for each advertiser, so a stalled advertiser stops hiding missing data elsewhere.
• TikTok Ads fetches partially complete recent report windows after a resume or a restart, so the newest data is not skipped.
• TikTok Ads handles more error responses from the provider rather than failing the sync outright.
• Oracle destinations support SCD Type 2, so a dimension table keeps its change history instead of overwriting a row in place.
• Singer source validation messages carry the underlying provider error, such as a missing permission, instead of a generic credentials message.
• The Snowflake connector runs on a newer Snowflake SDK driver version that carries the provider's latest fixes.
• Resumed and retried initial syncs report steadily advancing progress, and BigQuery receives completion notifications during large initial loads.
• Chunk workers wait until the schema record is fully applied before they start, so chunked uploads complete more consistently.
• Large pipelines wait for destination preparation to finish, and the platform validates timing and retry settings before use.
• DB2 setup queries double-quote database, schema, and table names consistently on the generated setup SQL.
• Alert setup consistently rejects malformed, unauthorized, or excessive notification-hook references when an alert is created or edited.

Fixes

• Snowflake operations quote reserved-word columns and names containing double quotes without a pipeline change.
• Chunked COPY statements on Snowflake run in sequence, and chunk stream keys resolve before they leave the copy handler.
• The TikTok Ads pixel_instant_page_events stream completes on parameter combinations the provider does not support.
• DB2 CDC setup SQL runs on the DB2 CLP, and it handles SAP-style identifiers, unusual punctuation, and repeated execution.
• Initial-sync progress reports current and total in the right order, and null fields return a result rather than a server error.
• Auto restart completes without firing a false error event hook.
• Oracle pipelines handle a duplicate record on an index column instead of failing.
• User invitations stay scoped to the correct organization, and moving an existing account requires the invited user to accept.
• Observability alert validation on create and edit checks object ownership.

Performance Enhancements

• Snowflake imports of more than 1,000 files complete instead of failing partway, so a high-volume load no longer needs splitting into smaller batches.
• TikTok Ads backfills use thirty-day windows and larger pages, so a historical load needs fewer requests and still fetches the latest partial range.

Tell us how the Oracle changes hold up on your largest tables, and which sources should get column detection next.

July's release cycle extended destination coverage to the lakehouse and added a widely requested support-platform source. The rest of the cycle went into initial-sync reliability and giving teams a way to verify their alerting before they need it.

The Databricks Destination Lands Replicated Data in Delta Tables

Teams running on the lakehouse previously had to route CDC output through an intermediate warehouse before it reached Databricks. Pipelines can now replicate into Delta tables directly, with staging and load handled for you so resyncs, CDC updates, and scheduled runs behave the same way they do on every other warehouse destination. Type conversion is mapped across ten source types, including MySQL, Postgres, SQL Server, Oracle, DB2, Salesforce, BigQuery, and file storage, so high-precision decimals, Postgres complex types, SQL Server TIME, and JSON survive the load intact. Setup runs through a guided connection flow in the app, and AWS is supported today with Azure and GCP planned.

New Connectors

• Intercom Source: Intercom is now a supported source via OAuth with eleven streams covering contacts, companies, conversations, conversation parts, tickets, admins, teams, tags, segments, data attributes, and articles. Support and product teams can replicate their full Intercom history into the warehouse alongside the rest of their customer data.

New Features

• Slack Notification Hook: Pipeline run notifications can now go to Slack through a one-click OAuth connection in the Hooks UI. Teams get failure and completion alerts in the channel they already watch instead of only by email.
• Test Hook: A Test Hook action in the Hooks menu and in Settings sends a real ping to a configured Slack channel or email recipient list. Hook configuration can be verified at setup time rather than on the next failed run.
• Expanded Stripe Streams: Refunds, payment methods, and dispute balance transactions are now available as Stripe streams. Finance teams can reconcile chargebacks and refunds in the warehouse without maintaining a separate extract.
• Deleted Ad Records in Facebook Ads and TikTok Ads: Deleted ad rows are now included by default on supported streams, with a one-time historical backlog arriving on the first sync. Contact our team if you would rather exclude them.

Improvements

• Editing a hook preserves the fields you did not change, and an invalid configuration is rejected with a clear error instead of being saved.
• Databricks resyncs drop and recreate the destination table by default, with an opt-out for teams that manage table lifecycle themselves.
• The Intercom connector recovers on its own when a sync cannot resume from its last position, instead of stopping until someone intervenes.
• Intercom data attributes now use a composite primary key, so tables created before this change, including data_attributes and tickets, need a drop and resync.

Fixes

• Initial syncs no longer produce duplicate rows on affected pipelines, though a small number of tables that were mid-sync may need an explicit resync.
• DB2 to BigQuery initial syncs complete instead of hanging indefinitely.
• Pipelines that stop with an error fire the Pipeline Errored notification instead of Pipeline Stopped.
• The Bing Ads campaign stream includes the ad type that was previously missing.
• Intercom syncs complete for organizations with more than 10,000 records and recover from expired sessions.

Performance Enhancements

• Initial syncs to BigQuery run in parallel with safer retry handling, cutting the time it takes to load a large source.
• Out-of-memory failures during large initial syncs have been resolved, so a parallel sync finishes rather than ending partway through.

