Question 1

What's the difference between Data Integration and ETL?

Accepted Answer

ETL (Extract, Transform, Load) is a common method used to do data integration. Data Integration is the broader goal: combining data from multiple systems into a unified, usable view. ETL is one approach (often batch). Other approaches include ELT (transform after loading into a warehouse), data virtualization, and real-time streaming integration.

Question 2

When should I use Data Integration?

Accepted Answer

Use data integration when you need a consistent view of data across systems—for example, combining CRM leads, billing records, and product usage events to measure customer health. It’s especially useful when teams are spending time manually exporting spreadsheets, reports disagree because data definitions differ, or you need automated pipelines feeding a data warehouse/lake for dashboards, ML, or operational apps.

Question 3

How much does Data Integration cost?

Accepted Answer

Costs depend on (1) data volume moved and processed, (2) how often pipelines run (batch frequency or streaming), (3) transformation compute (e.g., Spark jobs, dataflow activities), (4) connector/licensing needs (some SaaS connectors can add cost), and (5) storage and network egress. Managed services typically charge for orchestration/activity runs plus compute used for transformations, so a small nightly batch pipeline can be inexpensive, while high-throughput streaming with heavy transformations can be significantly more.

Data Integration

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