Philadelphia SQL Server Users Group (PSSUG), a 501(c)(3)’s Post

Wrapping up Sunday with a new Azure project! As part of my Azure learning journey, I built a pipeline in Azure Data Factory using the medallion architecture to move data from a Data Lake into Azure SQL Server. Check out the details here: #data #Azure #AzureDataFactory

Create a Config table and load SQL data into Azure storage accounts parallelly based on SQL flags and configuration values from the table. In this hands-on demo, you will learn how to create and insert configurations into an SQL server and create azure linked services to connect to Azure resources and query the data. The pipeline will connect to the SQL server through the linked service dataset and query the config values. For every item in the config response, the pipeline will parallel execute the configuration values to query SQL tables and write to Azure blog storage via copy activity. #azuredataengineer #azuredatafactory #azurecloud #azuredatabricks #dataengineering #etl #datapipeline #azureservices #sql #sqlserver #ssms https://lnkd.in/gexHphnY

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D11 #azuredataengineer #azurecloud #azuredatafactory #azurecloudengineer #cloudengineer Triggers in Azure Data Factory (ADF) allow you to automate the execution of your data pipelines at specified intervals Scheduled triggers in ADF? The schedule trigger is used to execute the Azure Data Factory pipelines on a wall-clock schedule. Where you need to specify the reference time zone that will be used in the trigger start and end date, when the pipeline will be executed, how frequent it will be executed and optionally the end date for that pipeline. Here's a brief overview: Pipeline Creation: Author your data workflow using ADF's authoring tools. Add Trigger: Within the pipeline, click "Add Trigger" to set up a trigger. Choose "New/Edit" to configure the trigger details. Schedule Configuration: Select the trigger type, often "Tumbling Window" for recurring schedules. Define start/end times, recurrence frequency, and time zone. Dependencies and Conditions: Set dependencies or conditions for execution, ensuring proper sequence if needed. Debugging and Monitoring: Utilize debugging tools and monitoring features to validate and track pipeline runs. In the ADF interface, it might look like this: Adjustments will depend on your specific requirements, but this provides a general idea of setting up scheduled triggers in Azure Data Factory. #adf #bigdataengineer #developercommunity #bigquery

In Azure Data Factory (ADF) for incremental data load: 1. Identify a column with timestamps or incrementing IDs. 2. Use a watermark to track the last loaded value. 3. Store the watermark in a persistent location. 4. Create parameters and use dynamic SQL in source datasets. 5. Update the watermark after a successful load. 6. Implement pipeline activities for control flow. 7. Consider soft deletes or maintaining a separate table for deletions. Activities used in ADF pipeline: Lookup + Copy data + Stored procedure Source: Azure SQL DB || Sink: ADLS Gen2 First Lookup activity, second Copy activity and third Stored Procedure to update Watermarktable with LastModifiedTime. In Stored Procedure activity settings option to update LastModifiedTime using @{activity('Lookup2').output.firstRow.NewwatermarkValue} and to update TableName using @{activity('Lookup1').output.firstRow.TableName}.   Incremental data load is used to optimize data transfer and processing by loading only new or modified data since the last load, reducing time and resources in data integration workflows. Thanks Arun Kumar for your constant guidance and such productive training sessions. #azuredatafactory #azure #azuredataengineer #dataengineering #ForumDE #mentor Arun Kumar

🎉Exciting week with Azure Data Factory! 🎊 Just guided my team to wrapped up a data pipeline project, seamlessly moving data from on-premises SQL Server to Azure Blob Storage. Can't express how much it made my job easy - from orchestrating workflows to the scalability, ADF is a game-changer! 🌐💡 #AzureDataFactory #DataIntegration #CloudInnovation Whenever we use ADF, I suggest my team to follow below basic coding model and adapt it based on specific requirements. It's the foundation for seamless data workflows! 💻📊 #DataPipeline #TechInAction" Basic Coding Model: { "name": "CopyDataPipeline", "activities": [ { "name": "CopyDataActivity", "type": "Copy", "inputs": [ { "referenceName": "OnPremSQLServerDataset" } ], "outputs": [ { "referenceName": "AzureBlobStorageDataset" } ], "typeProperties": { "source": { "type": "SqlSource" }, "sink": { "type": "BlobSink" } } } ] }

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🚀 Excited to share that I've successfully completed a Project using ADF on covid-19 analysis !! I have utilized azure services like Azure data factory , Azure blob storage , Azure Data Lake Gen2 and Azure SQL database in this project . Below is the Project Execution Flow : - Extracted the data from Azure blob and http website and ingested it to Azure data lake Gen 2 . - Transformed the data using Data Flow in ADF . - Loaded the transformed data into Azure SQL database . - Created an E2E pipeline for all the above Activities and also used triggers (Storage event and tumbling window) for executing the pipeline on daily basis . ADF components used are : -Linked Services -Integration Runtime -Activity -Pipeline -Data Flow -Triggers GitHub link - https://lnkd.in/gJa5_XgR #DataEngineer #Azure #ADF #ETL #DataPipeline

## My Azure Data Factory Journey: A few weeks ago, I decided to learn Azure Data Factory (ADF), and it's been a transformative experience! Although I haven't integrated it into my workflow yet, the learning process has been eye-opening. Having used SQL Server Management Studio (SSMS) and SQL Server Integration Services (SSIS) , I wanted to share some key insights and advantages I've found with ADF. Real-Life Benefits Over SSMS and SSIS 1. Cloud Power   - Scalability: ADF scales effortlessly with your needs, no more worrying about server limits   -Managed Service: Less maintenance, more focus on actual data tasks 2. Cost Efficiency   -Pay-As-You-Go: Only pay for what you use, which is much more economical than maintaining on-prem infrastructure 3. Integration Made Easy   - Wide Range of Connectors: Easily connect to both cloud and on-premises data sources   - Hybrid Integration: Seamlessly combine data from different environments 4. Advanced Features   - Data Flows: Visual, code-free data transformations   - Pipeline Orchestration: Intuitive drag-and-drop interface with excellent monitoring tools 5.Security and Compliance   - Built-In Security: Robust security features like data encryption and Azure Active Directory integration ### Final Thoughts Learning ADF has been a game-changer. The process has not only expanded my skill set but also opened up new possibilities for future projects. If you're thinking about learning ADF or just getting started, dive in. It is worth it. #AzureDataFactory #DataIntegration #CloudComputing #TechJourney #DataTransformation CapItAll