Azure Data Factory Medium Simplifies Enterprise Data Processing

Azure Data Factory Medium is designed for organizations that need to process and transform large amounts of data.

It's a cloud-based platform that allows users to create data pipelines, integrate with various data sources, and execute complex data transformations.

With Azure Data Factory Medium, you can process data from various sources such as SQL Server, Oracle, and Azure Blob Storage.

This platform is particularly useful for organizations with large datasets, as it simplifies the data processing and transformation process.

To create a pipeline in Azure Data Factory, you can start by defining a new pipeline, such as the "runAllFilePrefixes" pipeline, and giving it two parameters. I set the FilePrefixes parameter to a simple JSON array.

You can add a single ForEach activity to the pipeline and set the Items property to the array of prefixes defined in the pipeline’s input parameter FilePrefixes. This allows you to iterate over the array of prefixes and perform actions for each one.

Pipelines are made up of discrete steps, called activities, which are executed on integration runtime. You pay for data pipeline orchestration by activity run, and activity execution by integration runtime hours.

To create the inner loop "copyWebhookSource" pipeline, start by creating a linked service to connect to the Azure Blob Storage resource where your data resides. This is the foundation of your pipeline.

Next, create the pipeline itself, which will contain two input parameters: one for the name of the webhook source (WebhookSource) and one for the FilePrefix to use. These parameters will be crucial for the pipeline's functionality.

To override certain properties in the pipeline, use the Code button in the top-right corner of the ADF editor. This will allow you to access and modify specific properties, such as the fieldList property.

In the fieldList property, look for the field called childItems and set it to expose. This will enable you to access the properties of the pipeline's activities.

To loop over all prefixes, use the ForEach activity, which will iterate over an array of items. Within the ForEach, add a single activity: Execute Pipeline. This will allow you to execute the previously created pipeline for each item in the array.

Creating the outer loop "runAllFilePrefixes" pipeline is a crucial step in automating file processing. You'll need to create a new pipeline and give it two parameters: FilePrefixes and WebhookSource.

To add a ForEach activity to the pipeline, set the Items property to the array of prefixes defined in the pipeline's input parameter FilePrefixes. This will allow you to loop over each prefix in the array.

Within the ForEach activity, you'll need to add a single activity: Execute Pipeline. Enter the name of the pipeline you previously created and set the two input parameters of the pipeline to set the FilePrefix to the current item in the array and the WebhookSource you're enumerating over.

To do this, you'll need to reference the pipeline you created earlier and pass the current item in the array as the FilePrefix value. This will allow you to execute the pipeline for each prefix in the array.

Here's a summary of the pipeline parameters:

By following these steps, you'll be able to create an outer loop "runAllFilePrefixes" pipeline that can process multiple file prefixes in a single pipeline.

Pipeline orchestration is the backbone of any data pipeline, and it's essential to understand how it works. You pay for data pipeline orchestration by activity run.

Pipelines are control flows of discrete steps referred to as activities. The integration runtime, which is serverless in Azure and self-hosted in hybrid scenarios, provides the compute resources used to execute the activities in a pipeline.

Orchestration refers to activity runs, trigger executions, and debug runs. Use of the copy activity to egress data out of an Azure datacenter will incur additional network bandwidth charges.

You can orchestrate activities in the cloud or on-premises. Activities running in the cloud, such as copy activities, are charged at $- per activity per month for low frequency and $- per activity per month for high frequency.

Here's a breakdown of the costs for different types of orchestration:

The integration runtime charges are prorated by the minute and rounded up. This means that if your pipeline runs for 1 hour and 5 minutes, you'll be charged for 1 hour and 1 minute.

You can also execute pipelines within other pipelines using the Execute Pipeline activity. This allows you to create complex workflows and orchestrate multiple pipelines at once.

You can use the Get Metadata activity to fetch files from a dataset, but you need to create a new dataset to know where to fetch the files from.

The Get Metadata activity can be configured to expose a field as an argument to other activities, like the Child Items field. Unfortunately, this option may not always appear in the ADF pipeline editor.

To process multiple files, you can use the ForEach activity, which references the Get Metadata activity by name and uses the output property childItems. This allows you to iterate through each file and perform actions on it.

Here's a step-by-step breakdown of the ForEach activity:

1. Open the file and get the timestamp property.

2. Copy the file to the specified container and folder using the timestamp property to determine the location.

You can use a Lookup activity to open a specific file, which needs a new dataset to know where to pull the file from. This dataset can be parameterized by adding the name of the file and the source of the webhook as input parameters.

To get a specific file to read its contents using the Lookup activity, you can add another activity called Copy data and drag the success path from the Lookup to it.

To configure a pipeline in Azure Data Factory, you'll want to start by setting up the pipeline's input parameters, which can include arrays of file prefixes.

In the 'runAllFilePrefixes' pipeline, the FilePrefixes input parameter is used to define an array of prefixes. This array can be set to a specific value or a dynamic value based on the pipeline's configuration.

A single ForEach activity is then added to the pipeline and its Items property is set to the array of prefixes defined in the FilePrefixes input parameter.

The webhook_dump dataset has two parameters: WebhookSource and FilePrefix. These parameters are used in the Connection tab to make the query more dynamic.

To specify the connection settings for the webhook_dump dataset, simply type in the expression, but be aware that it won't be evaluated if you try to type it in. However, you can tell it's been properly added because the input box will be a little blue with a trashcan icon in the input box to clear it.

This connection setting is crucial in making the query more dynamic, and it's essential to get it right to ensure the pipeline runs smoothly.

Data Factory Data Flows are visually-designed components that enable data transformations at scale. You pay for the Data Flow cluster execution and debugging time per vCore-hour.

The minimum cluster size to run a Data Flow is 8 vCores. Execution and debugging charges are prorated by the minute and rounded up.

Change Data Capture artifacts are billed at General Purpose rates for 4-vCore clusters during public preview of CDC. The same Data Flow Reserved Instance pricing discount also applies to CDC resources.

Data Factory Data Flows will also bill for the managed disk and blob storage required for Data Flow execution and debugging.

Here are the Data Flow Execution and Debugging prices:

Azure Data Factory offers a range of services to help you manage and process your data.

Azure Data Factory V2 includes read/write operations, which allow you to create, read, update, and delete entities such as datasets, linked services, pipelines, integration runtime, and triggers.

These operations are essential for managing your data factory, and understanding how they work can help you optimize your data processing pipeline.

You can find more information on Azure Data Factory V2 features and capabilities, and learn more about how to plan and manage costs for Azure Data Factory.

Here are some key services offered by Azure Data Factory:

Azure Data Factory V2 also includes monitoring operations, which allow you to get and list pipeline, activity, trigger, and debug runs. This can help you track the performance of your data factory and identify areas for improvement.

Azure provides a range of services, including Azure Data Factory, which enables you to create data pipelines and integrate data from various sources.

Azure Data Factory is a cloud-based service that allows you to create, schedule, and manage data pipelines. You can use Azure Data Factory to integrate data from different sources, including on-premises data sources, and transform it into a format that's suitable for analysis or reporting.

Azure Data Factory has two versions: V1 and V2. V2 provides more features and capabilities than V1, including support for more data sources and improved performance.

In Azure Data Factory V2, read/write operations include create, read, update, and delete entities, such as datasets, linked services, pipelines, integration runtime, and triggers. Monitoring operations include get and list for pipeline, activity, trigger, and debug runs.

A pipeline in Azure Data Factory is a logical grouping of activities, and it can be active for a user-specified period of time. If you're running a pipeline that uses other Azure services, such as HDInsight, you'll be billed separately for those services.

Data movement charges in Azure Data Factory are prorated by the minute and rounded up. For example, if a data copy takes 41 minutes and 23 seconds, you'll be charged for 42 minutes. You may also incur data transfer charges, which will show up as a separate outbound data transfer line item on your bill.

Here's a summary of the costs associated with Azure Data Factory:

Azure Data Factory charges are incurred after one month of zero runs for inactive pipelines. You can find pricing examples on the Azure Data Factory Documentation page and guidance on how to plan and manage ADF costs on the Azure Data Factory documentation page.

Azure Data Factory has a Service Level Agreement (SLA) that ensures a high level of data integration and processing reliability.

The SLA guarantees that Azure Data Factory will be available at least 99.9% of the time, measured over a calendar month.

Data Factory's SLA also includes a 10-minute limit on the duration of any unavailability, ensuring that your data pipelines are up and running quickly.

In the event of an outage, Azure Data Factory will provide a notification and a detailed explanation of the cause within 24 hours.

Enterprise integration is a vital aspect of any organization's data strategy. With Azure Data Factory, you can simplify hybrid data integration at an enterprise scale.

Azure Data Factory provides a data integration and transformation layer that works across your digital transformation initiatives, empowering citizen integrators and data engineers to drive business and IT-led analytics/BI. This service takes care of code generation and maintenance, allowing you to transform faster with intelligent intent-driven mapping that automates copy activities.

You can gain up to 88% cost savings with Azure Hybrid Benefit, making it an attractive option for organizations looking to modernize SQL Server Integration Services (SSIS). With Data Factory, you can enjoy the only fully compatible data integration service that makes it easy to move all your SSIS packages to the cloud.

Ingesting data from diverse and multiple sources can be expensive and time-consuming, but Azure Data Factory offers a single, pay-as-you-go service. You can choose from more than 90 built-in connectors to acquire data from various sources, including big data sources, enterprise data warehouses, and software as a service (SaaS) apps.

Here are some of the key benefits of using Azure Data Factory for enterprise integration:

By leveraging Azure Data Factory, you can achieve your vision for hybrid big data and data warehousing initiatives, and unlock transformational insights into your organization's data landscape.

Setting up your Azure Data Factory pipeline requires careful consideration of data storage and management. Storing transformed data in Azure Data Lake Storage Gen2 is a common practice, and it's essential to handle errors and exceptions during the write operation.

Best practices for storing transformed data include treating the first row as headers and specifying options for overwriting existing data. It's also crucial to ensure proper access controls and permissions are set up for the Azure Data Lake Storage Gen2 account to restrict unauthorized access.

Partitioning large datasets into multiple files can improve performance, but this should be considered on a case-by-case basis. The size of the data will dictate the best approach for partitioning.

Here are some key considerations for setting up and storing your data: