Data Integration
Definition
The process of combining data from different sources into a unified view for analysis and applications, enhancing decision-making capabilities.
Use Cases
- Netflix: Unifying application and viewing-event data for analytics and reporting across teams. — Netflix has publicly described using Apache Kafka for event ingestion and Apache Spark-based processing to transform and prepare large-scale datasets for analytics platforms and internal dashboards. (Faster access to consistent datasets for analytics, enabling teams to monitor performance and make data-driven product decisions at scale.)
- The Home Depot: Integrating retail, supply chain, and digital channel data to support enterprise analytics and operational reporting. — The Home Depot has publicly discussed using cloud-based data platforms and streaming/batch data pipelines (including Kafka and Spark ecosystems) to move and transform data into centralized analytics environments. (Improved visibility across channels and operations, supporting better forecasting, inventory decisions, and business reporting.)
Provider Equivalents
- AWS: AWS Glue
- Azure: Azure Data Factory
- GCP: Cloud Data Fusion
- OCI: OCI Data Integration
Frequently Asked Questions
- What's the difference between Data Integration and ETL?
- 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.
- When should I use Data Integration?
- 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.
- How much does Data Integration cost?
- 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.
Category: analytics
Difficulty: intermediate
Related Terms
See Also