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CDC overview diagram.png","associationType":"TEASER","width":932,"height":481,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNjUwMjQ2LTQxMjUyOWk4QTk3RUMyMENFMERCMzNE?revision=13\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNjUwMjQ2LTQxMjUyOWk4QTk3RUMyMENFMERCMzNE?revision=13","title":"uchrist_2-1665567400870.png","associationType":"BODY","width":1647,"height":539,"altText":null},"BlogTopicMessage:message:3650246":{"__typename":"BlogTopicMessage","subject":"General availability of SAP CDC capabilities for Azure Data Factory and Azure Synapse Analytics","conversation":{"__ref":"Conversation:conversation:3650246"},"id":"message:3650246","revisionNum":13,"uid":3650246,"depth":0,"board":{"__ref":"Blog:board:AzureDataFactoryBlog"},"author":{"__ref":"User:user:1393467"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n \n Customers use SAP systems for their business-critical operations. Today, customers want to be able to combine their SAP data with non-SAP data for their analytics needs. Azure Data Factory (ADF) is an industry-leading data integration service which enables customers to ingest data from diverse data sources (e.g., multi-cloud, SaaS, on-premises), transform data at scale, and more. \n \n Azure Data Factory (ADF) works seamlessly to combine data and prepare it at cloud-scale. Customers are using ADF to ingest data from different SAP data sources (e.g., SAP ECC, SAP Hana, SAP Table, SAP BW Open Hub, SAP BW via MDX, SAP Cloud for Customers), and combining them with data from other operational stores (e.g., Cosmos DB, Azure SQL family, and more). This enables customers to gain deep insights from both SAP and non-SAP data. \n \n Today, we are excited to announce the General Availability of SAP CDC support in Azure Data Factory and Azure Synapse Analytics. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":20338},"postTime":"2022-10-12T12:30:00.026-07:00","lastPublishTime":"2022-10-12T12:30:00.026-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Customers use SAP systems for their business-critical operations. Today, customers want to be able to combine their SAP data with non-SAP data for their analytics needs. Azure Data Factory (ADF) is an industry-leading data integration service which enables customers to ingest data from diverse data sources (e.g., multi-cloud, SaaS, on-premises), transform data at scale, and more. \n \n Azure Data Factory (ADF) works seamlessly to combine data and prepare it at cloud-scale. Customers are using ADF to ingest data from different SAP data sources (e.g., SAP ECC, SAP Hana, SAP Table, SAP BW Open Hub, SAP BW via MDX, SAP Cloud for Customers), and combining them with data from other operational stores (e.g., Cosmos DB, Azure SQL family, and more). This enables customers to gain deep insights from both SAP and non-SAP data. \n \n Today, we are excited to announce the General Availability of SAP CDC support in Azure Data Factory and Azure Synapse Analytics. \n \n Using Azure Data Factory and Azure Synapse Analytics mapping data flows, customers will now be able to extract changes from different SAP systems using the new SAP CDC connector, transform the data at scale, and deliver the data into a data lake or data warehouse for analytics. The ability to work with change data significantly reduces resource consumption and provides a tremendous efficiency boost in high data volume scenarios. \n \n \n \n What does the new connectivity offer? \n \n \n \n \n Wide variety of sources: With the SAP CDC connector, customers get access to the most widely adopted SAP sources, including \n \n SAP ECC extractors \n SAP S/4HANA CDS views \n SAP BW and SAP BW/4HANA InfoProviders, InfoObjects and Queries \n SAP tables (in combination with SAP Landscape Transformation Replication Server SLT) \n \n For each of these options, SAP delivers thousands of objects out-of-the box, providing data integration interfaces across all SAP components. Of course, custom-built objects are accessible with the new connector as well. \n \n Scalable, CDC enabled data integration: Via SAP’s Operational Data Provisioning framework (ODP), these sources provide a robust and scalable interface for data extraction, including full and delta (change data capture) capabilities with low latency. \n \n Multi-cloud enablement: The architecture can be applied to virtually any SAP system wherever it is deployed: using a self-hosted integration runtime as a proxy between Microsoft Azure and the SAP system, you can connect to SAP whether it is running on-premises, in Microsoft Azure cloud, or on any other hyperscaler’s infrastructure. \n \n How does it work? \n Starting point are mapping data flows in Azure Data Factory or Azure Synapse Analytics which provide an easy-to-use, integrated end-to-end experience to extract the data from SAP, transform it and apply the changes to any supported sink. \n \n Below, you see a simple mapping data flow extracting raw change data from SAP, applying a simple transformation (consider the derived column as a placeholder for your actual technical or business transformations), and updating the resulting data to a sink (in this case a Delta table in Azure Data Lake Gen2). \n \n \n \n The beauty of the solution is that the source transformation does all the hard work for you: it reads the raw change data from SAP and interprets the associated metadata to handle inserts, updates and deletes correctly. That way, developers can concentrate on the required technical or business transformations without having to worry about the complex internal logic of the SAP change feeds. \n \n Unleashing the power of the Microsoft Azure Data portfolio on your SAP data has never been easier. With the new SAP CDC connector, Azure Data Factory and Azure Synapse Analytics now provide you with an easy-to-use tool to extract SAP data in a robust, scalable and high performant way. \n \n Click here to get started! 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l},"BlogTopicMessage:message:3925362":{"__typename":"BlogTopicMessage","subject":"Metadata Driven Pipelines for Dynamic Full and Incremental Processing in Azure SQL","conversation":{"__ref":"Conversation:conversation:3925362"},"id":"message:3925362","revisionNum":19,"uid":3925362,"depth":0,"board":{"__ref":"Blog:board:AzureDataFactoryBlog"},"author":{"__ref":"User:user:1927178"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n \n With Azure SQL or Synapse, you can facilitate a dynamic ETL for incremental processing (dynamic SQL delete and insert) and full table processing with 3 metadata pipelines. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":31116},"postTime":"2023-09-28T07:35:51.355-07:00","lastPublishTime":"2023-09-28T07:35:51.355-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Developing ETLs/ELTs can be a complex process when you add in business logic, large amounts of data, and the high volume of table data that needs to be moved from source to target. This is especially true in analytical workloads involving Azure SQL when there is a need to either fully reload a table or incrementally update a table. In order to handle the logic to incrementally update a table or fully reload a table in Azure SQL (or Azure Synapse), we will need to create the following assets: \n \n Metadata table in Azure SQL \n \n This will contain the configurations needed to load each table end to end \n \n \n Metadata driven pipelines\n \n Parent and child pipeline templates that will orchestrate and execute the ETL/ELT end to end \n \n \n Custom SQL logic for incremental processing\n \n Dynamic SQL to perform the delete and insert based on criteria the user provides in the metadata table \n \n \n \n *This article uses Azure SQL DB as the source and sink databases. However, Azure SQL MI, On-Prem SQL, and Synapse Dedicated Pools (along with Synapse Pipelines) will also be compatible for this solution. As a source, you can use databases like MySQL, Oracle, and others. You will just need to adjust the query syntax/connections to match the desired source. \n \n Scenario \n There is a need to load SQL tables from a SQL Server source on a daily frequency or multiple times a day. The requirements are to land the data first in ADLS Gen 2, and then finally load the tables into Azure SQL DB with the correct processing (incremental or full) while using a dynamic pipeline strategy to limit the number of objects used. \n Metadata Table \n The first set up that is required in our dynamic ETL is going to be a metadata (sometimes called \"config\" table) table on the destination SQL server environment. This table contains all of the information that is needed to pass into the ADF pipelines to determine the source query, ADLS Gen 2 storage location and metadata, processing metadata, staging metadata, and other metadata critical to performing the ETL. An example of a metadata table design and sample are below. \n \n Metadata table definition \n \n \n \n CREATE TABLE [meta].