Design Data Integration Strategies

Key Concepts

• Data Sources and Targets
• Data Integration Patterns
• Data Transformation and Mapping
• Data Orchestration
• Data Quality and Monitoring

Data Sources and Targets

Data sources are the various systems and databases from which data is extracted, while data targets are the systems where the processed data is loaded. Understanding the nature and requirements of both sources and targets is crucial for designing effective integration strategies.

Example: A retail company might have data sources like point-of-sale systems, customer relationship management (CRM) systems, and inventory management systems. The data targets could be a data warehouse for reporting and analytics.

Data Integration Patterns

Data integration patterns define how data is moved and integrated between sources and targets. Common patterns include ETL (Extract, Transform, Load), ELT (Extract, Load, Transform), and real-time integration. Each pattern has its own advantages and use cases.

Example: ETL is commonly used when data needs to be transformed before loading into the target system, such as cleaning and aggregating sales data before loading it into a data warehouse. ELT is useful when the target system has the capability to handle transformations, such as loading raw data into a data lake and transforming it using big data tools.

Data Transformation and Mapping

Data transformation involves converting data from its source format to the target format, ensuring consistency and compatibility. Data mapping defines how data elements from the source are mapped to the target.

Example: When integrating data from a CRM system to a data warehouse, customer names might need to be standardized (e.g., converting "John Doe" to "John D."), and fields like "Customer ID" in the CRM system might be mapped to "Customer_Key" in the data warehouse.

Data Orchestration

Data orchestration is the coordination of multiple data integration tasks to ensure they are executed in the correct order and at the right time. Azure Data Factory is a powerful tool for orchestrating complex data workflows, including data extraction, transformation, and loading.

Example: A financial institution might use Azure Data Factory to orchestrate the daily extraction of transaction data from multiple bank branches, transformation of the data to remove sensitive information, and loading the cleaned data into a central data warehouse.

Data Quality and Monitoring

Data quality and monitoring ensure that the integrated data is accurate, consistent, and reliable. This involves setting up monitoring tools to detect anomalies and implementing data quality checks.

Example: A healthcare provider might implement data quality checks to ensure that patient records are complete and accurate before integrating them into a central patient database. Monitoring tools can alert administrators to any discrepancies or missing data.