🏭🗒️Microsoft Fabric: Data Pipelines [Notes]

Disclaimer: This is work in progress intended to consolidate information from various sources for learning purposes. For the latest information please consult the documentation (see the links below)! 

Last updated: 9-Feb-2024

[Microsoft Fabric] Data pipeline

• {def} a logical sequence of activities that orchestrate a process and perform together a task [1]
• usually by extracting data from one or more sources and loading it into a destination; 
• ⇐ often transforming it along the way [1]
• ⇐ allows to manage the activities as a set instead of each one individually [2]
• ⇐ used to automate ETL processes that ingest transactional data from operational data stores into an analytical data store (e.g. lakehouse or data warehouse) [1]
• {concept} activity
• {def} an executable task in a pipeline
• a flow of activities can be defined by connecting them in a sequence [1]
• its outcome (success, failure, or completion) can be used to direct the flow to the next activity in the sequence [1]
• {type} data movement activities
• copies data from a source data store to a sink data store [2]
• {type} data transformation activities
• encapsulate data transfer operations
• incl. simple Copy Data activities that extract data from a source and load it to a destination
• incl. complex Data Flow activities that encapsulate dataflows (Gen2) that apply transformations to the data as it is transferred
• incl. notebook activities to run a Spark notebook
• incl. stored procedure activities to run SQL code
• incl. delete data activities to delete existing data
• {type} control flow activities
• used to 
• implement loops
• implement conditional branching
• manage variables
• manage parameter values
• enable to implement complex pipeline logic to orchestrate data ingestion and transformation flow  [1]
• can be parameterized
• ⇐enabling to provide specific values to be used each time a pipeline is run  [1]
• when executed, a run is initiated (aka data pipeline run
• runs can be initiated on-demand or scheduled to start at a specific frequency
• use the unique run ID to review run details to confirm they completed successfully and investigate the specific settings used for each execution [1]
• {benefit} increases pipelines’ reusability
• {concept} pipeline template
• predefined pipeline that can be used and customize as required
• {concept} data pipeline run
• occurs when a data pipeline is executed
• the activities in the data pipeline are executed to completion [3] 
• can be triggered one of two ways
• on-demand
• on a schedule
• the scheduled pipeline will be able to run based on the time and frequency set [3]

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References:
[1] Microsoft Learn (2023) Use Data Factory pipelines in Microsoft Fabric [link]
[2] Microsoft Learn (2024) Microsoft Fabric: Activity overview [link]
[3] Microsoft Learn (2024) Microsoft Fabric Concept: Data pipeline Runs [link]

Resources
[R1] Metadata Driven Pipelines for Microsoft Fabric (link)
[R2]