[ADLS_Metadata](\n\t[FileName] [varchar](100) NULL,\n\t[StorageAccount] [varchar](100) NULL,\n\t[StorageContainer] [varchar](100) NULL,\n\t[ContainerDirectoryPath] [varchar](100) NULL,\n\t[LoadType] [varchar](25) NULL,\n\t[LoadIndicator] [varchar](25) NULL,\n\t[SourceSchema] [varchar](25) NULL,\n\t[SourceTable] [varchar](100) NULL,\n\t[StagingSchema] [varchar](25) NULL,\n\t[StagingTable] [varchar](100) NULL,\n\t[TargetSchema] [varchar](25) NULL,\n\t[TargetTable] [varchar](100) NULL,\n\t[ColumnKey] [varchar](500) NULL,\n\t[WaterfallColumn] [varchar](100) NULL,\n\t[TableColumns] [varchar](1000) NULL\n) ON [PRIMARY]\nGO \n \n \n \n \n Sample output of metadata table \n \n \n The ETL will be facilitated entirely from this metadata table. Any tables that are not included within this table, would not be executed in our ETL pipelines. Any new tables or work that are needed to be added, simply insert them into the metadata table and they will be available when the pipeline is triggered, without needing to alter the ADF pipelines. Whether the data is going to the same storage container or a different one, different databases, etc. the metadata table allows you dictate where, what, and how of your ETL from one central location. This is a simple metadata table example, but you can make this as robust as you desire by adding in test flags, different load frequency indicators, and many others. \n \n Metadata Driven Pipelines \n Now that the metadata table is constructed, time to build the dynamic ADF pipelines to orchestrate and execute the ETL. \n \n Here are the ADF objects needed to execute the ETL for 'N' number of tables. These will be shown in steps below. It is important the note the power of the dynamic metadata driven pipelines, they are able to execute/facilitate an enterprise level ETL with only 3 pipelines, 2-3 linked services, and 2-3 datasets in this scenario. \n \n \n Linked Services:\n \n Source SQL Server*\n \n Authenticated with system-assigned managed identity. \n \n \n Sink SQL Server*\n \n Authenticated with system-assigned managed identity. \n \n \n ADLS Gen 2 Storage\n \n Authenticated with system-assigned managed identity. \n \n \n *IF the source SQL Server and sink are the same service with the same authentication and integration runtime then you only need one linked service. Ex. both are Azure SQL DBs with the same authentication. However, if the authentication differs or they are different services (Azure SQL DB vs Azure SQL MI) then create one dynamic linked service for each. \n \n \n Datasets:\n \n Source SQL Server Dataset* \n Sink SQL Server Dataset* \n ADLS Gen 2 Storage Dataset \n *One dataset per linked service. Separate datasets may not be needed if you have one dynamic linked service. \n \n \n Pipelines:\n \n Main Orchestration Pipeline \n Full Load Processing Pipeline \n Incremental Processing Pipeline \n \n \n \n \n Linked Service Creation \n In this scenario, the source and sink SQL environments are both Azure SQL DB with the same authentication, so there will be only one linked service created with parameters to handle the dynamic use. Feel free to use your own naming conventions for the objects and parameters, just be sure they are generic and descriptive. Ex. not using \"parameter1\" or \"linkedService1\". \n \n The generic name of the linked service will be \"AzureSQLDB\". The domain name and database name are referenced from the parameters that we created in the linked service to pass this connection information at runtime from the pipelines. Default values are available and will be used if there are not values passed through the pipeline. \n \n \n \n \n The same pattern will be used for the ADLS Gen 2 linked service. In this linked service, there is a generic name used \"ADLSGen2\" and only the storage account parameter is used. There is no path specification used here to allow the use of the linked service for all containers and paths using the same authentication method and Integration Runtime. The path and file will be optional parameters of the dataset that references this linked service. \n \n \n Dataset Creation \n The dataset will created and using the linked services that were created above. There needs to be the parameters that are used in the linked service as well as additional parameters. The parameter names will align with the metadata table column names to provide ease of use. \n \n For the SQL environment, there needs to be the parameters \"serverName\" and \"databaseName\" which come from the linked service. Then adding the parameters \"schemaName\" and \"tableName\" to have the ability to query/use all tables in a server or database using that linked service. Create the parameters first on the \"Parameters\" tab and then use the 'add dynamic content' to place the reference to the parameters that were just created. These parameters will be exposed/prompted when referenced in the pipelines that are created later in this article. \n \n \n \n \n For ADLS Gen 2 storage, there will be a dataset for each type of file and compression. In this scenario, the data will be stored as Parquet files with snappy compression. The same concept as above is used for the parameters. For the linked service, the parameter is created on the dataset \"storageAccountName\". Then dataset specific parameters to identify all possible containers, paths and files within the dataset are \"storageContainer\", \"containerDirectoryPath\", and \"fileName\". \n \n \n \n Pipeline and ETL Creation \n With the metadata table, linked services, and datasets created, it is time to build out the metadata driven pipelines. The walkthrough below is split up into the 3 different pipelines, the main orchestration (parent) pipeline, full processing pipeline (child), and incremental processing pipeline (child). These pipelines are organized into folders for ease of access/formatting. The folders are virtual and offer no functionality other than organization in the UI. The folders are \"Orchestration\" which houses the main parent pipeline and \"ETL\" which contains the children pipelines that perform the processing. \n \n \n \n \n Main Orchestration pipeline: \n The main orchestration pipeline in this example is called \"adventureWorks_Main\". This pipeline will have a trigger associated with it and will control the execution of the whole ETL. This is the design of the pipeline in the UI, and each activity will be described. \n \n \n On the parent pipeline, it is critical to have pipeline parameters to allow this process to be dynamic. These pipeline parameters will be used throughout the activities, and passed to the children pipelines. They will look familiar as they will be used in the dynamic datasets, linked services, and querying the metadata table. \n \n sourceServerName\n \n source server connection to passed through the parameterized dataset to the parameterized linked service \n \n \n sourceDatabaseName\n \n source database connection to passed through the parameterized dataset to the parameterized linked service \n \n \n targetServerName\n \n Target server connection to passed through the parameterized dataset to the parameterized linked service. Typically where your metadata table lives as well. \n \n \n targetDatabaseName\n \n Target database connection to passed through the parameterized dataset to the parameterized linked service. Typically where your metadata table lives as well. \n \n \n loadIndicator\n \n This is a frequency/use indicator. 'Daily' is an example that signifies the table is loaded daily. This is used as a filter. So, you can place'Test' or some other value to control which tables and different frequencies or uses to execute.\n \n Ex. 'Testing Only', 'Monthly', 'Hourly'. The frequency would correspond with a trigger frequency as well. \n \n \n \n \n waterfallLookbackDays\n \n The amount of days to incrementally process. Used only to find the changed rows in incremental data sources. Requires a reliable date stamp that corresponds to tracked inserts and updates.\n \n Ex. rows that have been updated within the last 120 days (-120) \n \n \n There are many ways to incrementally process, and this is just one used in the example. This article discusses using waterfall column/columns. You would just need to adjust the parameters, syntax, and dynamic script to fit your criteria. \n \n \n \n \n \n \n The full reload path and the incremental reload path have the same activities and pattern, however they differ in 2 ways. \n \n Look up query. Specifically, the WHERE clause \n The parameters passed to the 'Execute Pipeline' activity within the 'ForEach Loop' \n \n \n Full Reload Pattern/Queries: \n Use a look up to extract the rows based on the SQL query criteria -> pass that result set to the for each loop and iterate over each table to perform the loading in the full load processing pipeline. \n \n \n \n Lookup Activity - Full Reload: \n Inside the look up activity - \"Full Reload - Lookup Metadata\". \n \n The parameters defined on the dataset appear as properties within the activity using the dataset. The lookup activity will query the metadata table, which lives in the target server. This uses the pipeline parameters. The \"schemaName\" and \"tableName\" parameters are not needed since the lookup activity is performing a query, so placing a \"x\" value allows the pipeline to validate even though these are not used. \n \n Dynamic content for \"serverName\" parameter \n \n \n \n @pipeline().parameters.targetServerName \n \n \n \n \n Dynamic content for the query. The pipeline parameter for \"loadIndicator\" is used in the SQL query as well as a hard coded filter for \"Full\" load types. \n \n \n \n \n SELECT LoadType, TargetTable \nFROM \n\tmeta.ADLS_Metadata\nWHERE \n\tLoadIndicator = '@{pipeline().parameters.loadIndicator}'\n\tAND LoadType = 'Full' \n \n \n \n \n Sample query output: \n \n \n For Each Loop - Full Reload: \n The next step is to iterate through the output in the for each loop. Use the settings tab to define the items from the full reload lookup. \n \n \n \n \n \n @activity('Full Reload - Lookup Metadata').output.value \n \n \n \n Inside the for each loop, an execute pipeline activity will be used to call the full load processing pipeline (child). There are parameters on the child pipeline that are required to enter when executing. The parameters are going to be the pipeline parameters (from the parent pipeline) EXCEPT for a new parameter called 'targetTable'. The 'targetTable' comes from the item value that we are iterating from the output of