Solutions / Use case

Data integration, from every source to the warehouse and back.

Data moves between apps, databases, files and the warehouse all day. When a feed misses a delete or a field changes upstream, reports drift and nobody hears until month end. Tray.ai runs every feed in one place, so the numbers match wherever people look.

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One corrected invoice moving through data integration: changed in NetSuite, landed raw in AWS S3, updated in a Snowflake finance table, checked for quality, shown on a Looker dashboard, and the warehouse totals reconciled back to NetSuite.

The short answer

What is data integration?

Data integration is the work of moving data between the systems a business runs on and keeping it correct as it changes: capturing changes from databases and apps, replacing file feeds, delivering events, loading the warehouse and serving data back out through APIs. Most of it runs on scheduled jobs nobody watches, and most failures are silent: a missed delete, a renamed field, a feed that stopped without an error.

  1. 1 Capture changes
  2. 2 Replace file feeds
  3. 3 Deliver events
  4. 4 Load the warehouse
  5. 5 Serve through an API
  6. 6 Absorb source changes
  7. 7 Monitor quality
for an integration project at Airbnb, measured against MuleSoft
8 weeks → 1 week
lower integration cost at Yext than on its legacy platform
60%
synced for an enterprise software leader's reporting, up from 2,500 a day
2M records/hour
less data loaded at AVID Property Group, with reports refreshed every 15 minutes
73%

The data integration process, stage by stage

Seven stages, each with what breaks and a how-to guide for building it.

  1. 1

    Capture changes from databases

    Every insert, update and delete in a production database is read as it happens and carried to the target in order.

    Where it breaks: A nightly query on a modified date never sees a deleted row, so the warehouse keeps it forever.

  2. 2

    Move file feeds onto APIs

    Nightly file drops are replaced by API calls, run side by side until the two agree, then switched one record type at a time.

    Where it breaks: A row missing from tonight's file meant "deleted", and the new API feed never learned that.

  3. 3

    Deliver events in real time

    One event from a source system reaches every app that needs it, each with its own retries.

    Where it breaks: One slow app holds up the rest, the sender gives up and sends again, and everyone gets the event twice.

  4. 4

    Load business apps into the warehouse

    Records that changed in the ERP and other apps land raw in the warehouse, then become tables finance and analytics can trust.

    Where it breaks: A reversed journal overwrites the original, the row count matches and the balance does not.

  5. 5

    Serve data through one API

    Apps and teams read customer, order and account data through one stable API instead of calling each source directly.

    Where it breaks: A field renamed in one source breaks every app that called it, all on the same morning.

  6. 6

    Handle changes to source structure

    New, renamed and removed fields are spotted before a pipeline runs, and their owners hear what depends on them.

    Where it breaks: A renamed picklist value fails nothing, and a dashboard quietly drops a third of its rows.

  7. 7

    Monitor quality and freshness

    Checks on the tables that drive decisions, each with an owner, run after every load.

    Where it breaks: The alert goes to a shared channel, nobody owns the table, and the stale number reaches the board deck.

Three ways to run data integration

  • Scripts and scheduled jobs. Quick to write for one feed. Each has its own retries, logging and credentials, and often the person who wrote it is the only one who knows it exists.
  • A separate tool for each job. An ETL tool for the warehouse, an integration platform for apps, a gateway for APIs. Each works on its own, and the same customer record now passes through three tools with three sets of access rules.
  • One platform for all of it. Database changes, file feeds, events, warehouse loads and APIs built in one place, with the same error handling, logging and access control. This is Tray.ai: data and platform teams build the pipelines, IT governs them.

The Tray.ai platform behind it

Tray.ai is the enterprise orchestration platform for data and AI. These are the parts of it that run data integration.

Intelligent Integration

The iPaaS built for the AI era.

iPaaS Governance

AI governance built in, not bolted together.

The metrics data integration moves

What data and platform teams measure, and which stage each number depends on.

Data freshness
Time between a change in the source system and the change being correct in the target.
What moves it Reading changes as they happen instead of waiting for a nightly export. How to build a change data capture pipeline 
Reconciliation differences
Records or totals that disagree between the source and the target.
What moves it A daily comparison of totals against the source, with each difference sent to an owner. How to build an ERP to warehouse sync 
Failed deliveries
Events a consuming app never received, or received more than once.
What moves it Delivering to each app on its own, with its own retries and a place for what fails. How to build webhook fan-out 
Pipeline breaks from upstream changes
Runs that stopped, or ran and produced the wrong result, because a source field changed.
What moves it Comparing source structure before each run and telling the owner first. How to build schema change management 
Time to detect a data problem
Time from bad data landing to the person who owns it knowing.
What moves it Checks that run after every load and alert a named owner. How to build data quality monitoring 
Time to connect a new consumer
Days from a team asking for data to their app reading it in production.
What moves it One stable API in front of the sources, so a new consumer needs no new integration. How to build an API facade 

Data integration connectors

The systems data integration runs on, grouped by the seat each one fills. Each links to its connector page.

Business apps

Where customer, order, finance and people data starts.

Databases

Where production applications write, and where changes are captured as they happen.

Warehouses and lakes

Where data lands for reporting, analytics and AI.

Streams and queues

Where events travel between systems in real time.

Reporting, monitoring and alerts

Where the data is read, and where problems reach the people who fix them.

Connections between them

Field mapping, templates and common problems for the pairings data integration depends on.

Anything not listed connects through the full connector library, or directly through its API.

Teams running data integration on Tray.ai

“With Tray, we can build integrations 3x faster than with our legacy platform, giving us the agility we needed.”

