Quick Insight
Data integration is the process of combining data from disparate sources into a unified, consistent view for analysis or operations. Common methods include ETL and ELT, data virtualization, change data capture, API-based integration, and streaming. It requires schema mapping, entity resolution, and harmonizing formats, units, and identifiers so that records from different systems line up correctly.
How Data Integration Works
- Identify sources: CRMs, ERPs, spreadsheets, apps, and external data providers.
- Extract: Pull data out through APIs, database connections, or file transfers.
- Map schemas: Decide that “cust_id” in one system means the same as “customer_number” in another.
- Resolve entities: Recognize when two records describe the same customer, product, or place.
- Transform: Standardize formats, currencies, dates, and units.
- Load and serve: Deliver the combined data to a warehouse, lake, or application.
- Monitor: Catch failures and schema changes before they break reports.
Types of Data Integration
- ETL (extract, transform, load): Data is cleaned before it lands. Common for structured reporting.
- ELT (extract, load, transform): Raw data lands first and is transformed inside a cloud warehouse.
- Data virtualization: Data stays in place and is queried as if it were combined.
- Change data capture: Only new or changed records are synced, keeping systems current.
- Streaming: Events flow continuously for near real-time use.
Integration Challenges with Location Data
- Same place, different names: One source says “Walmart Supercenter,” another “Walmart #2241.”
- Coordinate systems: Datasets in different projections won’t line up on a map until converted.
- Spatial matching: Joining points to areas, such as stores to trade areas, needs spatial joins rather than simple key matches.
- Delivery fit: Data is easier to integrate when it arrives in the environment you already use. Providers that deliver directly into Snowflake, Databricks, ArcGIS, or CARTO skip a conversion step.
Shared identifiers for places make matching far more reliable, a theme covered in our guide to geospatial data.
Why Data Integration Matters
- One view: Teams stop reconciling numbers from separate systems by hand.
- Better analysis: Combining internal sales with external POI data shows performance in market context.
- Fewer silos: Departments work from the same facts.
How Data Integration Compares
- Data integration: Combines whole datasets into one view.
- Data enrichment: Adds specific attributes to existing records.
- Data ingestion: Moves data into a system, without necessarily combining it with anything.
Frequently Asked Questions
1. Is data integration a one-time project?
No. Sources change their schemas, add fields, and update records, so integrations need ongoing maintenance.
2. Should we build integrations or buy a tool?
Buy for common sources like popular SaaS apps, where prebuilt connectors exist. Build when sources are unusual or the logic is specific to your business.
3. How do you integrate data with no shared ID?
Through entity resolution: matching on combinations of name, address, and coordinates, then reviewing uncertain matches.
4. What usually breaks integrations?
Unannounced changes in a source system, such as renamed fields, new formats, or changed APIs.
5. Can data integration happen in real time?
Yes, with change data capture or streaming, but it costs more and adds complexity, so many teams reserve it for time-sensitive use cases.