look up activity. \n \n \n Full Reload - Processing Pipeline (child) \n The processing pipeline called \"adventureWorks_FullLoad\" is executed from the parent pipeline with the pipeline parameters being passed from parent to child. Because this pipeline is called within a for each loop, each table that is being iterated will be have their own execution from this pipeline. The overall purpose and design of this pipeline is: \n \n Extract source data \n Load source data to ADLS Gen 2 storage \n Full reload of data from ADLS Gen 2 to Azure SQL DB \n \n \n \n Lookup Activity: \n This will be the same dataset and configuration for the use of pipeline parameters as the parent pipeline lookup activity with the only difference being the query that is being passed through. \n \n \n Query used in dynamic content. This will return all the columns associated with the row, there should only be one row returned, if there are multiple due to the addition of different testing scenarios/frequencies, then refine the filtering logic for the appropriate context. \n \n \n \n SELECT * \nFROM \n\tmeta.ADLS_Metadata\nWHERE \n\tTargetTable = '@{pipeline().parameters.targetTable}'\n\tAND LoadIndicator = '@{pipeline().parameters.loadIndicator}' \n \n \n \n \n Copy data to ADLS Gen 2: \n The metadata gathered from the look up activity is used to extract the source table data and load to the location in ADLS Gen 2 specified by the metadata. \n \n In the 'Source' - The pipeline parameters for the source server and source database are used for \"serverName\" and \"databaseName\". Then the output from the lookup provides the \"schemaName\" and \"tableName\". This time, a table is used instead of a query. \n \n \n Example of activity output use in a parameter. \n \n \n \n \n @activity('Full Load - Lookup Metadata').output.firstRow.SourceSchema \n \n \n \n \n In the 'Sink' - all the parameters are populated from the lookup activity reading from the metadata table. This creates a path for each file. Each file will be overwritten with every execution. If you wish to retain historical copies of the loads, you can add an archive step to move the files from this location and/or add customer logic for date suffixes in the expression builder. \n \n \n Example of storage container with loaded files \n \n \n Copy Data to Azure SQL DB: \n Once the data has been landed into ADLS Gen 2 as parquet files, it is time to load the files into the Azure SQL DB using another Copy activity. \n \n In the 'Source' - this will be the same configuration as the 'Sink' of the previous copy activity. Using the output from the lookup activity which contains the metadata table result. \n \n \n In the 'Sink' - The dataset parameters will be populated with the pipeline parameters for the \"targetServerName\" and \"targetDatabaseName\". The \"schemaName\" and \"tableName\" are populated from the lookup activity output. \n \n There is a 'Pre-copy script' that is being executed to truncate the table if it exists already. If the table does not exist, the script will not try to truncate the table, and the 'Table option' of 'Auto create table' being selected will handle any new tables. The write behavior will be 'Insert' since there is a full reload. \n \n If schema drift is present, one solution is to replace the truncate with a drop instead to recreate the table each execution. That has other risks associated with it that need to be considered. \n \n \n Pre-copy script using pipeline parameters and lookup activity output parameters \n \n \n \n IF EXISTS (SELECT * FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = '@{activity('Full Load - Lookup Metadata').output.firstRow.TargetSchema}' AND TABLE_NAME = '@{activity('Full Load - Lookup Metadata').output.firstRow.TargetTable}' )\nBEGIN \nTRUNCATE TABLE [@{activity('Full Load - Lookup Metadata').output.firstRow.TargetSchema}].[@{activity('Full Load - Lookup Metadata').output.firstRow.TargetTable}] \nEND\n \n \n \n \n Incremental Load Pattern/Queries: \n The incremental processing load is going to be very similar the full reload processing method, with differences being in the filtering of lookup activities, additional parameters, and different methods to perform the loading inside the processing pipeline. \n \n \n Lookup Activity - Incremental Load \n This lookup activity will be the exact same dataset configuration as the full reload version, with the minor change of 'Incremental' being hard coded as the WHERE clause 'LoadType' filter instead of 'Full'. The parameters are still using the pipeline parameters to connect to the metadata table. \n \n \n \n \n \n SELECT LoadType, TargetTable \nFROM \n\tmeta.ADLS_Metadata\nWHERE \n\tLoadIndicator = '@{pipeline().parameters.loadIndicator}'\n\tAND LoadType = 'Incremental' \n \n \n \n For Each Loop - Incremental Load \n The for each loop will use the output from the incremental reload lookup activity output in the 'Items'. \n \n \n \n \n \n @activity('Incremental Reload - Lookup Metadata').output.value \n \n \n \n Inside the for each loop, there is an execute pipeline activity which calls the incremental reload processing pipeline (child). There is one extra parameter that was not used in the full reload processing pipeline execution - 'waterfallLookbackDays' (pipeline parameter). \n \n \n Incremental Reload - Processing Pipeline (child) \n The processing pipeline called \"adventureWorks_IncrementalLoad\" is executed from the parent pipeline with the pipeline parameters being passed from parent to child. Because this pipeline is called within a for each loop, each table that is being iterated will be have their own execution from this pipeline. The overall purpose and design of this pipeline is (differs slightly from the full reload): \n \n Extract source data \n Load source data to ADLS Gen 2 storage \n Full reload of data from ADLS Gen 2 to a staging table in Azure SQL DB \n Dynamic delete and insert from staging table into production table within Azure SQL DB \n \n \n \n Lookup Activity: \n This will be the same as the full reload version of the query and the dataset configuration. \n \n \n \n \n SELECT * \nFROM \n\tmeta.ADLS_Metadata\nWHERE \n\tTargetTable = '@{pipeline().parameters.targetTable}'\n\tAND LoadIndicator = '@{pipeline().parameters.loadIndicator}' \n \n \n \n Copy Data to ADLS Gen 2: \n This activity is where the main difference between the full reload and the incremental loads start. \n \n In the 'Source' - instead of the table, there will a query used. This query will utilize the 'waterfallColumn' value from the metadata table lookup and the 'waterfallLookbackDays' pipeline parameter to filter the results to only that time period. This will allow the query to build for each table dynamically and return a subset of the source table regardless of where the source is, the table, etc. \n \n \n Query using dynamic content \n \n \n @concat(\n 'SELECT * \n FROM ', '[', activity('Incremental Load - Lookup Metadata').output.firstRow.SourceSchema, '].[', activity('Incremental Load - Lookup Metadata').output.firstRow.SourceTable, '] ',\n 'WHERE ', 'CONVERT(DATE, ', activity('Incremental Load - Lookup Metadata').output.firstRow.WaterfallColumn, ') >= DATEADD(DAY,', pipeline().parameters.waterfallLookbackDays, ', GETDATE())' ) \n \n \n \n If the source is different than SQL, you can adjust the dynamic query to match the syntax for the source environment like Oracle, MySQL, etc. \n \n In the 'Sink' - the configuration will the same as the full reload. The dataset parameters will come from the lookup activity output on the same dataset. \n \n \n \n \n @activity('Incremental Load - Lookup Metadata').output.firstRow.StorageAccount \n \n \n \n Copy Data from ADLS Gen 2 to Stage Table in Azure SQL DB: \n The next step is to load the data from ADLS Gen 2 into a staging table to prep to perform the incremental processing. The staging table allows for temporary data to be stored and leverage the full compute power of the Azure SQL DB, as well as maintaining more control over the processing. \n \n In both 'Source' and 'Sink' - The configurations that are used will be the same as the full reload version, with the only differences being the parameters are pointing to the staging table referenced in the metadata table instead of the final version of the table. This table is in a different schema and has a 'STAGE_' prefix on the table name. In the 'Sink', the process of truncating the table pre copy, full loading, and/or auto creating tables that don't exist is the same. \n \n \n \n \n \n @activity('Incremental Load - Lookup Metadata').output.firstRow.FileName \n \n \n \n \n \n Pipeline parameter example for 'tableName' pointing to the 'StagingTable' \n \n \n \n @activity('Incremental Load - Lookup Metadata').output.firstRow.StagingTable \n \n \n \n Pre-copy script - referencing the staging tables \n \n \n IF EXISTS (SELECT * FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = '@{activity('Incremental Load - Lookup Metadata').output.firstRow.StagingSchema}' AND TABLE_NAME = '@{activity('Incremental Load - Lookup Metadata').output.firstRow.StagingTable}' )\nBEGIN\nTRUNCATE TABLE [@{activity('Incremental Load - Lookup Metadata').output.firstRow.StagingSchema}].