— Tulasi Donthireddy, Director of IT and Business Applications, Yext
Read the study

Processes next to data integration

These run on the same pipelines and the same warehouse without being stages of them.

Data operations

How to build a CRM to warehouse sync

Capture history instead of current state, handle deletes and field changes, land raw then model, and prove the row counts. The Headless prompts that build it.

Data operations

How to build reverse ETL from the warehouse

Push modelled data back into the tools people work in, syncing deltas, respecting field ownership, and never overwriting a human. The prompts that build it.

Data operations

How to build a customer 360 and master data sync

Pick a survivorship order per attribute, resolve identity on more than email, and publish a golden record every system can point at. The prompts.

Customer success

How to build a product usage to CRM sync

Send the three signals that change a conversation, aggregated and labelled, without turning the CRM into an analytics tool. The Headless prompts that build it.

AI operations

How to build a knowledge base to vector sync

Chunk on structure, carry permissions into the index, delete on delete, and re-embed only what changed. The Headless prompts that build it.

AI operations

How to build a document intelligence pipeline

Score confidence per field rather than per document, validate against a system of record, and route only the uncertain fields to a person. The prompts.

Reading and resources

On-demand webinar

Unblocking the AI data supply chain bottleneck

Learn how to modernize AI data pipelines with in-flight SQL transformation. See Data Engineering and SQL Transformer in action.

eBook

Five keys to integrating Snowflake and Databricks into your business

Operationalize Snowflake and Databricks for reverse ETL. Discover five integration principles for building AI-ready, real-time workflows.

eBook

How enterprise teams are scaling integration for modern workloads

IT and platform leaders are scaling integration to support modern workloads, AI ops, and cross-team orchestration—with less code and more speed.

On-demand webinar

How Yext cut integration costs by 60% in 90 days

Yext delivered 100+ integrations in 3 months. See how they did it—and why their old platform was slowing everything down.

From the blog

BlogAI is exposing the limits of legacy data pipelinesBlogWhat is API integration? A practical guideBlogReduce your integration technical debt with a modern iPaaSBlogiPaaS migration has always been a time and cost problem. It just got 95% faster.BlogTurning real-time event streams into game-changing valueBlogBasic ETL on the Tray platform tutorialBlogPower data-driven insights with AWS automation for cloud storageBlogHow do APIs work?BlogiPaaS vs RPA: What’s the difference?BlogReverse ETL: why reps never see warehouse dataBlogReverse ETL vs ETL: what changes, and for whomBlogReverse ETL use cases, team by teamBlogSalesforce to Snowflake: why the copy driftsBlogData integration process: where pipelines breakBlogChange data capture: why CDC falls behindBlogSFTP to API: why file feeds outlive migrationsBlogSchema changes: why one renamed field breaks fiveBlogData quality monitoring: who sees bad data firstBlogWhat are webhooks, and why do they fire twice?

Speaking the same language

Definitions for the terms that come up in this conversation.

Data integration

The practice of moving, normalizing, and synchronizing data between systems — so one application's data is usable in another.

ETL

Extract, Transform, Load — the process of pulling data from source systems, reshaping it, and loading it into a destination like a data warehouse.

Change data capture

A technique that tracks and streams database changes in real time — inserts, updates, and deletes — rather than re-pulling the full dataset on a schedule.

API management

The tools and processes for publishing, securing, monitoring, and versioning APIs — so teams can expose data and services without custom infrastructure.

iPaaS

Integration Platform as a Service — a cloud-hosted platform for building, running, and managing integrations between applications.

Frequently asked questions

What is the difference between data integration and ETL?

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ETL (extract, transform, load) is one pattern inside data integration, usually a scheduled load into the warehouse. Data integration also covers capturing database changes as they happen, delivering events between apps, replacing file feeds and serving data through APIs.

Should data integration run in real time or in batches?

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Both. Changes that people act on, like an order, a payment or a deleted record, should move as they happen. A scheduled reconciliation then compares source and target, because a missed change sends no error and nothing else will notice it.

We run MuleSoft, Boomi or Informatica. What does moving look like?

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Most teams move one use case at a time rather than all at once. Yext moved more than 100 integrations off its legacy platform in 3 months and now builds integrations 3 times faster. Airbnb, measuring Tray.ai against MuleSoft, turned an 8-week integration project into a week's work.

Which systems does Tray.ai connect for data integration?

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Business apps (Salesforce, NetSuite, Workday, SAP Business One, ServiceNow, Zendesk, Stripe, HubSpot), databases (PostgreSQL, MySQL, Microsoft SQL Server, MongoDB, and others through JDBC), warehouses and lakes (Snowflake, Google BigQuery, Amazon Redshift, Databricks), file storage (Amazon S3, Google Cloud Storage, Azure Blob Storage, Box, SharePoint) and streams (Kafka, Amazon Kinesis, Amazon SQS, RabbitMQ, IBM MQ). Anything without a connector connects through its API.

Do we have to replace our warehouse or our existing pipelines?

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No. Tray.ai connects the systems you already run and carries the data between them. Teams usually start with the feeds that break most often and move the rest as they come up for change.

Where do AI agents fit in data integration?

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Agents need the same data people do. The pipelines that load the warehouse can also keep a knowledge base current, and an API in front of your sources gives an agent one controlled way to read them instead of a credential for each system.

How is the data kept secure in transit?

+

Tray.ai is SOC 1 and SOC 2 Type 2 audited across the platform. Credentials are held by the platform rather than in scripts, access is set by role, and every run is logged, so a number in the warehouse can be traced to the run that produced it.

Last reviewed 2 October 2026.

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