Let us know which sources you want us to cover next.

June's release cycle focused on expanding destination coverage with three new connectors and opening up programmatic pipeline management through a public API. Alongside that, sync reliability and security posture were hardened across MySQL, PostgreSQL, and Snowflake.

The Public API and YAML Import/Export Enable Programmatic Pipeline Management
Pipelines can now be managed programmatically through a new public API, with support for exporting and importing pipeline definitions as YAML. Teams can version pipeline configurations alongside application code, replicate pipelines across environments, and manage pipelines from their own tooling instead of relying solely on the dashboard. See the documentation for endpoint reference and authentication setup.

PostgreSQL, MySQL, and SQL Server Destinations Round Out Database Coverage
These Databases are now available as destinations, rounding out the platform's database destination coverage alongside existing targets. Teams running on these databases can land CDC data directly into them without staging through an intermediate warehouse first.

New Features

• Google Ads Expanded Performance Report Fields: CAMPAIGN, AD, ADGROUP, BUDGET, and KEYWORDS performance reports now expose significantly more fields per stream, aligning their schemas with the equivalent entity-stream reports and giving analysts access to metrics that previously required a separate report.
• MySQL and BigQuery Schema Change Hooks: MySQL and BigQuery sources now emit NEW_COLUMN and NEW_TABLE hook events, letting teams trigger downstream automation when source schemas change instead of polling for new fields or tables.
• Hubspot Custom Object Support: Hubspot sources can now sync custom objects in addition to standard objects, giving teams with custom Hubspot schemas full pipeline coverage instead of being limited to default object types.

Improvements

• PostgreSQL destination's graceful termination handling has been improved, and credential inputs are now available directly in the UI setup instead of requiring manual configuration.
• Snowflake destination role requirements have been hardened to least privilege, dropping the OWNERSHIP and schema MODIFY privileges that were previously required to sync data.
• MySQL source now supports per-table error isolation, so an issue with one table no longer halts replication for the rest of the source, and an optional GTID rewind override is available when replication state is ahead of the source.
• PostgreSQL destination now supports an override to make the heartbeat table optional, accommodating deployments that restrict heartbeat table creation.
• Parallel chunk workers on sources writing to BigQuery destinations have been disabled by default to avoid tripping BigQuery rate limits during sync.
• Hubspot connector now automatically syncs new columns added to custom objects, extending the custom object support shipped earlier this cycle.

Fixes

• PostgreSQL destination no longer produces ghost rows during a full resync, and no longer requires table ownership or CREATE privileges to sync.
• Non-PostgreSQL destinations no longer fail from a regression tied to a null file compression configuration value.
• Snowflake connector's test connection setup query helper no longer returns undefined values.
• MySQL connector no longer mismaps uint columns, JSON columns during parallel sync, or datetime values nested inside JSON during parallel sync.

We'd love to hear how these updates land in your day-to-day syncs, so don't hesitate to reach out with feedback.

• Snowflake Custom Host: Users can now specify an optional custom Snowflake host when configuring a Snowflake destination: supporting non-standard deployments and use cases like those of Yuki Data, a partner who's focused on optimizing data warehouse cost and spend on a custom Snowflake setup.
• Google Ads Time-Variant Keyword and Budget Reports: Five new KEYWORDS_PERFORMANCE_REPORT streams (daily, weekly, monthly, quarterly, and yearly) are now available, along with a new BUDGET_PERFORMANCE_REPORT_BY_CAMPAIGN collection, making time-segmented keyword and budget data accessible for more granular paid-search analysis.
• Google Ads All-Time Metrics Collection: A new `_TIMES` collection is now available for every Google Ads report, consolidating all-time aggregated metrics alongside the existing time-windowed streams - removing the need to union multiple reports to get full-range totals.
• TikTok Ads Expanded Field Coverage: A broad set of additional fields is now available on ad_groups, ads, campaigns, creative_assets_videos, and pixels collections, with metrics and dimensions exposed on most report collections and dimension and metric selections persisted across sessions.
• Destination Column Default Preservation: Existing column defaults on Redshift and Snowflake destination tables are no longer overwritten during pipeline runs, protecting intentional database-level defaults from being cleared by sync operations.

• When new tables are added to a Salesforce V2 source, their incremental replication key and streaming support are now saved automatically, so incremental syncs and streaming options work correctly for newly added tables without additional configuration.

• Chunking issue on composite string primary keys that could cause rows to be missed or duplicated during chunk-based syncs - now resolved.
• Snowflake configuration validation no longer rejects pipelines where the custom host field is unset, allowing existing Snowflake pipelines to start normally.
• PostgreSQL 14+ tables that had not been analyzed no longer stall on resync - reliable initial sync behavior has been restored.
• Pipeline hooks no longer send duplicate email notifications when the same hook is attached to multiple pipelines.
• Enterprise SAML users are no longer blocked at login when their identity provider does not set the `email_verified` attribute.
• Data Observability sync lag alert no longer triggers a false positive when a sync returns no data.

• DB2 and BigQuery connectors now support parallel chunk workers for initial sync, splitting large tables across concurrent threads and reducing time-to-first-value when onboarding new sources or backfilling historical data.