45. Hands-On: Synapse Project : Event Based Triggers in Azure Synapse || Event Trigger Properties

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We Will Learn:
How to trigger pipeline when new file uploaded into azure storage account (blob created)
How to trigger pipeline on file deleted from azure storage account (blob deleted)
How to avoid running pipeline when file size is 0 bytes in azure storage account
How to copy data from blob Storage to ADLSGen2
How to use event trigger captured properties







Azure synapse unifies data integration big data and enterprise data warehousing into a single service for end-to-end analytics.
Azure Synapse workspace is a collaboration place for doing cloud based enterprise analytics in azure
A workspace will be associated with ADLS Gen2 and File System(container) for data exploration
A workspace allows you to perform analytics with SQL server less called built-in and dedicated sql pool finally Apache Spark for in-memory data analytics.
Resources available for SQL and Spark analytics are organized into SQL and Spark pools.


You can use T-SQL (Transact-SQL) within Azure Synapse to explore and analyze data effectively. Synapse provides two main types of SQL pools for this:
Serverless SQL Pool (built-in):
Every Synapse workspace includes a serverless SQL pool, referred to as the built-in pool.
The serverless SQL pool enables you to quickly explore data stored in Azure Data Lake Storage without the need for dedicated infrastructure.
Pricing: Serverless SQL pools operate on a pay-per-query pricing model, meaning you are charged based on the amount of data processed by each query.
Dedicated SQL Pool
You can also create a dedicated SQL pool within your Synapse workspace for high-performance analytics.
The dedicated pool uses traditional columnar storage to store data in tables, and it is suited for running complex analytical queries at scale.
Pricing: With dedicated SQL pools, you pay for the capacity you reserve and the storage you use


















In Azure Data Factory (ADF), triggers are used to execute pipelines automatically based on specific conditions or schedules
Data Factory supports three types of triggers:
Event-based trigger: Triggers pipelines in response to events, such as file creation or deletion in storage services.
Schedule trigger: Executes pipelines at a predefined time and frequency.
Tumbling window trigger: A tumbling window trigger executes pipelines in recurring time intervals (windows), ensuring that each window is processed exactly once, regardless of success or failure. It is useful for processing data in consistent time slices, especially for historical or backdated data or incremental data.








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