[@{activity('Incremental Load - Lookup Metadata').output.firstRow.StagingTable}]\nEND\n \n \n \n \n Dynamic Delete and Insert Script \n This step is what actually performs the incremental processing of the tables. It will delete the rows that are in the staging table (simulating that data has changed and needs to be either updated or inserted) and then insert the staging table rows into the production table. The script will be provided and explained below. \n \n This is a 'Script' activity with the linked service parameters pointing to the location of the target tables and metadata table. There is also an input script parameter 'FileName' that uses the 'FileName' value from the lookup activity. The option for 'NonQuery' is selected since this script performs a DDL statement and does not return a result. If a result was being returned instead of DDL statements, then 'Query' would be selected. \n \n FileName parameter dynamic content \n \n \n \n @activity('Incremental Load - Lookup Metadata').output.firstRow.FileName \n \n \n \n Dynamic Delete and Insert SQL Script \n \n \n --DECLARE @FileName VARCHAR (500) -- Manual Runs inside procedure will toggle this for troublshooting\nDECLARE @TargetTable VARCHAR (500)\nDECLARE @StagingTable VARCHAR (500)\nDECLARE @WhereClause VARCHAR(MAX) \nDECLARE @StagingSchema VARCHAR (50)\nDECLARE @TargetSchema VARCHAR (50)\nDECLARE @FullStagingTableName VARCHAR (500)\nDECLARE @FullTargetTableName VARCHAR (500)\nDECLARE @TargetTableColumnList NVARCHAR(MAX)\nDECLARE @DeleteStatementSQL NVARCHAR (MAX)\nDECLARE @InsertStatementSQL NVARCHAR (MAX)\nDECLARE @StatisticsUpdateSQL NVARCHAR (MAX)\n\n\n--SET @FileName = 'SalesOrderHeader.parquet' -- Manual Runs inside procedure will toggle this for troublshooting\nSET @TargetTable = (SELECT TargetTable FROM meta.ADLS_Metadata WHERE FileName = @FileName)\nSET @TargetSchema = (SELECT TargetSchema FROM meta.ADLS_Metadata WHERE FileName = @FileName)\nSET @StagingTable = (SELECT StagingTable FROM meta.ADLS_Metadata WHERE FileName = @FileName)\nSET @StagingSchema = (SELECT StagingSchema FROM meta.ADLS_Metadata WHERE FileName = @FileName)\nSET @FullStagingTableName = CONCAT(@StagingSchema, '.', @StagingTable)\nSET @FullTargetTableName = CONCAT(@TargetSchema, '.', @TargetTable)\nSET @TargetTableColumnList = (\tSELECT \n\t\t\t\t\t\t\t\t\tColumnList = STRING_AGG('[' + col.NAME + ']', ',' )\n\t\t\t\t\t\t\t\tFROM\n\t\t\t\t\t\t\t\t\tsys.tables tab\n\t\t\t\t\t\t\t\t\t\tLEFT JOIN \n\t\t\t\t\t\t\t\t\tsys.schemas sch\n\t\t\t\t\t\t\t\t\t\tON tab.schema_id = sch.schema_id\n\t\t\t\t\t\t\t\t\t\tLEFT JOIN \n\t\t\t\t\t\t\t\t\tsys.columns col\n\t\t\t\t\t\t\t\t\t\tON tab.object_id = col.object_id\n\t\t\t\t\t\t\t\tWHERE \n\t\t\t\t\t\t\t\t\tsch.name = @TargetSchema\n\t\t\t\t\t\t\t\t\tAND tab.name = @TargetTable\n\t\t\t\t\t\t\t\t\tAND col.is_identity = 0\n\t\t\t\t\t\t\t)\n ;\n\nWITH PrimaryKeyList AS (\n\t\t\t\t\t\tSELECT \n\t\t\t\t\t\t\tColumnKey = RTRIM(LTRIM(Value)),\n\t\t\t\t\t\t\tRowNumber = ROW_NUMBER () OVER (ORDER BY value ASC)\n\n\t\t\t\t\t\tFROM\n\t\t\t\t\t\t\tmeta.ADLS_Metadata\n\t\t\t\t\t\t\t\tCROSS APPLY \n\t\t\t\t\t\t\tSTRING_SPLIT( ColumnKey, ',')\n\t\t\t\t\t\tWHERE \n\t\t\t\t\t\t\tFileName = @FileName\n\t\t\t\t\t\t)\n \n /******* Section for single primary key OR Keys that do not need to be concated to be uniquely identified *********************/\n SELECT\n @WhereClause = STRING_AGG(CASE \n WHEN E.ColumnKey IS NOT NULL THEN CONCAT( Beg.ColumnKey,' IN (SELECT ', Beg.ColumnKey, ' FROM ', @FullStagingTableName, ') AND')\n ELSE CONCAT( Beg.ColumnKey,' IN (SELECT ', Beg.ColumnKey, ' FROM ', @FullStagingTableName, ')' )\n END, ' ')\n FROM \n PrimaryKeyList Beg\n LEFT JOIN\n PrimaryKeyList E\n ON Beg.Rownumber = E.Rownumber - 1 \n ;\n/***************************************************************************************************************************************/\n\n/************************* Section used to concat a composite key and create the unique identifier during the load process if it does not exist in the source tables *******************\nSELECT\n @WhereClause = CONCAT(\t'CONCAT(', \n\t\t\t\t\t\t\tSTRING_AGG(CASE \n\t\t\t\t\t\t\t\t\t\t\tWHEN E.ColumnKey IS NOT NULL THEN Beg.ColumnKey\n\t\t\t\t\t\t\t\t\t\t\tELSE CONCAT(Beg.ColumnKey, ') ')\n\t\t\t\t\t\t\t\t\t\tEND, ', '\n\t\t\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t'IN (SELECT CONCAT(', \n\t\t\t\t\t\t\tSTRING_AGG(CASE \n\t\t\t\t\t\t\t\t\t\t\tWHEN E.ColumnKey IS NOT NULL THEN Beg.ColumnKey\n\t\t\t\t\t\t\t\t\t\t\tELSE CONCAT(Beg.ColumnKey, ') ')\n\t\t\t\t\t\t\t\t\t\tEND, ', '\n\t\t\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t'FROM ', @FullStagingTableName, ')'\n\t\t\t\t\t\t)\nFROM \n PrimaryKeyList Beg\n LEFT JOIN\n PrimaryKeyList E\n ON Beg.Rownumber = E.Rownumber - 1 \n ;\n \n *********************************************************************************************************************************************************/\n \nSELECT\n @DeleteStatementSQL = CONCAT('DELETE FROM ', @FullTargetTableName, ' WHERE ', @WhereClause) ;\n \nSELECT \n @InsertStatementSQL = CONCAT('INSERT INTO ', @FullTargetTableName, ' (', @TargetTableColumnList, ') ', ' SELECT ', @TargetTableColumnList, ' FROM ', @FullStagingTableName)\n \n--SELECT \n--\t@StatisticsUpdateSQL = CONCAT('UPDATE STATISTICS ', @FullTargetTableName) \n\n--PRINT @DeleteStatementSQL\n--PRINT @InsertStatementSQL\n--PRINT @StatisticsUpdateSQL\n \nEXECUTE sp_executesql @DeleteStatementSQL ; \n\nEXECUTE sp_executesql @InsertStatementSQL ;\n\n--EXECUTE sp_executesql @StatisticsUpdateSQL ; Used in Dedicated SQL Pool to update statistics once tables have been loaded \n \n \n \n See examples of the different steps of the script below for the table 'SalesOrderHeader' \n \n Metadata table results for the staging table, target table, and the primary keys for the target table 'SalesOrderHeader'. You will notice that this table has multiple primary keys to provide the unique record for the data. This script will handle multiple primary keys or single primary keys in a method shown later. \n \n \n First, the variables are built. One important variable is @TargetTableColumnList which compiles a comma separated list of the target table columns from the system tables. You will not need to maintain the columns in the target table since the script will compile a list from the system tables and exclude identity columns since these are not updated or inserted. If that is needed, then logic can be added to turn the identity insert on and off in the script. \n \n \n The next step is to build the WHERE clause of our delete statement. This is done by using the column keys and splitting them out into different predicates. Executing the code down to the @WhereClause creation will produce this output. \n \n \n \n @WhereClause = rowguid IN (SELECT rowguid FROM stage.STAGE_SalesOrderHeader) AND SalesOrderNumber IN (SELECT SalesOrderNumber FROM stage.STAGE_SalesOrderHeader) AND SalesOrderID IN (SELECT SalesOrderID FROM stage.STAGE_SalesOrderHeader) \n \n \n \n There is a section commented out for handling composite keys that will not evaluate with each column key in their own predicate. In this scenario, the values are concatenated. The commented section for composite keys will show the below result for the same table/key combination. \n \n \n \n @WhereClause = CONCAT(rowguid, SalesOrderNumber, SalesOrderID) IN (SELECT CONCAT(rowguid, SalesOrderNumber, SalesOrderID) FROM stage.STAGE_SalesOrderHeader) \n \n \n \n Next, the delete and insert statements are created using the dynamic SQL in the script and previous steps. Here are the outputs from our example. \n \n Delete statement \n \n \n DELETE FROM salesLT.SalesOrderHeader WHERE rowguid IN (SELECT rowguid FROM stage.STAGE_SalesOrderHeader) AND SalesOrderNumber IN (SELECT SalesOrderNumber FROM stage.STAGE_SalesOrderHeader) AND SalesOrderID IN (SELECT SalesOrderID FROM stage.STAGE_SalesOrderHeader) \n \n \n Insert statement \n \n \n INSERT INTO salesLT.SalesOrderHeader ([SalesOrderID],[RevisionNumber],[OrderDate],[DueDate],[ShipDate],[Status],[OnlineOrderFlag],[SalesOrderNumber],[PurchaseOrderNumber],[AccountNumber],[CustomerID],[ShipToAddressID],[BillToAddressID],[ShipMethod],[CreditCardApprovalCode],[SubTotal],[TaxAmt],[Freight],[TotalDue],[Comment],[rowguid],[ModifiedDate]) SELECT [SalesOrderID],[RevisionNumber],[OrderDate],[DueDate],[ShipDate],[Status],[OnlineOrderFlag],[SalesOrderNumber],[PurchaseOrderNumber],[AccountNumber],[CustomerID],[ShipToAddressID],[BillToAddressID],[ShipMethod],[CreditCardApprovalCode],[SubTotal],[TaxAmt],[Freight],[TotalDue],[Comment],[rowguid],[ModifiedDate] FROM stage.STAGE_SalesOrderHeader \n \n \n \n Finally, those statements are passed into sp_executesql to be executed. \n Summary \n The template and scripts will allow you to build dynamic metadata driven ETL process at enterprise scale with as little as 3 pipelines to facilitate 'N' number of tables. This metadata driven approach is highly flexible and scalable, which will allow you to build upon this solution and even cater it to your exact needs. Even if the requirements or change tracking logic is more complex than waterfall columns or composite keys, there is still an ability to add complex logic into this process to handle your ETL needs. 