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.