Data Integration for Modern Data Teams
Your data is scattered across disconnected databases, apps, and files, and every new pipeline turns into another engineering ticket.
DataFuseAI connects the databases, cloud warehouses, files, and REST APIs your data already lives in — PostgreSQL, MySQL, Oracle, MongoDB, Snowflake, BigQuery, Redshift, S3, and more — through pre-built connectors organized by category. You validate each connection with a built-in Connection Check before it goes live, without opening an engineering ticket.
Your data already lives in too many places
You are not missing a system — you have too many. Here is where your data sits today, and what connects it.
The average company now runs 101 apps, crossing 100 for the first time — so your data is spread across more separate systems than ever — Okta, 2025.
Even the systems meant to unify data are not universal: 53.47% of EU enterprises used ERP, CRM, or BI software in 2025, and adoption drops to 41.08% at small enterprises versus 88.71% at large ones — Eurostat, 2026.
DataFuseAI ships pre-built connectors across 10 source categories — relational (RDBMS), NoSQL, cloud data warehouses, AWS, Azure, GCP, file, FTP/SFTP, S3, and REST API — DataFuseAI, 2026.
That spans the databases and warehouses you already run — PostgreSQL, MySQL, Oracle, SQL Server, IBM DB2, MongoDB, Snowflake, BigQuery, Redshift, and more — DataFuseAI, 2026.
Every connection is validated before it goes live with a built-in Connection Check, and database and REST API sources land in the same workspace — DataFuseAI, 2026.
Every one of those systems holds a slice of your data. DataFuseAI is where you connect them.
Connect every source your data already lives in
DataFuseAI connects to your relational databases, NoSQL stores, cloud data warehouses, cloud-hosted database services on AWS, Azure, and GCP, and your file, FTP/SFTP, S3, and REST API sources. Each connector is pre-built and organized by category on one screen, so you pick a source instead of writing integration code for it.
Your data does not sit in one kind of system, so the connector library does not either. It is grouped by the categories your sources actually fall into, with the marquee databases and warehouses your team already runs.
Whether your data sits in an on-prem Postgres box, a Snowflake warehouse, an S3 bucket, or behind a REST API, the connector is already built. You can also see the full connector list.
From source to validated connection
Connecting a source in DataFuseAI is three steps: choose a connector, configure and validate the connection, then ingest the data — and it is the same three steps whether the source is a relational database, a NoSQL store, a cloud warehouse, a file source, or a REST API. The Connection Check confirms the source works before it goes live, so you find a bad credential or host during setup, not in a broken pipeline. Setup runs within minutes per connection.
Pick your source from the category grid on the Driver Management screen — a relational database, a warehouse, a cloud-hosted database service, a file source, or a REST API. Outcome: DataFuseAI loads the connection profile for that source type, so you configure the fields that source needs and nothing else.
Enter the connection profile the source needs — host, port, database, and credentials for a relational database or cloud warehouse; a connection string for a NoSQL store; a base URL and auth type for an API. Each database and warehouse connection pins a specific driver build, so connectivity is controlled and versioned rather than a black box — the PostgreSQL example below uses driver Postgresql 42.7.3 (class org.postgresql.Driver), and the same versioned-driver approach covers MySQL, Oracle, SQL Server, IBM DB2, and the Snowflake, BigQuery, and Redshift warehouses. Then run the Connection Check: a multi-step test that returns green success ticks when the source is reachable and the credentials work. Outcome: you confirm any connection before it goes live.
Once the connection is validated, DataFuseAI ingests the data — a table or SQL query from a relational database or cloud warehouse, documents from a NoSQL store, objects from an S3 bucket or file source, or records from a REST API. The API example below sets a base URL and auth type, then defines one or more endpoints — each with an HTTP method and a path — with pagination handled through a next-page field (next_url), so multi-page responses come through in full. Outcome: relational, NoSQL, warehouse, file, and API sources all land in the same workspace, ready for the next step. For a worked example, see how to get insights from API data.
Easy setup within minutes — you connect and validate a live source in one guided flow, not a multi-week engineering project.
Why data teams run their connections here
Data teams run their connections here because every part of the setup is specific and visible: the driver build is pinned per connection, the connection is validated before go-live, API sources are first-class alongside databases, and each workspace keeps its own connection profiles. These are the details that decide whether a connection is trustworthy in production.
Each database and warehouse connection pins a specific driver build — PostgreSQL's Postgresql 42.7.3 is one example, with the same for MySQL, Oracle, SQL Server, IBM DB2, and the cloud warehouses. You know exactly what version talks to each source, which is what keeps a driver upgrade from silently breaking a pipeline — see why vendor lock-in in data infrastructure makes controlled, named drivers matter.
The Connection Check runs a multi-step test with green success ticks before the source is live — a test-before-you-trust step, so a wrong host or credential surfaces in setup, not in a 2 a.m. pipeline failure.
API sources are configured like any other: base URL, auth type, and an endpoints table with HTTP method and next_url pagination. Non-database data is a real source here, not an afterthought.
Connection profiles are scoped to a workspace (for example, the "MSN-Corps" workspace), so separate teams or clients keep their sources and credentials apart.
These are not adjectives on a feature grid — they are the specifics you can see in the product before you trust it with a source. When multiple systems disagree, connecting them cleanly is where reliable data starts.
Statistics reference
Every figure cited on this page, with its named source, year, and a direct link. The connector-category figure is first-party — verified from the DataFuseAI product, not an external report. External figures are Eurostat and Okta, opened and verified at the links below.
| Statistic | Figure & context | Source | Year |
|---|---|---|---|
| App sprawl | The average company now uses 101 apps, crossing 100 for the first time — data spread across more separate systems than ever | Okta, "Businesses at Work 2025" | 2025 |
| Unifying-system gap | 53.47% of EU enterprises used ERP/CRM/BI in 2025; adoption drops to 41.08% at small enterprises vs 88.71% at large ones | Eurostat, "Digital economy and society statistics – enterprises" | 2026 |
| Connector categories (first-party) | Pre-built connectors across 10 source categories: RDBMS, NoSQL, cloud warehouses, AWS, Azure, GCP, FILE, FTP, SFTP, S3, API | DataFuseAI (first-party, product-verified) | 2026 |
Frequently Asked Questions About Data Integration
A data integration platform is the software layer that connects your separate data sources — databases, cloud warehouses, files, and APIs — and brings that data into one place your team can work from. DataFuseAI does this with pre-built connectors organized by category, a Connection Check that validates each source before it goes live, and support for both database and REST API ingestion in the same workspace.
Your data rarely lives in one system. It is split across databases, SaaS apps, files, and APIs, so answering a single question means pulling from several disconnected places by hand. Data integration connects those sources into one workflow, so your team stops rebuilding the same manual pulls and every source stays queryable from one platform.
Common approaches include ETL and ELT (extract, transform, and load in different orders), data replication, change data capture, and API-based integration. Which one fits depends on your sources and how fresh the data must be. DataFuseAI supports database connections and REST API ingestion through pre-built connectors, so you can combine relational, NoSQL, warehouse, file, and API sources in one workspace.
ETL — extract, transform, load — is one method for moving and reshaping data. Data integration is the broader goal: connecting all your sources so the data works together, whether that happens through ETL, ELT, replication, or API calls. In DataFuseAI, connecting a validated source is the first step, and transformation and pipeline automation follow from there.
In DataFuseAI it works in three steps. You choose a connector from the category grid, configure the connection profile and run the built-in Connection Check to validate it, then ingest the data through a database connection or a REST API with endpoints and pagination. The validation step confirms the source is reachable and the credentials work before it goes live.
You add an API source, set its base URL and authentication type, then define one or more endpoints, each with an HTTP method and a path. DataFuseAI handles pagination through a next-page field such as next_url, so multi-page API responses come through in full. This is the same REST API configuration DataFuseAI shows in its connection profile screen.
Yes. Connecting a source in DataFuseAI is a guided, form-based step: pick a connector, fill in the connection profile, and run the Connection Check. There is no custom pipeline code to write, so a pipeline change stops being an engineering ticket and analytics or BI leads can connect a source without waiting on a backlog.
It depends on how many sources you are connecting and your access to credentials for each one. A single database or API connection is a guided setup: choose the connector, enter the profile, validate it, and start ingesting. DataFuseAI is built for setup within minutes per connection rather than a multi-week engineering project.