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With the help of the cloud-based data integration tool Azure Data Factory (ADF), you can design data-driven processes for coordinating and automating data transformation and transportation. ADF does not keep any data on its own. You can design data-driven workflows to coordinate data transfer between the supported data stores, process the data utilizing compute services in different locations or process the data locally. You can also use programmatic and UI mechanisms to monitor and manage workflows. This article covers detailed knowledge of Azure Data Factory.
How does Azure Data Factory Work?
You can build data pipelines using the Data Factory service that moves and transforms data and schedules the pipelines to run regularly (hourly, daily, monthly, etc.). As a result, time-sliced data is used and produced by processes.
Step 1: Connect and Collect
Create connections to all required data and processing sources, such as file shares, FTP, SaaS services, and online services.
Then, move the data to the central location for further processing.
Step 2: Transform and Enrich
Data is converted utilizing computing services like HDInsight Hadoop, Spark, Azure Data Lake Analytics, and machine learning once in a centralized data store in the cloud.
Step 3: Publish
Deliver transformed data from your cloud storage sources to on-premise sources like SQL Server or leave it there for BI and analytics tools and other applications to use.
Azure Data Factory Key Components
Azure Data Factory requires the following four crucial elements to describe input and output data, processing events, as well as the timetable and resources required to complete the intended data flow:
Within the Data Storage, Datasets Represent Data Structures
An input dataset represents a pipeline activity’s input. An output dataset represents the output of the activity. In the Azure Blob Storage, for instance, an Azure Blob dataset provides the blob container and folder from which the pipeline should read the data. An Azure SQL Table dataset designates the table to which the activity will write its output data.
A Pipeline is a Group of Activities
They are utilized to combine actions into a unit that works as a whole to complete a task. There may be one or more pipelines in a data factory. For example, a pipeline might have a set of operations that ingest data from an Azure blob and then split it using a Hive query on an HDInsight cluster.
Activities Define The Actions To Perform On The Data
Data transfer and transformation are the two sorts of activities Azure Data Factory offers.
What are the Advantages of Azure Data Factory?
Enterprises interested in migrating their Enterprise Data Warehouses (EDW) and Data Lakes from on-premises to the cloud will also need to consider code migration. The coding is what gives the data context and meaning. Data pipelines and a variety of objects will make up the code. Millions of lines of code and thousands of data pipelines must be transferred with each EDW or Data Lake cloud migration because many organizations have spent years maintaining, extending, and building their code.
Azure Data Factory (ADF) is the best option for migrating these existing ETLs when shifting workloads (data and code) to Microsoft Azure. The autonomous movement and manipulation of data at scale in the cloud is made possible by ADF.
The following are some of the significant advantages of Azure Data Factory:
Easy Migration of ETL Workloads to Cloud
ETL tasks from on-premises EDWs and Data Lakes can be transferred to the Azure cloud. ETL packages can be deployed, executed, and managed using ADF.
Low Learning Curve
The ETL GUIs are similar to the Azure Data Factory GUI. ADF offers a short learning curve for developers already familiar with the other ETL interfaces.
The tool manages all of the drivers needed to integrate with Oracle, MySQL, SQL Server, or other data stores. AWS or GCP can be utilized with this product even though it is an Azure offering.
As a result, Data Factory may be utilized with most databases, any cloud, and various add-on tools, including Databricks. This service processes, transforms and stores large volumes of data. Through ML models, Databricks also enables the exploration of unstructured data (such as sounds and photos).
Accessibility is essential when managing and controlling data. Data mobility is available in more than 25 countries and is secured by Azure security architecture, giving Azure Data Factory a global cloud presence.
The tool enables the creation of roles and grants each one a set of permissions. Contributor, owner, and administrator are the positions.
Data Factory transfers transforms, and regulates data using a sophisticated ETL process. The program is fully automated and aids in effectively orchestrating your data. Therefore, Azure Data Factory performance enables spending the least time configuring the tool, giving you more time to gain insights.
Additionally, Azure is responsible for managing the Data Factory, upgrades, and security patches and ensuring as little downtime as possible. You always get access to the most recent product as a result.
Since the solution is primarily automated, little human labor is needed. One developer must set up the Data Factory by the strategy, and a solution architect is required to plan the data collection process. As a result, working with Data Factory does not require you to hire a sizable workforce.
Best Practices to Implement Data Factory
Set up a Code Repository
You must create a code repository for your big data to get end-to-end development.
Toggle between different environment set-ups
Development, production, and test environments are all combined on a data platform. Varying situations require varying amounts of computing. