Introduction-Course Introduction
Course Introduction
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Section 1 - Batch Processing with Databricks and Data Factory on Azure-1.1 Introduction
1.1 Batch Processing with Databricks and Data Factory in Azure
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Section 1 - Batch Processing with Databricks and Data Factory on Azure-1.2 ELT Processing using Azure
1.2 - ELT Processing using Azure
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Section 1 - Batch Processing with Databricks and Data Factory on Azure-1.3 Databricks and Azure Spark
1.3 - Databricks and Azure Spark
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Azure Databricks and Apache Spark
Section 1 - Batch Processing with Databricks and Data Factory on Azure-1.4 Transform Data using Databricks in Azure Data Factory
1.4 Transform Data using Databricks in ADF
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Section 1 - Batch Processing with Databricks and Data Factory on Azure-1.5 Use Case: Azure Data Factory and Spark
1.5 Use Case: ADF and Spark
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Section 1 - Batch Processing with Databricks and Data Factory on Azure-Labs & Exercises
Exercise 1 - Use Batch Processing with Databricks and Data Factory on Azure
Exercise 2 - Intro to Databricks and Data Factory Page
Section 2 - Creating Pipelines and Activities-2.1 Introduction
Pipelines and Activities - Introduction
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Section 2 - Creating Pipelines and Activities-2.2 Processing using Pipelines and Activities with Azure Data Factory
Processing using a Pipeline
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Section 2 - Creating Pipelines and Activities-2.3 Analyzing Logs for an HDInsight Cluster
Analyzing Logs for an HDInsight Cluster
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Section 2 - Creating Pipelines and Activities-2.4 Using Azure Blob Storage within HDInsight
Using Azure Blob Storage within HDInsight
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Using Azure Blob Storage with HDInsight
Section 2 - Creating Pipelines and Activities-Labs & Exercises
Exercise 1 - Pipeline Activities & Usage in Azure Data Factory
Exercise 2 - Examine Logs within the HDInsight/Blob Storage
Section 3 - Link Services and Datasets-3.1 Introduction
Link Services and Datasets - Introduction
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Section 3 - Link Services and Datasets-3.2 Identifying pipelines for a Data Factory
Identifying Pipelines for a Data Factory
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Section 3 - Link Services and Datasets-3.3 Data stores and Azure Blob Datasets, Containers, and Folders
Data Stores and Azure Blob Storage
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Section 3 - Link Services and Datasets-3.4 Linked Service and Connecting Data Factory to External Resources
Linked Service and Connecting Data Factory to External Resources
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Section 3 - Link Services and Datasets-3.5 Processing Input Blobs with Azure Data Factory
Processing Input Blobs with Azure Data Factory
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Section 3 - Link Services and Datasets-Labs & Exercises
Exercise 1 - Link Data within Datasets in Azure Storage
Section 4 - Schedules and Triggers-4.1 Introduction
Schedules and Triggers - Introduction
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Section 4 - Schedules and Triggers-4.2 Creating a Trigger that Runs A Pipeline on a Schedule
Creating a Trigger that Runs a Pipeline on a Schedule
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Section 4 - Schedules and Triggers-4.3 Scheduling a Trigger in Azure Data Factory
Scheduling a Trigger in Azure Data Factory
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Section 4 - Schedules and Triggers-4.4 Pipeline Execution and Triggers in Azure Data Factory
Pipeline Execution and Triggers in ADF
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Pipeline Execution and Triggers in ADF
Section 4 - Schedules and Triggers-4.5 Use Case: Azure Schedule/Trigger/Events
Use Case: Azure Schedule, Trigger, and Events
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Section 5 - Selecting Windowing Functions-5.1 Introduction
Selecting Windowing Functions - Introduction
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Section 5 - Selecting Windowing Functions-5.2 How Stream Analytics Support Native Windowing Functions to Enable Developers to Author Complex Stream Processing Jobs
How Stream Analytics Support Native Windowing Functions
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Section 5 - Selecting Windowing Functions-5.3 Four Kinds of Temporal Windows
Temporal Windows
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Understanding Stream Analytics Windowing Functions
Section 5 - Selecting Windowing Functions-5.4 Using Window Functions in the GROUP BY Clause of the Query Syntax in Your Stream Analytics Job
Using Window Functions in the GROUP BY Clause
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Section 5 - Selecting Windowing Functions-5.5 Aggregating Events over Multiple Windows using WindowsQ
Aggregating Events over Multiple Windows using WindowsQ
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Section 6 - Configuring Input and Output for Streaming Data Solutions-6.1 Introduction
How Stream Analytics Relate to Data Solutions
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Section 6 - Configuring Input and Output for Streaming Data Solutions-6.2 Generating Sample Phone Call Data and Sending it to Azure Event Hubs
Generate Sample Call Data and Send it to Event Hubs
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Section 6 - Configuring Input and Output for Streaming Data Solutions-6.3 Creating a Stream Analytics Job
Creating a Stream Analytics Job
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Section 6 - Configuring Input and Output for Streaming Data Solutions-6.4 Configuring Job Input and Output
Configuring Job Input and Output
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Section 6 - Configuring Input and Output for Streaming Data Solutions-6.5 Defining a Query to Filter Fraudulent Calls
Define a Query to Filter Fraudulent Calls
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Section 6 - Configuring Input and Output for Streaming Data Solutions-6.6 Testing and Starting the Job
Test and Start the Job
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Section 6 - Configuring Input and Output for Streaming Data Solutions-6.7 Visualizing Results in Power BI
Visualize Results in Power BI
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Output Real-Time Stream Analytics Data to a Power BI Dashboard
Section 7 - ELT versus ETL in Polybase-7.1 Introduction
ELT vs ETL in PolyBase - Introduction
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Section 7 - ELT versus ETL in Polybase-7.2 How SQL Data Warehouse in Microsoft Offers Extract Load Transform Solutions
How SQL Data Warehouse in Microsoft Offers ELT Solutions
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Video: Ingesting Data using Polybase | Azure SQL Data Warehouse
Section 7 - ELT versus ETL in Polybase-7.3 SQL Data Warehouse Loading Methods using non-Polybase Options
Loading Methods using Non-PolyBase Options
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Section 7 - ELT versus ETL in Polybase-7.4 Use Case: A Deeper Dive into ETL Processing using Polybase and ELT Solutions using Microsoft Datawarehouse ELT Approach
Use Case: A Deeper Dive into ETL Processing
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