Data sources are the foundational inputs for any Business Intelligence (BI) architecture. Understanding the types of data sources available is critical for designing effective data pipelines and selecting appropriate tools for extraction and transformation.
These are operational databases that record day-to-day business transactions.
- Examples: MySQL, PostgreSQL, Microsoft SQL Server, Oracle DB
- Use in BI: Sales records, inventory updates, customer orders
- Characteristics: Highly normalized, optimized for write operations
Structured or semi-structured files used for data exchange between systems.
- Examples: CSV, Excel (.xlsx), JSON, XML, TXT files
- Use in BI: Importing legacy data, receiving data from external partners
- Characteristics: Portable but lack referential integrity
Programmatic interfaces that expose real-time or near-real-time data from external services.
- Examples: REST APIs, GraphQL endpoints, SOAP services
- Use in BI: Social media analytics, financial market data, weather data
- Characteristics: Dynamic, requires authentication, rate-limited
Continuous, real-time data feeds generated by devices, applications, or sensors.
- Examples: Apache Kafka, AWS Kinesis, IoT sensor feeds
- Use in BI: Real-time dashboards, fraud detection, operational monitoring
- Characteristics: High velocity, requires stream processing tools
Centralized repositories that store raw data at scale in its native format.
- Examples: AWS S3, Google Cloud Storage, Azure Data Lake
- Use in BI: Storing unstructured and semi-structured data before transformation
- Characteristics: Scalable, cost-effective, schema-on-read
Choosing the right data source type depends on the business use case, the required data freshness, and the volume of data involved. A mature BI architecture typically integrates multiple source types through a well-designed data pipeline.