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Kromer","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0xNzU5ODktMTMzMjE4aUE2NEZBNEVBN0RFRjM2Q0Y"},"id":"user:175989"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMzk0MzEzLTE5MjE3MWlFMjY4M0UxNTM1NzQ5QzVF?revision=2\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMzk0MzEzLTE5MjE3MWlFMjY4M0UxNTM1NzQ5QzVF?revision=2","title":"cols1.png","associationType":"TEASER","width":1118,"height":509,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMzk0MzEzLTE5MjE3NGk0MDBBMEFFMzc1MjJFQ0E1?revision=2\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xMzk0MzEzLTE5MjE3NGk0MDBBMEFFMzc1MjJFQ0E1?revision=2","title":"cols2.png","associationType":"BODY","width":708,"height":194,"altText":null},"BlogTopicMessage:message:1394313":{"__typename":"BlogTopicMessage","subject":"New data flow functions for dynamic, reusable patterns","conversation":{"__ref":"Conversation:conversation:1394313"},"id":"message:1394313","revisionNum":2,"uid":1394313,"depth":0,"board":{"__ref":"Blog:board:AzureDataFactoryBlog"},"author":{"__ref":"User:user:175989"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" ADF has added columns() and byNames() functions to make it even easier to build ETL patterns that are reusable and flexible for generic handling of dimensions and other big data analytics requirements. \n \n ","introduction":"","metrics":{"__typename":"MessageMetrics","views":15438},"postTime":"2020-05-15T15:37:27.417-07:00","lastPublishTime":"2020-05-15T15:43:35.235-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" ADF has added columns() and byNames() functions to make it even easier to build ETL patterns that are reusable and flexible for generic handling of dimensions and other big data analytics requirements. \n \n In this example below, I am making a generic change detection data flow that looks for changed column values by hashing the row. I can use static column names in the hashing function (I'm using sha2 in this case) or I can use columns() as a way to tell ADF to use all of the stream's incoming columns as a single function argument. This way, my pattern can be re-used with any source without needing to hardcode column names. This is all supported by the ADF schema drift feature and is achieved by using the new columns() function as the argument to the sha2() function: \n \n \n \n sha2(256,columns()) \n \n In the second example above, I'm passing in a comma-separated list of values as a string that represent the specific columns that I wish to check for change detection. In this case, I don't want to use static columns and I don't want to look for changes from any column using columns() for the entire row. Instead, I want to parameterize the hash function. In this case, I use byNames() and split my string parameter to create an array of string column names that can be sent to the data flow activity at runtime, making the hashing dynamic. \n \n \n sha2(256,byNames(split($cols,','))) \n \n \n \n In this case, I'm going to hash the columns movies, title, and genres, and that will detect any changes to the values in those columns. This is achieved by using the new byNames() function as the argument to the sha2() hash function and using split() to create an array from the string parameter that contains column names. 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In this blog, we will deep dive into permissions settings for Storage Event Trigger, and properly understand how to enforce access control in this architecture. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":10625},"postTime":"2021-01-27T04:35:12.869-08:00","lastPublishTime":"2021-03-11T19:25:08.536-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Storage Event Trigger in Azure Data Factory is the building block to build an event-driven ETL/ELT architecture (EDA). Data Factory's native integration with Azure Event Grid let you trigger processing pipeline based upon certain events. Currently, Storage Event Triggers support events with Azure Data Lake Storage Gen2 and General Purpose version 2 storage accounts, including Blob Created and Blob Deleted. \n \n As with any architecture, it's sometimes critical to enforce Role Based Access Control (RBAC) to ensure that only certain members on the team can access certain sensitive information. Unauthorized access to listen to, subscribe to updates from, and trigger pipelines linked to blob accounts should be strictly prohibited. \n \n Azure Data Factory make it really easy for you and enforce the following rules: \n \n To successfully create a new or update an existing Storage Event Trigger, the Azure account signed into the Data Factory and publish the event trigger needs to have appropriate access to the relevant storage account. Otherwise, the operation with fail with Access Denied. \n Data Factory needs no special permission to your Event Grid, and you do not need to assign special RBAC permission to Data Factory service principal for the operation. \n \n \n In order to understand how Azure Data Factory delivers the two promises, let's take a step back and take a sneak peek behind the scene. These are the high level architecture for integration among Data Factory, Storage, and Event Grid. \n \n Create a new Storage Event Trigger Two noticeable callouts from the flows are:\n \n Azure Data Factory makes no direct contact with Storage account. Request to create a subscription is instead relayed and processed by Event Grid. Hence, your Data Factory needs no permission to Storage account in this stage \n Access control and permission checking happens on Azure Data Factory side. Before ADF issues a request to subscribe to Storage event, it checks the permission for the user. More specifically, it checks whether the Azure account signed in and attempting to create the Event trigger have appropriate access to the relevant Storage account. If the permission check fails, trigger creation also fails \n Any of the following RBAC settings works:\n \n Owner role to the storage account \n Contributor role to the storage account \n Microsoft.EventGrid/EventSubscriptions/Write permission to /subscriptions/####/resourceGroups/####/providers/Microsoft.Storage/storageAccounts/storageAccountName \n \n \n \n \n Storage event trigger Data Factory pipeline run \n \n \n \n \n When it comes to Event triggering pipeline in Data Factory, three noticeable call outs in the workflow: \n \n Event Grid uses a Push model that it relays the message as soon as possible when storage drops the message into the system. This is different from messaging system, such as Kafka where a Pull system is used. \n Event Trigger on Azure Data Factory serves as an active listener to the incoming message and it properly triggers the associated pipeline. \n Storage Event Trigger itself makes no direct contact with Storage account\n \n That said, if you have a Copy or other activity inside the pipeline to process the data in Storage account, Data Factory will make direct contact with Storage, using the credentials stored in the Linked Service. Please ensure that Linked Service is set up appropriately \n However, if you make no reference to the Storage account in the pipeline, you do not need to grant permission to Data Factory to access Storage account \n \n \n \n \n \n What's in the bag for the future? \n The team is currently in the process of expanding functionalities for Event Trigger family. Soon, we will support Custom Event in Event Grid to give customers even more flexibilities in defining the Event Driven Architecture. Please keep an eye out for the exciting announcement, as we test the functionality thoroughly and gradually roll it out to General Availability. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"4064","kudosSumWeight":1,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yMTAxNzgyLTI0OTc4N2lEMjdBN0M1ODdBRjE3Q0M0?revision=6\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yMTAxNzgyLTI0OTc5NmlFNDFCNjRCMTBBODIxQUJE?revision=6\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3260307":{"__typename":"Conversation","id":"conversation:3260307","topic":{"__typename":"BlogTopicMessage","uid":3260307},"lastPostingActivityTime":"2023-10-18T04:46:38.112-07:00","solved":false},"User:user:241953":{"__typename":"User","uid":241953,"login":"Abhishek Narain","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0yNDE5NTMtNzI4MjRpRjkxQTMyREM0OTZEQkFDRg"},"id":"user:241953"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMjYwMzA3LTM1NjY0MmlCMUI2MjY5MTJFNzE3MjNE?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMjYwMzA3LTM1NjY0MmlCMUI2MjY5MTJFNzE3MjNE?revision=1","title":"async-request.png","associationType":"BODY","width":791,"height":383,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMjYwMzA3LTM1NjY0NGk1NkFGQzY5MUJGMzQ4RTg5?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMjYwMzA3LTM1NjY0NGk1NkFGQzY5MUJGMzQ4RTg5?revision=1","title":"Screenshot 2022-03-17 at 9.33.41 AM.png","associationType":"BODY","width":1222,"height":1300,"altText":null},"BlogTopicMessage:message:3260307":{"__typename":"BlogTopicMessage","subject":"Web activity response timeout improvement","conversation":{"__ref":"Conversation:conversation:3260307"},"id":"message:3260307","revisionNum":1,"uid":3260307,"depth":0,"board":{"__ref":"Blog:board:AzureDataFactoryBlog"},"author":{"__ref":"User:user:241953"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Web activity response timeout improvement ","introduction":"","metrics":{"__typename":"MessageMetrics","views":12888},"postTime":"2022-03-17T10:18:39.399-07:00","lastPublishTime":"2022-03-17T10:18:39.399-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Web activity is helpful when invoking an external endpoint from within an ADF pipeline. While Azure Data Factory/ Synapse pipelines offer various orchestration activities, web activity helps provide extensibility when building data pipelines that span across different services. \n \n Web activities work exceptionally well with APIs that follow the asynchronous request-reply pattern, a suggested approach for building scalable web APIs/ services. In such scenarios, it automatically polls for the status endpoint (when it receives HTTP 202) till the API returns HTTP 200. It behaves synchronously and waits till the response is HTTP 200 before kicking off the down-stream activities. \n \n \n \n Most Azure-based APIs are async request-reply pattern and gels with the existing web activity behavior. \n \n Problem \n But in case you have API endpoints that do not follow the above pattern and expect the caller to wait for a response till the processing/ actions are done. As default action, the web activity will timeout within 1 minute if it does not receive any response. \n \n Improvement \n We are introducing a response timeout property 'httpRequestTimeout' in