A data connector is software that links a source system, such as an ERP, CRM, accounting package or database, to a destination, such as a data warehouse, BI tool or AI data analyst. It handles the login, reads the data on a schedule or on demand, and delivers it, so nobody has to export spreadsheets by hand.
Sylus connects to systems like Microsoft Dynamics 365, Odoo and Oracle NetSuite, builds a semantic understanding of your business, and lets anyone ask questions about that data in plain English.
Key takeaways
A connector does three jobs: it signs in to a system, reads its data, and delivers that data somewhere useful.
Integration tools copy data into a central store. AI analytics tools let people ask questions of it. Many teams need both.
You don’t always need a data warehouse first. Some AI analysts query your systems directly.
Niche and regional ERPs often have custom fields and workflows that generic connectors miss, so test with your own data.
What is a data connector?
A data connector is a piece of software that creates a repeatable link between two systems. On one side is a source, where data is created: your ERP, CRM, accounting system, point of sale, online store or ad accounts. On the other side is a destination, where data is used: a data warehouse, a BI tool, a spreadsheet or an AI data analyst.
Without a connector, moving data between them means manual exports. Someone downloads a CSV from the ERP every Monday, pastes it into a spreadsheet and rebuilds the same report. A connector does that work automatically, the same way every time.
Connectors are often confused with three related things:
APIs are the doors a system opens to the outside world. A connector is the software that knocks on that door, signs in and asks for the data.
ETL and ELT platforms are larger tools that move and reshape data. Connectors are usually one component inside them.
Data pipelines are the full workflow from source to report. The connector is the first step in the pipeline.
How do data connectors work?
Most connectors follow the same three stages.
1. Connect and authenticate
The connector signs in to the source system with an API key, an OAuth login, a database user or a certificate. This is where most of the setup happens, and where most failures start: an expired token, a changed password or a blocked network route will stop data flowing. Read-only access is the safer default for analytics, because the connector only needs to read.
2. Read the data
There are two common ways to read data:
Full extraction pulls the whole dataset every time. It is simple, but slow and heavy on large tables. It is common with older systems and flat files that don’t track changes.
Incremental extraction pulls only records that are new or changed since the last run, using an “updated at” timestamp, a sequence number or the database’s change log. It is faster and lighter, but the source has to track changes.
3. Deliver the data
If the connector writes data into a destination, it uses one of three load modes:
Replace overwrites the destination on every run.
Append adds new rows without touching old ones.
Merge (also called upsert) inserts new rows and updates changed ones, matched on a key such as an invoice number.
The read mode and the load mode have to match. Incremental reads with a replace load lose history, because each run overwrites everything with only the latest changes. Full reads with an append load duplicate the whole table on every run. Incremental reads with a merge load is the usual safe pattern.
Some tools skip the copy entirely. Instead of loading data into a warehouse, they send a query to the source when you ask a question and return only the result. Whether a tool copies your data or queries it live is one of the first things to check, and the FAQ at the end covers how Sylus handles it.
What are the main types of data integration?
Connectors are the building blocks. How you combine them is your integration approach. These are the six you will meet most often.
Integration type | How it works | When to use it |
|---|---|---|
ETL (extract, transform, load) | Data is read from the source, cleaned and reshaped on a separate server, then loaded into the destination. | When data must be cleaned or masked before it lands, or the destination has little compute of its own. |
ELT (extract, load, transform) | Raw data is loaded into a cloud warehouse first, then reshaped there with SQL. | When you have a cloud warehouse such as Snowflake or BigQuery and want to keep the raw history. |
Change data capture (CDC) | Only changed rows are read from the database’s change log and passed on continuously. | When you need fresh data from a busy transactional database without heavy full reloads. |
API-based integration | Software calls each application’s API to read or write records, on a schedule or when events happen. | For SaaS tools such as CRMs, accounting apps and ad platforms that expose APIs but not their databases. |
Data virtualisation (live query) | Data stays in the source. A query layer reads it on demand and combines the results at question time. | When you want answers without building and maintaining a copy of every system. |
Streaming | Events are sent continuously through a stream such as Apache Kafka and processed as they arrive. | For high-volume, time-sensitive events such as clickstreams, sensor readings or payments. |
Most businesses end up using more than one. A finance team might load its ERP into a warehouse every night with ELT, while a sales manager queries the CRM live.
Which business systems can you connect, and what can you ask?
For business teams, the useful question is not which protocol a connector uses but what it lets you find out. The table below lists common system categories, example systems and a question a manager could ask once the data is connected.
System category | Example systems | Example question a business user can ask |
|---|---|---|
ERP | “Which product lines had the lowest gross margin last quarter?” | |
CRM | “How many deals moved to closed-won this month, and which reps closed them?” | |
Accounting | “Which customers have invoices more than 60 days overdue?” | |
Point of sale (POS) | “Which stores sold the most units last weekend, and which items drove it?” | |
E-commerce | “What share of customers from the last 90 days have ordered twice?” | |
Ads and marketing | “Which campaigns brought in customers with the highest order value?” | |
Databases | “What were weekly active users by plan over the last six months?” | |
Spreadsheets | “How does this month’s sales target sheet compare with actual sales in the ERP?” |
The most valuable questions usually cross categories: ad spend in Meta Ads joined to orders in Shopify, or sales targets in a spreadsheet compared with invoices in the ERP. That only works when every system is connected to the same place. You can browse every supported system on the Sylus integrations page.
What is the difference between a data integration tool and an AI analytics tool?
A data integration tool moves data. Products in this category, such as Fivetran, Airbyte or MuleSoft, connect to source systems and copy or sync records into a central store, usually a data warehouse. Their users are mostly data engineers, and their output is tables, not answers.
