Gaurav Malhotra joins Scott Hanselman to show how you can run your Azure Machine Learning (AML) service pipelines as a step in your Azure Data Factory (ADF) pipelines.

This enables you to run your machine learning models with data from multiple sources (85+ data connectors supported in ADF).

This seamless integration enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns.     

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Data integration is complex and has many moving parts that spans across hybrid data environments. Typically, data integration projects have dependencies upstream and downstream making dependencies an important aspect to consider in any job scheduling.

Gaurav Malhotra joins Scott Hanselman to show how you can create dependent pipelines in Azure Data Factory by creating dependencies between tumbling window triggers in your pipelines. Using these dependencies assures you that the trigger is only executed after the successful execution of the dependent trigger in your data factory.

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Gaurav Malhotra joins Scott Hanselman to show how to build a modern data warehouse solution from ingress of structured, unstructured, semi-structured data to code-free data transformation at scale and finally to extracting business insights into your Azure SQL Data Warehouse.     

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Gaurav Malhotra joins Scott Hanselman to discuss the Azure Data Factory visual tools, which enable you to iteratively create, configure, test, deploy, and monitor data integration pipelines. We took into account your feedback to enable functional, performance, and security improvements to the visual tools.

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Gaurav Malhotra joins Scott Hanselman to discuss Azure Data Factory (ADF) integration with Azure Monitor, which enables you to route your data factory metrics to Operations and Management Suite (OMS). Get the Azure Data Factory Analytics OMS service pack from the Azure marketplace.

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Gaurav Malhotra discusses how you can operationalize Jars and Python scripts running on Azure Databricks as an activity step in a Data Factory pipeline.

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Today’s business managers depend heavily on reliable data integration systems that run complex ETL/ELT workflows (extract, transform/load and load/transform data).

Gaurav Malhotra joins Scott Hanselman to discuss how you can iteratively build, debug, deploy, and monitor your data integration workflows (including analytics workloads in Azure Databricks) using Azure Data Factory pipelines.

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Gaurav Malhotra shows Donovan Brown how you can now visually build pipelines for Azure Data Factory V2 and be more productive by getting pipelines up & running quickly without writing any code.

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