Web activity that will let you provide larger timeouts for such synchronous APIs. \n \n \n \n \n Reference: \n Web Activity - Azure Data Factory & Azure Synapse | Microsoft Docs ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"1443","kudosSumWeight":4,"repliesCount":3,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMjYwMzA3LTM1NjY0MmlCMUI2MjY5MTJFNzE3MjNE?revision=1\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMjYwMzA3LTM1NjY0NGk1NkFGQzY5MUJGMzQ4RTg5?revision=1\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3420904":{"__typename":"Conversation","id":"conversation:3420904","topic":{"__typename":"BlogTopicMessage","uid":3420904},"lastPostingActivityTime":"2023-08-25T02:59:56.985-07:00","solved":false},"User:user:76165":{"__typename":"User","uid":76165,"login":"Sandy Winarko","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS03NjE2NS0xNzk1ODRpMEVERDk1MDIyNDMwRkU1OA"},"id":"user:76165"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM3NDY4Mmk4MzYzRjE3QzNEMTc5M0Qw?revision=27\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM3NDY4Mmk4MzYzRjE3QzNEMTc5M0Qw?revision=27","title":"Noelle_Li_3-1653403499908.png","associationType":"TEASER","width":708,"height":477,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM3NDY3OWlBMjM1QUNCODIyNDFFOTQw?revision=27\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM3NDY3OWlBMjM1QUNCODIyNDFFOTQw?revision=27","title":"Noelle_Li_0-1653403291924.png","associationType":"BODY","width":1044,"height":171,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM3NDY5NWkxM0JGMDMzOEI1NTM3NkI4?revision=27\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM3NDY5NWkxM0JGMDMzOEI1NTM3NkI4?revision=27","title":"SandyWinarko_0-1653404669953.png","associationType":"BODY","width":999,"height":554,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM3NDY4MWkwRTU0QzJFMzcyRDEyMjc3?revision=27\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM3NDY4MWkwRTU0QzJFMzcyRDEyMjc3?revision=27","title":"Noelle_Li_2-1653403327003.png","associationType":"BODY","width":1358,"height":329,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM4NDQyNmlGNjkxOTc3OUMxM0JDMDZE?revision=27\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM4NDQyNmlGNjkxOTc3OUMxM0JDMDZE?revision=27","title":"uchrist_1-1656506268316.png","associationType":"BODY","width":1114,"height":341,"altText":null},"BlogTopicMessage:message:3420904":{"__typename":"BlogTopicMessage","subject":"Announcing Public Preview of the SAP CDC solution in Azure Data Factory and Azure Synapse Analytics","conversation":{"__ref":"Conversation:conversation:3420904"},"id":"message:3420904","revisionNum":27,"uid":3420904,"depth":0,"board":{"__ref":"Blog:board:AzureDataFactoryBlog"},"author":{"__ref":"User:user:76165"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n UPDATE! We've launched the new SAP connector in ADF today, June 30, 2022, and updated the blog post below accordingly. \n \n For decades, companies have relied on Microsoft and SAP software to run their most mission-critical operations. Today, we’re excited to launch public preview of SAP Change Data Capture (CDC) in Azure Data Factory (ADF). Combining a new data connector with predefined data flow templates, this solution streamlines the integration of SAP data within core Azure services like Azure Synapse Analytics and Azure Machine Learning. \n \n The new SAP ODP connector leverages SAP Operational Data Provisioning (ODP) framework, which is an established best practice for data integration within SAP landscapes. ODP provides access to a wide range of sources across all major SAP applications and comes with built-in CDC capabilities. In combination with the predefined data flow templates to process and update the changed records to any sink, this makes SAP data integration into Azure very much straight forward. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":40808},"postTime":"2022-05-24T08:00:02.369-07:00","lastPublishTime":"2022-07-21T07:22:53.130-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" UPDATE! We've launched the new SAP connector in ADF today, June 30, 2022, and updated the blog post below accordingly. \n \n For decades, companies have relied on Microsoft and SAP software to run their most mission-critical operations. Today, we’re excited to launch the public preview of SAP Change Data Capture (CDC) in Azure Data Factory (ADF) and Azure Synapse Analytics. Combining a new data connector with predefined data flow templates, this solution streamlines the integration of SAP data within core Azure services like Azure Synapse Analytics and Azure Machine Learning. \n \n The new SAP ODP connector leverages SAP Operational Data Provisioning (ODP) framework, which is an established best practice for data integration within SAP landscapes. ODP provides access to a wide range of sources across all major SAP applications and comes with built-in CDC capabilities. In combination with the predefined data flow templates to process and update the changed records to any sink, this makes SAP data integration into Azure very much straight forward. \n \n \n \n Background \n For many of our customers, SAP systems are critical to their business operations. As organizations mature, become more sophisticated, and graduate from using only descriptive analytics to adopting more predictive/prescriptive analytics, they want to combine their SAP data with non-SAP data in Azure, where they can leverage the advanced data integration and analytics capabilities to generate timely business insights. ADF is a data integration (ETL/ELT) Platform as a Service (PaaS) and, for SAP data integration, ADF currently offers six connectors: \n \n \n \n These connectors can only extract data in batches, where each batch treats old and new data equally without identifying data changes (“batch mode”). This extraction mode isn’t optimal when dealing with large data sets, such as tables with millions or even billions of records, that change often. To keep your copied SAP data fresh, frequently extracting it in full is expensive and inefficient. \n \n There’s a manual and limited workaround to extract mostly new or updated records, but this process requires a column with timestamp or monotonously increasing values, and continuously tracking the highest value since last extraction (“watermarking”). Unfortunately, some tables have no column that can be used for watermarking and this process can’t handle deleted records. \n \n Our customers have been asking for a new connector that can extract only data changes (inserts/updates/deletes = “deltas”), using CDC capabilities provided by SAP systems (“CDC mode”). To meet this need, we’ve built a new SAP connector leveraging SAP ODP framework. This new connector can connect to all SAP systems that support ODP, such as ECC, S/4HANA, BW, and BW/4HANA, directly at the application layer or indirectly using SAP Landscape Transformation (SLT) replication server as a proxy. The connector can fully or incrementally extract SAP data that includes not only physical tables, but also logical objects created on top of those tables, such as Extractors or ABAP Core Data Services (CDS) views, without watermarking. \n \n How does it work? \n Our new SAP ODP connector can extract various data source (“provider”) types, such as: \n \n SAP extractors, originally built to extract data from SAP ECC and load it into SAP BW \n ABAP CDS views, the new data extraction standard for SAP S/4HANA \n InfoProviders and InfoObjects in SAP BW or BW/4HANA \n SAP application tables, when using SLT replication server as a proxy \n \n These providers run on SAP systems to convert full/incremental data into data packages in Operational Delta Queue (ODQ) that can be consumed by ADF pipelines leveraging SAP ODP connector (\"subscriber\"). \n \n You can run ADF copy activity with SAP ODP connector on self-hosted integration runtime (SHIR) to extract the raw SAP data and load it into any destination, such as Azure Blob Storage or Azure Data Lake Store (ADLS) Gen2, in CSV/Parquet format, essentially archiving/preserving all historical changes. With the available data flow templates, it becomes straight forward to design and run ADF data flow activity on Azure Databricks/Apache Spark cluster (Azure IR) to transform the raw SAP data, merge all changes, and load the result into any destination, such as Azure SQL Database or Azure Synapse Analytics, in effect replicating your SAP data. \n \n \n \n If you load the merged result into ADLS Gen2 in Delta format (Delta Lake/Lakehouse), you can query it using Azure Synapse serverless SQL/Apache Spark pool to produce snapshots of SAP data for any specified periods in the past (“time-travel”). ADF pipelines containing these copy and data flow activities can be auto-generated using ADF templates and frequently run using ADF tumbling window triggers to replicate SAP data into Azure with low latency and without watermarking. \n \n \n \n Today, June 30, 2022, we are releasing our SAP CDC solution in ADF including SAP ODP connector and data replication templates for public preview. \n \n To learn more about this new solution, see our online webinar and