Design and Implement Data Integration

Key Concepts

• Data Integration
• ETL (Extract, Transform, Load)
• ELT (Extract, Load, Transform)
• Data Pipelines
• Data Orchestration

Data Integration

Data Integration is the process of combining data from different sources into a unified view. This is essential for organizations to make informed decisions based on comprehensive data insights.

Example: Think of data integration as assembling a puzzle. Each piece (data source) comes from a different place, but when combined, they create a complete picture (unified view).

ETL (Extract, Transform, Load)

ETL is a traditional data integration process where data is extracted from source systems, transformed to fit the target schema, and then loaded into the target system. This process is commonly used for data warehousing.

Example: ETL is like preparing ingredients for a recipe. You extract the ingredients (data) from their original packaging (source systems), transform them (chop, mix) to fit the recipe (target schema), and then load them into the cooking pot (target system).

ELT (Extract, Load, Transform)

ELT is a modern data integration approach where data is first extracted from source systems and loaded into the target system, and then transformed within the target system. This method leverages the processing power of modern data warehouses.

Example: ELT is like preparing a salad. You extract the vegetables (data) from their packaging (source systems), load them onto the plate (target system), and then transform them (wash, chop) on the plate itself.

Data Pipelines

Data Pipelines are automated workflows that move data from one system to another. They ensure that data is consistently and reliably transferred, often involving multiple stages of processing.

Example: Data pipelines are like assembly lines in a factory. Each station (stage) performs a specific task (processing), ensuring that the product (data) moves smoothly from start to finish.

Data Orchestration

Data Orchestration involves managing and coordinating multiple data pipelines to ensure they work together seamlessly. This includes scheduling, monitoring, and managing dependencies between pipelines.

Example: Data orchestration is like conducting an orchestra. The conductor (orchestration tool) ensures that each musician (data pipeline) plays their part at the right time, creating a harmonious performance (seamless data flow).