An AI analytics tool answers questions. It reads data, either from a warehouse or directly from source systems, and turns a question such as “why did margin drop in March?” into a query, a chart and an explanation. Its users are managers, finance teams and analysts. Our guide to the best AI tools for business analytics compares the main options.
Many organisations need both: integration to consolidate data, and analytics to make it useful. Smaller teams without a data engineer often start with an AI analyst that connects to their systems directly, then add a warehouse later if data volumes or history require it.
Do you need a data warehouse before connecting an AI analytics tool?
Not always. A warehouse helps when you have many systems, large volumes or years of history that you want in one place with consistent definitions. Clean, centralised data also makes AI answers more reliable, because the tool has fewer conflicting tables to choose from.
But a warehouse is a project in its own right: someone has to build the pipelines, model the tables and keep them running. If your goal is to get answers from an ERP, CRM and accounting system this quarter, check whether the analytics tool can connect to those systems directly. If it can, start there and add a warehouse when you outgrow it. If you already run a warehouse such as Snowflake or BigQuery, connect the analytics tool to it as well.
Why do standard connectors fall short for niche and regional ERPs?
Most pre-built connectors are designed around mainstream ERP data models. Niche and regional systems, and heavily customised installs of mainstream ones, break those assumptions. This comes up often in the UAE and the wider GCC, where retailers frequently run LS Retail on Microsoft Dynamics 365 Business Central and growing companies often extend Odoo with custom modules. If that is your setup, see how Sylus works for UAE and GCC businesses.
Typical failure scenarios look like this:
Custom fields and statuses are skipped. A custom “pending quality hold” status or a branch-specific field doesn’t exist in the connector’s fixed schema, so those records never arrive.
Custom objects can’t be mapped. Add-on modules create their own tables, and a generic connector doesn’t know how they join to sales or stock.
Schema changes break things quietly. An ERP upgrade renames a field, and the connector keeps running but returns blanks.
API limits get hit. Some ERPs throttle API calls or page results in non-standard ways, so large historical pulls stall.
When this happens, someone ends up writing transformation scripts by hand, which defeats the point of having a connector.
How do you evaluate a connector for your ERP?
Ask the vendor these questions before you commit, and test with your own data rather than a demo dataset:
Does it support your exact ERP version and the modules you use, including custom ones?
Does it read custom fields, custom statuses and add-on tables, or only the standard schema?
What happens when your ERP is upgraded or its schema changes? Does the connector pick up new fields?
Is access read-only, and can you limit which tables and fields it can see?
How does it authenticate (OAuth, API key or database user), and is the connection encrypted?
Does it read data live, sync it on a schedule, or copy it into the vendor’s own storage?
How does it handle API rate limits and large historical loads?
Who maintains the connector when the ERP vendor changes its API, and how quickly are fixes released?
Two broader rules from data integration projects apply too. Check connector coverage for your specific sources and destinations rather than the size of the catalogue. And model the total cost of ownership, including maintenance and support, not just the licence fee.
What is a semantic layer, and why does it matter for AI analysis?
A connector puts data within reach. It doesn’t tell anyone what the data means. A semantic layer does: it is a shared set of business definitions that sits between raw tables and the people (or AI) asking questions. It records which table holds invoices, how “net revenue” is calculated, whether refunds are excluded and how a customer in the CRM matches a customer in the ERP.
This matters for AI because a language model reading raw tables has to guess. Column names such as amt_2 or cust_ref don’t explain themselves, and two systems may define “customer” differently. With a semantic layer, the AI works from agreed definitions, so two people asking the same question get the same number. Our guide to conversational data query tools explains why this is the biggest factor in answer accuracy.
Sylus builds a semantic understanding of your business from the systems you connect. Your team can save approved metric definitions in one catalog, and Sylus answers from them first. Every answer shows the SQL query behind it, so you can check how it was calculated.
Frequently asked questions
What is a data connector?
A data connector is software that links a source system, such as an ERP, CRM or database, to a destination, such as a warehouse, BI tool or AI analyst. It signs in, reads the data and delivers it automatically, so teams don’t have to export and re-import files by hand.
How do data connectors work?
A connector first authenticates with the source system using OAuth, an API key or database credentials. It then reads data, either in full or only what changed since the last run. Finally it delivers the data to a destination, or answers a query directly, depending on the tool.
Do I need a data warehouse before connecting an AI analytics tool?
Not always. A warehouse helps when you have many systems, large volumes or long history to keep in one place. If the AI tool can connect to your ERP, CRM and accounting systems directly, you can start without one and add a warehouse later.
What is the difference between a data integration tool and an AI analytics tool?
A data integration tool moves data from source systems into a central store, and its output is tables. An AI analytics tool answers questions about that data in plain English, with charts and explanations. Many organisations use both.
Can I connect a niche or regional ERP?
Usually, if the ERP exposes an API or read access to its database. The harder part is custom fields, statuses and add-on modules, which generic connectors often miss. Check the vendor’s list of supported systems and test the connector with your own data before you commit.
Is my data copied or queried live?
It depends on the tool. ETL and ELT tools copy data into a warehouse, while live-query tools read the source when you ask. Sylus reads your schema to understand your data, and never copies your tables. When you ask a question, Sylus runs the query in your warehouse and uses only the results needed for that answer.
What is the difference between ETL and ELT?
In ETL, data is transformed on a separate server before it is loaded into the destination. In ELT, raw data is loaded into a cloud warehouse first and transformed there. ELT has become common because cloud warehouses have the computing power to do the transformation themselves.