docs. \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"5407","kudosSumWeight":15,"repliesCount":18,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM3NDY4Mmk4MzYzRjE3QzNEMTc5M0Qw?revision=27\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM3NDY3OWlBMjM1QUNCODIyNDFFOTQw?revision=27\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM3NDY5NWkxM0JGMDMzOEI1NTM3NkI4?revision=27\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM3NDY4MWkwRTU0QzJFMzcyRDEyMjc3?revision=27\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDU","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNDIwOTA0LTM4NDQyNmlGNjkxOTc3OUMxM0JDMDZE?revision=27\"}"}}],"totalCount":5,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[{"__typename":"VideoEdge","cursor":"MHxodHRwczovL3lvdXR1LmJlL1ZURDQzaUZEM1FrfDB8MjU7MjV8fA","node":{"__typename":"AssociatedVideo","videoTag":{"__typename":"VideoTag","vid":"https://youtu.be/VTD43iFD3Qk","thumbnail":"https://i.ytimg.com/vi/VTD43iFD3Qk/hqdefault.jpg","uploading":false,"height":338,"width":600,"title":null},"videoAssociationType":"INLINE_BODY"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3239969":{"__typename":"Conversation","id":"conversation:3239969","topic":{"__typename":"BlogTopicMessage","uid":3239969},"lastPostingActivityTime":"2023-04-13T23:53:53.819-07:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMjM5OTY5LTM1MjMxNWkzMzA2MTIxNDlBNERGODgz?revision=4\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMjM5OTY5LTM1MjMxNWkzMzA2MTIxNDlBNERGODgz?revision=4","title":"AbhishekNarain_0-1646209643679.png","associationType":"BODY","width":908,"height":512,"altText":null},"BlogTopicMessage:message:3239969":{"__typename":"BlogTopicMessage","subject":"Execute SQL statements using the new 'Script' activity in Azure Data Factory and Synapse Pipelines","conversation":{"__ref":"Conversation:conversation:3239969"},"id":"message:3239969","revisionNum":4,"uid":3239969,"depth":0,"board":{"__ref":"Blog:board:AzureDataFactoryBlog"},"author":{"__ref":"User:user:241953"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Script activity provides the ability to execute single or multiple SQL statements. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":61409},"postTime":"2022-03-02T00:33:16.785-08:00","lastPublishTime":"2022-03-02T00:41:08.139-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" We are introducing a Script activity in pipelines that provide the ability to execute single or multiple SQL statements. \n \n \n \n Using the script activity, you can execute common operations with Data Manipulation Language (DML), and Data Definition Language (DDL). DML statements like SELECT, UPDATE, and INSERT let users retrieve, store, modify, delete, insert and update data in the database. DDL statements like CREATE, ALTER, and DROP allow a database manager to create, modify, and remove database objects such as tables, indexes, and users. \n \n Script activity can be used for a variety of purposes: \n \n Truncate a table or view in preparation for inserting data. \n Create, alter, and drop database objects such as tables and views. \n Re-create fact and dimension tables before loading data into them. \n Run stored procedures. \n Use the rowset/ resultset returned from a query in a downstream activity. \n \n \n Supported data stores: \n \n Azure SQL Database \n Azure Synapse Analytics \n SQL Server Database \n Oracle \n Snowflake \n \n \n The below table compares Script activity with existing pipeline activities like Lookup, SProc and can provide guidance on when to choose what. \n \n \n \n \n \n \n \n \n Script Activity \n \n \n Lookup Activity \n \n \n SProc Activity \n \n \n \n \n Supported data source \n \n \n Database (SQL family, Snowflake, Oracle) \n \n \n all data sources \n \n \n SQL family \n \n \n \n \n Supported Operations \n \n \n Read / Modify \n \n \n Read* \n \n \n Modify \n \n \n \n \n Multiple query support \n \n \n Yes \n \n \n No** \n \n \n Yes*** \n \n \n \n \n Query parameter support \n \n \n Input / Output \n \n \n Not supported \n \n \n Input \n \n \n \n \n Output Result set support \n \n \n One or more \n \n \n One \n \n \n No \n \n \n \n \n Output query logs (PRINT) \n \n \n Yes \n \n \n No \n \n \n No \n \n \n \n \n Integrated CICD (ADF) \n \n \n Yes \n \n \n Yes \n \n \n No \n \n \n \n \n * Lookup activity does not block modify operations, but it is not recommended to use lookup activity to modify data. \n ** Multiple queries can be executed successfully in lookup activity for some connectors but cannot retrieve full result. \n *** For SProc activity, ‘multiple queries’ means one stored procedure with multiple queries in it. Cannot support executing more than one stored procedure in a single activity. \n \n \n Note: Be careful when returning result sets, since the activity output is limited to 5000 row/ 2MB size. If you are logging through your SQL script (Print statements), you can work around the limit by choosing your Storage account for logging. \n \n For more details, refer script activity documentation. \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"2653","kudosSumWeight":8,"repliesCount":24,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMjM5OTY5LTM1MjMxNWkzMzA2MTIxNDlBNERGODgz?revision=4\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3724379":{"__typename":"Conversation","id":"conversation:3724379","topic":{"__typename":"BlogTopicMessage","uid":3724379},"lastPostingActivityTime":"2023-03-07T14:29:30.394-08:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI0NWk5QUMzNDJBRjk1RTFCNkU1?revision=9\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI0NWk5QUMzNDJBRjk1RTFCNkU1?revision=9","title":"AbhishekNarain_5-1674638816112.png","associationType":"TEASER","width":1794,"height":1102,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI0MGlDQjRFMTFFN0U5RTA4OEYz?revision=9\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI0MGlDQjRFMTFFN0U5RTA4OEYz?revision=9","title":"AbhishekNarain_1-1674638533411.png","associationType":"BODY","width":940,"height":346,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI0NmlEQThBMzcwMDYwOUQ2NUNC?revision=9\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI0NmlEQThBMzcwMDYwOUQ2NUNC?revision=9","title":"AbhishekNarain_6-1674638924720.png","associationType":"BODY","width":644,"height":658,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI1MmlCMjRFMEU0NkJFMEY4ODJD?revision=9\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI1MmlCMjRFMEU0NkJFMEY4ODJD?revision=9","title":"AbhishekNarain_8-1674639505172.png","associationType":"BODY","width":940,"height":818,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI1M2kyQUFDRTVEQUI2REZGNTlG?revision=9\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI1M2kyQUFDRTVEQUI2REZGNTlG?revision=9","title":"AbhishekNarain_9-1674639603373.png","associationType":"BODY","width":1796,"height":946,"altText":null},"BlogTopicMessage:message:3724379":{"__typename":"BlogTopicMessage","subject":"Orchestrate and operationalize Synapse Notebooks and Spark Job Definitions from Azure Data Factory","conversation":{"__ref":"Conversation:conversation:3724379"},"id":"message:3724379","revisionNum":9,"uid":3724379,"depth":0,"board":{"__ref":"Blog:board:AzureDataFactoryBlog"},"author":{"__ref":"User:user:241953"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n Today, we are introducing support for orchestrating Synapse notebooks and Synapse spark job definitions (SJD) natively from Azure Data Factory pipelines. These new native activities make it easy to run Synapse notebooks and SJD from an ADF pipeline! ","introduction":"","metrics":{"__typename":"MessageMetrics","views":12563},"postTime":"2023-01-25T11:18:15.800-08:00","lastPublishTime":"2023-01-27T16:33:51.056-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n \n Today, we are introducing support for orchestrating Synapse notebooks and Synapse spark job definitions (SJD) natively from Azure Data Factory pipelines. It immensely helps customers who have invested in ADF and Synapse Spark without requiring to switch to Synapse Pipelines for orchestrating Synapse Notebooks and SJD. \n \n NOTE: Synapse notebook and SJD activities were only available in Synapse Pipelines previously. \n \n One of the critical benefits of Synapse notebooks is the ability to use Spark SQL and PySpark to perform data transformations. It allows you to use the best tool for the job, whether it be SQL for simple data cleaning tasks or PySpark for more complex data processing tasks. \n \n How to get started with Synapse Notebooks in ADF? \n \n1. Add Synapse Notebook activity into a Data Factory pipelines \n \n \n \n \n 2. Create a connection to Synapse workspace through a new compute Linked Service (Azure Synapse Analytics Artifact) \n \n \n \n 3. Choose an existing notebook to operationalize \n \n \n Note: If you do not specify 'Spark pool', 'Executor size', etc., it will use the one specified in the notebook. These properties are optional and only provides you additional spark configurations to override these during the operational run. \n 4. Grant the ADF Managed Identity the \"Synapse Compute Operator\" permissions to execute a Notebook / SJD in the Synapse Workspace \n \n \n Step 2 (Creation of Azure Synapse Analytics artifact linked service) highlights the Managed Identity Name of the Data Factory that needs to be granted permission to run a notebook / SJD. \n \n 5. Monitor the notebook run details by accessing the activity output, which contains \"sparkApplicationStudioUrl\" that takes you to Synapse Workspace for detailed run monitoring. Notebook \"exitValue\" is also accessible in the output and can be referenced in the down stream activities. \n \n \n \n Resources \n \n \n Documentation: Synapse Notebook activity in ADF \n \n \n Documentation: Synapse SJD (Spark job definition) activity in ADF \n \n \n Documentation: Azure Synapse Analytics (Artifact) Linked Service in ADF \n \n \n Permissions required for running Synapse notebooks and SJDs \n \n \n \n We are always open for feedback so please let us know your thoughts in the comments below or add to our Ideas forum.\n \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"2512","kudosSumWeight":1,"repliesCount":7,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI0NWk5QUMzNDJBRjk1RTFCNkU1?revision=9\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI0MGlDQjRFMTFFN0U5RTA4OEYz?revision=9\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI0NWk5QUMzNDJBRjk1RTFCNkU1?revision=9\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI0NmlEQThBMzcwMDYwOUQ2NUNC?revision=9\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDU","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI1MmlCMjRFMEU0NkJFMEY4ODJD?revision=9\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDY","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI0Mzc5LTQzNTI1M2kyQUFDRTVEQUI2REZGNTlG?revision=9\"}"}}],"totalCount":6,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:1922818":{"__typename":"Conversation","id":"conversation:1922818","topic":{"__typename":"BlogTopicMessage","uid":1922818},"lastPostingActivityTime":"2022-12-26T01:32:39.533-08:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xOTIyODE4LTIzNTU0N2lDMDRFOEY2MEY2ODkyRjY3?revision=10\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xOTIyODE4LTIzNTU0N2lDMDRFOEY2MEY2ODkyRjY3?revision=10","title":"databricks-grant-access-to-adf-msi-3.jpg","associationType":"TEASER","width":618,"height":710,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xOTIyODE4LTIzNTUzNmkzOUUxMTYxQTA3MTZFNDBD?revision=10\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xOTIyODE4LTIzNTUzNmkzOUUxMTYxQTA3MTZFNDBD?revision=10","title":"databricks-grant-access-to-adf-msi-1.jpg","associationType":"BODY","width":673,"height":468,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xOTIyODE4LTIzNTUzN2k0QzQxNDgxNzQ3Q0IyQzMx?revision=10\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xOTIyODE4LTIzNTUzN2k0QzQxNDgxNzQ3Q0IyQzMx?revision=10","title":"databricks-grant-access-to-adf-msi-2.jpg","associationType":"BODY","width":433,"height":738,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xOTIyODE4LTIzNTUzOGk4N0QyNEI1OTUyQUUwOUJD?revision=10\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xOTIyODE4LTIzNTUzOGk4N0QyNEI1OTUyQUUwOUJD?revision=10","title":"databricks-grant-access-to-adf-msi-3.jpg","associationType":"BODY","width":618,"height":710,"altText":null},"BlogTopicMessage:message:1922818":{"__typename":"BlogTopicMessage","subject":"Azure Databricks activities now support Managed Identity authentication","conversation":{"__ref":"Conversation:conversation:1922818"},"id":"message:1922818","revisionNum":10,"uid":1922818,"depth":0,"board":{"__ref":"Blog:board:AzureDataFactoryBlog"},"author":{"__ref":"User:user:241953"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Managed identity authentication in databricks linked service ","introduction":"","metrics":{"__typename":"MessageMetrics","views":65675},"postTime":"2020-11-23T03:27:28.173-08:00","lastPublishTime":"2020-11-23T05:58:36.405-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Azure Databricks supports Azure Active Directory (AAD) tokens (GA) to authenticate to REST API 2.0. The AAD tokens support enables us to provide a more secure authentication mechanism leveraging Azure Data Factory's System-assigned Managed Identity while integrating with Azure Databricks. \n \n Benefits of using Managed identity authentication: \n \n Managed identities eliminate the need for data engineers having to manage credentials by providing an identity for the Azure resource in Azure AD and using it to obtain Azure Active Directory (Azure AD) tokens. In our case, Data Factory obtains the tokens using it's Managed Identity and accesses the Databricks REST APIs. \n It lets you provide fine-grained access control to particular Data Factory instances using Azure AD. \n It helps prevent usage of Databricks Personal Access Tokens, which acts as a password and needs to be treated with care, adding additional responsibility on data engineers on securing it. \n \n Earlier, you could access the Databricks Personal Access Token through Key-Vault using Manage Identity. Now, you can directly use Managed Identity in Databricks Linked Service, hence completely removing the usage of Personal Access Tokens. \n \n High-level steps on getting started: \n \n Grant the Data Factory instance 'Contributor' permissions in Azure Databricks Access Control. \n Create a new 'Azure Databricks' linked service in Data Factory UI, select the databricks workspace (in step 1) and select 'Managed service identity' under authentication type. \n \n \n Note: Please toggle between the cluster types if you do not see any dropdowns being populated under 'workspace id', even after you have successfully granted the permissions (Step 1). \n \n Sample Linked Service payload: \n \n {\n \"name\": \"AzureDatabricks_ls\",\n \"type\": \"Microsoft.DataFactory/factories/linkedservices\",\n \"properties\": {\n \"annotations\": [],\n \"type\": \"AzureDatabricks\",\n \"typeProperties\": {\n \"domain\": \"https://adb-***.*.azuredatabricks.net\",\n \"authentication\": \"MSI\",\n \"workspaceResourceId\": \"/subscriptions/******-3ab0-48f2-b171-0f50ec******/resourceGroups/work-rg/providers/Microsoft.Databricks/workspaces/databricks-****\",\n \"existingClusterId\": \"****-030259-dent495\"\n }\n }\n} \n \n Note: There are no secrets or personal access tokens in the linked service definitions! ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"2526","kudosSumWeight":7,"repliesCount":5,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xOTIyODE4LTIzNTU0N2lDMDRFOEY2MEY2ODkyRjY3?revision=10\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xOTIyODE4LTIzNTUzNmkzOUUxMTYxQTA3MTZFNDBD?revision=10\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xOTIyODE4LTIzNTUzN2k0QzQxNDgxNzQ3Q0IyQzMx?revision=10\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xOTIyODE4LTIzNTUzOGk4N0QyNEI1OTUyQUUwOUJD?revision=10\"}"}}],"totalCount":4,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:2013406":{"__typename":"Conversation","id":"conversation:2013406","topic":{"__typename":"BlogTopicMessage","uid":2013406},"lastPostingActivityTime":"2022-11-02T03:01:55.659-07:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yMDEzNDA2LTI0MjY1MGlDQjVBN0U1M0Q0MDY4RkZF?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yMDEzNDA2LTI0MjY1MGlDQjVBN0U1M0Q0MDY4RkZF?revision=8","title":"fill2.png","associationType":"TEASER","width":434,"height":382,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yMDEzNDA2LTI0MjY1MWk0ODkyNUY4QUE3RDQxRjgx?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yMDEzNDA2LTI0MjY1MWk0ODkyNUY4QUE3RDQxRjgx?revision=8","title":"fill2.png","associationType":"BODY","width":434,"height":382,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yMDEzNDA2LTI0MjYwOWkyMDgwQjdEQ0FEREZFMjNC?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yMDEzNDA2LTI0MjYwOWkyMDgwQjdEQ0FEREZFMjNC?revision=8","title":"fill1.png","associationType":"BODY","width":1098,"height":118,"altText":null},"BlogTopicMessage:message:2013406":{"__typename":"BlogTopicMessage","subject":"Implement Fill Down in ADF and Synapse Data Flows","conversation":{"__ref":"Conversation:conversation:2013406"},"id":"message:2013406","revisionNum":8,"uid":2013406,"depth":0,"board":{"__ref":"Blog:board:AzureDataFactoryBlog"},"author":{"__ref":"User:user:175989"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \"Fill Down\" is an operation common in data prep and data cleansing meant to solve the problem with data sets when you want to replace NULL values with the value from the previous non-NULL value in the sequence. Here is how to implement this in ADF and Synapse data flows. \n \n ","introduction":"","metrics":{"__typename":"MessageMetrics","views":6467},"postTime":"2020-12-23T14:38:25.299-08:00","lastPublishTime":"2020-12-23T15:02:21.898-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \"Fill down\" is an operation common in data prep and data cleansing meant to solve the problem with data sets when you want to replace NULL values with the value from the previous non-NULL value in the sequence. Here is how to implement this in ADF and Synapse data flows. \n \n Note that this operation can have negative performance implications because you must create a synthetic window across your entire data set with a \"dummy\" category value. Additionally, you must sort by a value to create the proper data sequence to find the previous non-NULL value. \n \n In this case, my products data is missing the Color value in several rows. To fix this, I applied the fill down pattern in the Window transformation to fill in the NULL values: \n \n \n To achieve this, I used the coalesce formula in the Window transformation: \n \n \n coalesce(Color, last(Color, true())) \n \n \n The key to make this work is to first set the Window \"over\" clause to your entire data set. If you do not already have a value that is the same across all rows, you can set a synthetic window value in a Derived Column. In my case, I set an integer value of 1 to a column called \"dummy\". \n \n Next, the Window sorting should be sorted by the column that sets the proper ordering such that the previous non-NULL value will be your fill value. In my case, I used the Surrogate Key transformation to create an incrementing key to sort by. \n \n The final fill-down pattern looks something like this: \n \n \n \n To make this easy to add to your data flow pipelines, I added the script-behind as a data flow snippet to our online recipes here. \n \n I've also included this snippet below. This creates the synthetic category as \"dummy\" and sorts by a surrogate key. You can remove the surrogate key and use your own data-specific sort key. This code snippet assumes you've already added a Source transformation to your data flow called source1. \n \n source1 derive(dummy = 1) ~> DerivedColumn\nDerivedColumn keyGenerate(output(sk as long),\n\tstartAt: 1L) ~> SurrogateKey\nSurrogateKey window(over(dummy),\n\tasc(sk, true),\n\tRating2 = coalesce(Color, last(Color, true()))) ~> Window1 \n 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