Azure Data Factory Concepts Azure Data Factory Concepts Pipelines Activities Linked
Services Datasets Triggers Integration Runtime
Azure Data Factory Design Patterns What are Design Patterns? Reusable solutions for common problems:
Description or template Formalized best practices Not finished designs that can be transformed directly into source or machine code Why use Design Patterns?
Use tested, proven and documented solutions to: Speed up development
Prevent issues than can cause problems later Improve code readability Design Patterns 1. 2. 3.
4. Truncate and Load Merge Load Incremental Load Bulk Table Transfer Full Extract: Truncate and Load
Specific use cases: All data needed, but replication is not available Small data sets that change often
No historical requirements Very simple, but can be considered an antipattern Full Extract: Truncate and Load Source
Sink Source Table Sink Table Full Extract: Merge Load
Specific use cases: All data needed, but replication is not available Medium data sets that have few changes
Need to minimize churn on the staging tables Adds complexity, doesnt solve the incremental extract from source Full Extract: Merge Load Source Source
Table Sink Table Type Stored Procedure Sink Table
Incremental Load Specific use cases: All data needed, including a robust history
Large data sets that have many changes Need to minimize churn on the staging tables and load on source systems Often requires changes to the source system (triggers, added columns, or engine features) Incremental Load
Source Sink Source Table Change Table
Change Tracking Current Version Table Type Stored
Procedure Control Table (High Watermark) Sink Table
tables Delta Detection: High BE W ARY thes Watermark e appro of
ache s! Based on ascending integer or datetime Store the highest value in a control table or calculate by SELECT MAX() FROM Table Based on ascending date
Update or Create Assumes data is not updated and that the dates are maintained automatically Delta Detection: Change Tracking Lightweight solution for tracking data changes:
Has a row changed? Which rows have been changed? What kind of change was it? Which columns were changed?
Only tracks the latest change to a row Bulk Table Transfer Specific use cases:
Hundreds to thousands of tables to copy Similar loading patterns for all tables Need to minimize amount of code in solution Adds complexity, requires database tables to manage state
Bulk Table Transfer Source Source Table Sink Table Type
Stored Procedure Control Table List Sink Table Log Table
Auditing: Batches Every ETL Process should start by creating a Batch Batches are logical concepts used to tie multipipeline load processes together for Auditing and Logging A batch is closed when a nightly process is completed (Fail or Success)
Auditing: Common Columns CreatedDate - Date row was inserted CreatedBatchId - Batch that inserted row ModifiedDate - Date row was updated ModfiedBatchId - Batch that updated row IsDeleted - Indicates if record has been removed
Solution Overview Jason Horner Design Patterns: Key Take Aways Model your Metadata correctly Make composable single purpose Pipelines Leverage Parameters and User Properties Lookup, Foreach, and Metadata, activities are powerful
Edit the JSON files directly when you hit a wall Preview of? Azure Data Factory Data Flows
Azure Data Factory Data Flows ETL / ELT Visual Authoring Drag and Drop Azure Databricks No Code
Transform At Scale Join, Split, Aggregate, Lookup, Filter, Sort, Derived Column Azure Data Factory Data Flows ETL / ELT
Demo: Azure Data Factory Data Flows Cathrine Wilhelmsen Thank you! Jason Horner, Attunix [email protected]
@jasonhorner Cathrine Wilhelmsen, Inmeta [email protected] @cathrinew Please evaluate this
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