12- Schedule Trigger in Azure Data Factory.pptx
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Schedule triggers in Azure Data Factory allow pipelines to be executed according to a timed schedule. A schedule trigger specifies a start date, recurrence, and end date that determines how often the associated pipeline will run. Triggers can be configured to very specific schedules like every month or every Sunday at 9:00PM. This offers more flexibility than simple recurring schedules like every hour or day. Multiple triggers can be used to kick off a single pipeline, and a single trigger can trigger multiple pipelines.
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The Lookup activity in Azure Data Factory can retrieve datasets from supported data sources and pipelines in Synapse. It reads the content of configuration files and tables, and returns the results of queries and stored procedures. The output can be a single value or array that is then consumed by copy, transform, or control flow activities like ForEach. The Lookup activity is limited to returning the first 5000 rows, with a maximum output size of 4 MB, and it times out after 24 hours.
40 Stored Procedure Activity in Azure Data Factory.pptx
The Stored Procedure activity in Azure Data Factory allows you to execute stored procedures in SQL Server or Azure SQL Database. To use it, you first need to create a linked service connecting to your database. Then you create a dataset pointing to the specific stored procedure you want to run. The Stored Procedure activity is a built-in activity that lets Azure Data Factory run stored procedures on your databases.
38- Get Metadata Activity in Azure Data Factory.pptx
The document discusses the Get Metadata activity in Azure Data Factory. The Get Metadata activity can be used to retrieve metadata of any data in Azure Data Factory or a Synapse pipeline. The metadata output from Get Metadata can then be used for validation in conditional expressions or consumed by subsequent activities. The Get Metadata activity allows users to access metadata of data in Azure Data Factory and Synapse pipelines.
37- User Properties in Activity in Azure Data Factory.pptx
Azure Data Factory allows users to add properties to activities that can be monitored during activity runs. The activity runs monitoring view displays all user-added properties. Users can create up to 5 custom properties under user properties to monitor with activities.
36- Copy Activity Setting in Azure Data Factory.pptx
The document discusses the settings available when configuring a Copy Activity in Azure Data Factory, including options to set the maximum data integration unit, degree of copy parallelism, enable fault tolerance, logging and staging. It allows optimizing the performance of copy operations by controlling resources and error handling. The Copy Activity brings data from source to sink and these settings help make the copy process faster and more reliable.
35- Copy Activity in Azure Data Factory.pptx
The Copy Activity in Azure Data Factory is used to copy data from a source to a destination. To create a Copy Activity, you specify the source and destination data stores in the activity settings, as well as any data transformation settings. You then validate, publish, and monitor the pipeline to copy data between the source and destination.
34- Fail Activity in Azure Data Factory.pptx
The Fail activity in Azure Data Factory is used to force a pipeline to fail and stop processing if certain conditions are met. It can stop execution if an error occurs or conditions are not satisfied, preventing further downstream processing. The Fail activity triggers pipeline failure based on data validation failures, errors in data transformation, issues with connectivity or availability, failure to meet business rules, or when the activity itself is reached. When triggered, the pipeline execution immediately terminates and is marked as failed, without running subsequent activities. It ensures issues or errors are quickly detected and addressed to prevent downstream impacts on data and applications.
33- If Condition Activity in Azure Data Factory.pptx
The If Condition activity in Azure Data Factory allows conditional execution of activities based on expression evaluations, similar to if statements in programming languages. It will execute the activities in the "If True" section if the expression is true, and activities in the "If False" section if the expression is false. To use it, drag the If Condition activity onto the pipeline canvas, define an expression to evaluate, and select the activities to execute for the true and false conditions. This provides a way to conditionally control data flow based on expression results in Azure Data Factory pipelines.
32- Validation Activity in Azure Data Factory.pptx
The Validation activity in Azure Data Factory is used to verify that source data meets specified criteria before it is processed further. This helps ensure data quality and prevents downstream errors. The Validation activity can check that data conforms to a schema, contains required fields with valid data, and meets business rules or thresholds. To use it, a validation rule is defined using a JSON schema or expression specifying the criteria the data must meet to pass validation. Overall, the Validation activity is a useful tool for data quality and accuracy in Azure Data Factory pipelines.
31- Execute Pipeline Activity in Azure Data Factory.pptx
The document discusses the Execute Pipeline activity in Azure Data Factory. The Execute Pipeline activity allows a pipeline to invoke and execute another pipeline, enabling complex workflows composed of multiple chained pipelines. It requires specifying the pipeline name and any input parameters, and handles execution and error conditions. The activity executes the specified pipeline and waits for its completion before continuing, allowing modularization and reuse of pipeline components. An example demonstrates a master pipeline containing an Execute Pipeline activity that calls a separate invoked pipeline.
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53-Dataset Source and Sink Data flow in Azure Data Factory.pptx
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52- Source and Sink Data flow in Azure Data Factory.pptx
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