Intelligent iPaaS / Data Integration
Data integration without the complexity.
Get data where it needs to go — in real time, on a schedule, or on demand. Visual tooling, flexible transformation, and the connectivity to reach any system in your stack.
What it does.
Visual data pipelines
Build data flows without writing infrastructure code. Visual tooling with the depth to handle complex transformations, filtering, and routing.
Real-time sync
Keep systems in sync as data changes — bidirectional, with conflict resolution, field mapping, and deduplication handled for you.
Flexible scheduling
Run on a schedule, trigger from an event, or push in real time. Mix and match per pipeline based on what the data actually needs.
Data mapping & transformation
Map, reshape, enrich, and filter data in transit. Visual field mapping for most cases; custom transformation logic available where you need it.
Any source, any destination
Salesforce, Zendesk, Stripe into Snowflake, Redshift, BigQuery, or Databricks — data flows wherever it needs to go, across the full SaaS and warehouse stack.
Elastic processing
Pagination, rate limits, retries, and partial failures handled automatically. Pipelines scale with your data and run reliably without manual intervention.
Go further with Data Engineering
Data integration moves the data. Data Engineering prepares it — in the same pipeline, on the same platform, with no separate ETL infrastructure to manage. The SQL Transformer lets you reshape, join, and transform bulk data in-flight using ANSI SQL, directly where your agents and workflows execute.
- SQL Transformer: reshape, join, and transform in-flight using ANSI SQL
- No separate ETL system — transformation happens in the execution layer
- Feeds directly to Snowflake, Redshift, BigQuery, and Databricks
- One pipeline from raw data to agent-ready output
What you can build
See what teams build with Data Integration.
-
Salesforce in Snowflake. Always.
Sync accounts, contacts, and opportunities to Snowflake in real time. Sales and data teams stop arguing about whose number is right.
-
One schema. Six sources. No drama.
Pull from Salesforce, Zendesk, Stripe, and Marketo into a single Snowflake schema. One source of truth — not six exports stitched together in a spreadsheet.
-
Product events. Warehouse-ready. No tickets.
Stream product events into Snowflake or BigQuery as they happen. ML and analytics teams get fresh data without waiting on engineering.
-
NetSuite meets Salesforce. Finally.
Orders, invoices, and account data flowing bidirectionally — conflict resolution included. Finance and sales work from the same record.
-
Kill the stale report.
Pull and reshape operational data into a clean reporting schema on a schedule. Analysts get what they need without filing a ticket or waiting a week.
-
Raw data. Any destination. Clean on arrival.
Land data from 20+ SaaS sources into Snowflake, Redshift, BigQuery, or Databricks — transformations applied in transit.
Frequently asked questions
How much technical skill does this require? +
Most data flows are built visually — no code required. Custom transformation logic is available in JavaScript where you need it, but the majority of use cases don't get there.
How does it handle schema changes? +
Field mapping lives in the workflow. When a source API changes, you update the mapping in one place. Alerts surface unexpected schema drift before it breaks anything.
What scale can it handle? +
The platform runs at enterprise volume. Customers move millions of records daily, process billions of transactions annually, and run hundreds of concurrent pipelines — without running out of headroom.
One platform
No frankenware. Just one platform.
Process Automation, Data Integration, Connectivity, API Management, IDP, and Embedded Integrations — all on the same platform, sharing the same connector library, governance layer, and data model. Not acquired and stitched. Built as one.
See Data Integration in action.
Walk through a scenario from your stack with a Tray.ai expert.