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Microsoft Data Factory is essential for data migration on Azure Cloud –

Data information consumes data of many dimensions and forms, including product information, previous customer behavior, and user data, both locally and on the cloud. Enterprises may keep these details in various data storage systems, including an internal SQL server, a platform Azure SQL, and an Azure Blob store.

Using the Azure ETL product Microsoft Data Factory, users may construct pipelines to convert unorganized information from various data storage relating data sets. This article can be useful for programmers, who are looking for step-by-step instructions on how to migrate data on Azure Cloud. Also, it will be beneficial for business owners, who want to understand the data migration process more in-depth and improve their requirements specification document when they hire Azure developers.

Table of Contents –

  1. ETL tool definition? 
  2. What is Microsoft Data Factory? 
  3. Data migration using a real-time scenario 
  4. Data migration via CSV into Azure SQL Database steps:
  • Set up the activity’s source 
  • Set up the activity’s destination
  • Table Properties from CSV Properties
  1. How can Serverless360 improve the experience with Microsoft Data Factory? 

ETL tool definition

Understanding ETL software is necessary before delving deeply into Microsoft Data Factory. Extracted, modified, and loaded is referred to as ETL. The data will be removed from many sources, transformed into valuable data, and loaded into the target, such as databases, data stores, etc., via ETL software.

To comprehend the ETL tool in real-time, let’s examine management with multiple agencies like HR, Marketing, Finance, Operations, Supply Supervisors, and more. Each department will have a different type of data store. For example, the Financial department may maintain multiple books, and the CRM division might produce customer information, while their Programs may store large transaction amounts of data. 

For tremendous success, the company must turn the information into insightful and actionable insights. ETL tools similar to Microsoft Azure Factory are now available. This person will design data sets, build pipelines to convert information, and route them to various destinations using Microsoft Data Factory.

What is Microsoft Data Factory? 

A trustworthy ETL solution within a cloud with multiple integrations is required as cloud use grows. In contrast to previous ETL instruments and Microsoft, Azure Factory offers ETL as a provider with no coding while being highly scalable, more agile, and affordable. The many parts of Microsoft Data Factory include:

Pipelines: Pipelines are thinking skills of tasks that carry out a single charge. One Pipeline can transport out a variety of functions, including querying SQL databases and consuming information from the Storage Blob.

Activities: A Pipeline activity serves as a group of tasks. An “activity” is a task, such as moving data from a blob of storage to a storing table or converting JSON data from the lump of storage records for a SQL table.

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 Datasets: The information required by processes as inputs and outputs are pointed to by datasets, which are simulations of data types found in data stores.

Triggers: You may manage a pipeline by using triggers. Triggers define a pipeline when the action starts. Data Factory currently provides three different trigger types:

Trigger for Schedule: A trigger that activates at a particular pipeline time is known as a schedule trigger.

Window-tilting trigger: trigger that runs on a regular interval is known as a “tumbling window trigger.”

Trigger based on events: trigger based on events activates a pipeline whenever an event occurs.

Runtime for Integration: The computational architecture called Runtime for Integration (IR) enables data integration features, including Data Flow, Packet Forwarding, Activity transmission, and Update package performance. Integration Runtimes come in three different varieties.

            * Azur

           * Own-hosted

            *Microsoft SSIS

Data migration using an actual situation –

Recognize how Developers ought to create a method to automate and analyze the CSV document produced by the CRM software and move it to a central location, such as Microsoft SQL Database. 

Over 1000 client records’ unstructured data is contained in a CSV file utilizing an outliner. These data should be successfully sent to the central repository, Microsoft SQL Database. The Microsoft Data Factory is now here. It enables the creation of the Pipeline that transfers the customer’s information from a CSV file to a Customer Details Column in a Microsoft SQL Database.

Data migration via CSV into Microsoft SQL Database steps:

  • Switch to the Microsoft Information Factory Editor after creating a Microsoft Data Factory.
  • To establish a pipeline for Microsoft Data Factory, click the Editor link on the Page of the Editor to Add button.
  • As indicated below, give the Pipeline’s name (Migrate Customer Details).

Set up the activity’s source –

  • Drag the Access task into the planner after expanding the Move & Change branch from the left menu.
  • Give identify the exercise (Copy data from Blob)
  • Now must choose the Source tab, then click +New to bring up a blade where you can select a data source. Click to select Microsoft Azure Blob, as that is a data source.
  • Choose CSV File from Format Menu, then enter the file name and the OK button to store the information source.

Set up the activity’s destination –

  • Select the Sink tab now, then click +New to see a blade where you can select the destination. The Microsoft SQL Database option. Your final destination and press
  • Click Add fresh in the Connected Services section and enter the network data for the SQL Database on Azure. Saving the location, click OK.
  • Afterward, enter the Column Identify and click Next.

Table Properties from Properties CSV –

  • To have the CSV document automatically recognized and the CSV attributes mapped in Table column characteristics, select “Mapping” and then click the Button to Import Schemas.
  • In addition, it is feasible to modify the translations manually as indicated below, whether any need to be corrected.

After There is mapping. Complete, select Debug. Launch the run of the Pipeline and start transferring using the CSV files in the Table. Verify the Table in a SQL database after the Pipeline Run successfully ensures the records were correctly migrated.

The situation described above demonstrates the effectiveness of using Microsoft Data Factory. The user has many quickly moving data from various stores without writing a single word of code. Microsoft Services, Information Bricks, Deep Learning, and many more events are available through Microsoft Data Factory. The user can automate this Pipeline operation using the three different trigger kinds explained in the previous section.

How can Serverless360 improve the experience with Microsoft Data Factory?

Among the top ETL tools on the market is Azure Data Factory. Without creating complicated algorithms, it streamlines the procedure for migrating data. Although it has many attractive characteristics, Moreover, it a few restrictions for administration and field teams in the following areas:

  • No grouping at the application level
  • Absence of Integrated Monitoring

The presence of several Microsoft Data Factories having various pipelines and information sources spread over many subscription fees, countries, and tenants could present these difficulties. It is challenging to manage all Microsoft Portal usage. The enterprise may use something like Serverless360 to address these pressing issues.

Conclusion  

By building pipelines and activities, Microsoft Data Factory is essential for data migration across various data stores. It will go into more detail on Connectivity Code segments, Information Flow, etc.

 Author Bio

Meravath Raju is a Digital Marketer, and a passionate writer, who is working with MindMajix, a top global online training provider. He also holds in-depth knowledge of IT and demanding technologies such as Business Intelligence, Salesforce, Cybersecurity, Software Testing, QA, Data analytics, Project Management and ERP tools, etc.

 

 

 

 

 

 

 

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