7.Matillion ETL Components and Real-World Examples
Matillion provides a rich set of components that you can drag and drop onto the job canvas to build your ETL workflows. These components are categorized based on their functionality:
Orchestration Components
Used to control the flow of your ETL jobs.
– Start: Entry point of an orchestration job.
– End Success / End Failure: Marks the successful or failed completion of a job.
– Run Orchestration / Transformation: Triggers another job from within the current job.
– Python Script: Executes Python code for custom logic or API calls.
– SQL Script: Runs custom SQL queries directly on the data warehouse.
– If / Switch: Implements conditional logic to control job flow.
– Loop: Repeats a set of components based on a condition or list.
Example: A company wants to run a daily job that checks if new data is available. If yes, it triggers a transformation job; if not, it ends the process.
Data Staging Components
Used to load data from external sources into the staging area of your data warehouse.
– Load Components: S3 Load, Azure Blob Load, GCS Load.
– Database Query: Extract data from external databases (e.g., MySQL, PostgreSQL).
– API Connectors: Salesforce Query, Google Analytics Query, Facebook Ads Query.
Example: A marketing team pulls campaign performance data from Facebook Ads and stores it in Snowflake for analysis.
Transformation Components
Used to clean, enrich, and reshape data.
– Calculator: Create new columns using expressions.
– Filter: Select rows based on conditions.
– Join: Combine data from multiple sources.
– Aggregate: Summarize data.
– Sort: Order data.
– Remove Duplicates: Eliminate repeated records.
– Split Field: Break a column into multiple columns.
– Type Converter: Change data types.
– Rank: Assign ranks based on column values.
Example: A logistics company joins shipment data with customer orders, filters for delayed shipments, and aggregates average delivery times per region.
Data Output Components
Used to write transformed data back to the data warehouse or other destinations.
– Table Output: Load data into a target table.
– Bulk Load: Efficiently load large datasets.
– Unload: Export data from the warehouse to external storage.
Example: A financial institution transforms transaction data and loads it into a reporting table for monthly audits.
Validation & Testing Components
Ensure data quality and job reliability.
– Data Quality: Check for nulls, duplicates, or outliers.
– Assert: Validate conditions.
– Sample: Preview a subset of data for testing.
Example: A healthcare provider validates patient data to ensure no missing values before loading into analytics systems.
Matillion ETL Components Diagram
The diagram below illustrates the flow of Matillion ETL components from orchestration to data output: