Getting Started with Azure Data Factory

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Adding machine learning to business processes has become a critical need for many companies who are trying to compete in the marketplace. This can be achieved through data factories which allow enterprises to dynamically build and deploy their own models, while keeping all of their existing code, tools and infrastructure intact.

The “azure data factory tutorial for beginners pdf” is a guide that can be used to get started with Azure Data Factory. The guide will teach you how to use the tool and how to create your first data pipeline.

Getting Started with Azure Data Factory

Data is the new oil these days, but how does this influence a company moving to the cloud? or what happens when you move your data from several storage systems to the cloud, which is raw and disorganized? Thankfully, Microsoft Azure Data Factory has provided answers to these concerns.

Microsoft Azure Data Factory provides data integration and extract-transform-load (ETL) capabilities. These services enable you to create data pipelines, which are data-driven processes.

Sounds appealing? Let’s take a closer look at how fantastic Azure Data Factory is!

Prerequisites

Make sure you have an Azure subscription if you want to follow along with the step-by-step instruction. You can create a free Azure account if you don’t already have one.

What exactly is the Azure Data Factory?

Azure Data Factory is a cloud-based data integration tool that orchestrates data migration and transformation across various data repositories and computational resources.

Copying data to and from multiple Software-as-a-Service (SaaS) apps, on-premises data storage, and cloud data stores is one of the most fascinating tasks you can do with Azure Data Factory. Additionally, you may convert file types when copying.

After collecting the data, you work on the transformation side, which is termed Data Flows in the Integration Runtime (IR), and you take the ETL process seriously.

Creating a Data Factory in Azure

Because an introduction isn’t enough to grasp how Azure Data Factory works, you’ll build your own Azure Data Factory!

1. Launch your preferred web browser and go to the Azure Portal.

2. Select All services from the menu panel by clicking on the gateway menu icon in the top-left corner of the page. This allows you to see all of the services that are accessible to you.

All Services Are Available All Services Are Available

3. Click on Analytics —> Data factories to view the overview page where you’ll see all of your previous data factories’ resources.

Data Factories Access Data Factories Access

4. To create a new data factory resource, click Create to launch the assistant.

Creating an Azure Data Factory resource is the first step. Creating an Azure Data Factory resource is the first step.

5. To add additional resources, choose the subscription and resource group. If you’d rather create a new resource group, click the Create new link, give it a name, and then click OK.

Creating a New Resource Group for Azure Data Factory Deployment Creating a New Resource Group for Azure Data Factory Deployment

6. Now, add the following to the instance details:

  • A area in which to develop the resource, usually the one nearest to you.
  • A name for the resource that you like.
  • To access the most recent features and enhancements, make sure the version is V2 (recommended by default).
  • To continue the construction procedure, click the Next: Git configuration button.

Defining the Details of the Instance Defining the Details of the Instance

7. Uncheck the option Configure Git later since you’re simply setting up the instance right now. To enter the Networking setup tab, click Next: Networking.

How to Get Started with Git Bash on Windows (Related)

Git setup is being postponed. Git setup is being postponed.

8. To complete the configuration procedures, click Review + Create. The setting is set to Public endpoint by default, which is OK.

If you don’t want to utilize the Microsoft-managed key to encrypt your data, you’ll need to use the Advanced settings page. When you use tags to identify resources inside a project, for example, the Tags config tab is necessary. These tabs are not required for this demonstration.

Configuration of the Network Configuration of the Network

9. When all of the final validations pass, click the Create button to tell Azure to start building the Azure Data Factory instance.

Validating Data and Creating an Azure Data Factory Validating Data and Creating an Azure Data Factory

Creating a Storage Account in Azure Data Lake

You may now begin copying data using your Azure Data Factory. But first, build the Azure Data Lake Storage Account into which the Azure Data Factory will transfer data.

1. Hover your cursor over the Storage accounts button on the Azure Portal, and then click Create on the pop-up box that displays, as shown below. The website will then be sent to the “Storage Account” page.

Using the Azure Portal to create a Storage Account Using the Azure Portal to create a Storage Account

2. Under the Basics tab, choose the same resource group and region you selected in the “Creating a Data Factory in Azure” section (step six).

Setting up a Storage Account Resource Group and Region Setting up a Storage Account Resource Group and Region

3. Give your storage account a distinctive name, such as demodfstorageaccount in our example. To keep expenses reasonable for these examples, choose Standard performance and alter redundancy to Locally-redundant storage (LRS). To go to the Advanced tab, click Next: Advanced.

Defining the Storage Account's Name and Redundancy Defining the Storage Account’s Name and Redundancy

4. On the Advanced tab, click the enable hierarchical namespace option under Data Lake Storage Gen2. When you choose this option, your storage account becomes a data lake rather than a conventional blob storage account.

To examine your storage account options, click Review + Create.

The Storage Account's Hierarchical Namespace is enabled. The Storage Account’s Hierarchical Namespace is enabled.

5. To complete the establishment of your Azure Data Lake Storage Account, click the Create button after the validation:

Validating and finalizing the creation of a storage account Validating and finalizing the creation of a storage account

Now that you have an active storage account, all you have to do is test it to see whether it works. In your Azure Data Lake Storage Account, transfer an example CSV file into a blob container named weather.

The National Centers for Environmental Information / National Oceanic and Atmospheric Administration provided the datasets for this demonstration (NCDD-NOAA). They also provide a free sample database for the Global Historical Climate Network’s daily summaries, which you may download as CSV files.

1. Go to the Azure Data Factory home page in your browser, then click Ingest to access the Copy Data Tool, where you’ll start a copy job.

Using the Copy Data Tool Using the Copy Data Tool

2. Select the Built-in copy task option on the Properties tab to build a single pipeline. Click Next while keeping the default Run once now option selected for the task schedule.

When you choose Run once now, the copy job is started right after you put it up.

Choosing the Built-in Copy Option Choosing the Built-in Copy Option

3. Select HTTP from the Source type selection on the Source data store page to utilize the specified dataset for this example. To specify the source URL, click New connection.

Creating a New Source Data Store Connection Creating a New Source Data Store Connection

Set up the new connection by doing the following:

  • Give the link a name (the definition of the data source)
  • For this demonstration, copy and paste the following dataset URL into the Base URL field: https://www1.ncdc.noaa.gov/pub/data/cdo/samples/GHCND sample csv.csv
  • Make Anonymous the authentication type.
  • Leave the other defaults alone and click

Setting Up a New Connection Setting Up a New Connection

5. Click Next since there isn’t anything you need to update or specify for the source data storage in this sample.

Setting up the source data storage Setting up the source data storage

6. Select whether the first row should be considered a header by checking the First row as header option. Because the origin’s dataset is in CSV format, set the Column delimiter to Comma (,).

To see the example dataset given in the following step, click Preview data.

Page with file format options Page with file format options

7. View the contents of the dataset by scrolling through it, then shut the window and click Next.

a sample of the dataset's data a sample of the dataset’s data

8. Select your previously established storage account (Azure Data Lake Storage Gen2) from the Target type selection on the Destination data store page.

To open the assistant to make a new connection in the following step, click the New connection button.

Setting Up the Destination Data Store Setting Up the Destination Data Store

9. Set up the new connection by doing the following:

  • In the Name area, provide your desired name for the new connection.
  • Click on the Storage account name dropdown and choose the storage account you previously created (demodfstorageaccount) — “Creating a Storage Account in Azure Data Lake” section (step three).
  • To make a new connection, click Create.

Setting Up a New Azure Data Lake Storage Connection Setting Up a New Azure Data Lake Storage Connection

Enter your selected Folder path and File name now. However, for this demonstration, the dataset GHCN-daily will be moved to the weather folder.

Click Next after keeping the other default selections.

Defining the path to the destination folder and the file name Defining the path to the destination folder and the file name

11. In the Page with file format options, confirm the File format is set to Text format and the Column delimiter is set to Comma (,), then click Next.

Because you’re establishing a copy task for a CSV file, the delimiter must be set to Comma (,).

Page with file format options Page with file format options

12. Make the following changes to the Settings page:

  • In the Task name area, provide a pipeline name.
  • Change the tolerance for faults to To prevent issues, skip incompatible rows and tick the Enable logging option to preserve logs.
  • To preserve the logs, set the Folder path to weather/errors (these folders will be created).
  • To examine the general parameters of the copy job you’re generating, go to the next stage and click Next.

Pipeline Configuration for the Deployment Process Pipeline Configuration for the Deployment Process

13. Review the general parameters for the copy operation on the Summary screen, then click Next to begin the deployment process.

Data is being ingested from an HTTP source to an Azure Data Lake Storage Gen2 target, as seen below. The pipeline runs instantly since you choose the “run once now” option in step two.

Summary of the Copy Data Tool Process to View Summary of the Copy Data Tool Process to View

14. Once the deployment is complete, go to the “Monitor” page and click the Monitor button to see the execution.

To access the Monitor page, click the Monitor button. To access the Monitor page, click the Monitor button.

You’ll be taken to the Pipeline runs page after initiating the Deployment and selecting Monitor, as seen below.

Deployment was successful. succeeded Deployment was successful. succeeded

15. Finally, open Azure Storage Explorer to examine your freshly relocated data, as seen below.

In Azure Storage Explorer, you can see newly moved data. In Azure Storage Explorer, you can see newly moved data.

Conclusion

You learnt how to use the Copy Data Tool to download and load a CSV file from your demo datasets into your Azure data lake storage account in this article. Microsoft Azure Data Factory is a fantastic solution for quickly and effectively storing data in the cloud.

What comes next? Why not look at other data sources and datasets? Alternatively, you might use the file format options to change your data and save it to a new destination data repository.

The “azure data factory pipeline tutorial” is a guide that will help you get started with Azure Data Factory. It includes step-by-step instructions on how to set up your first pipeline and run it in production.

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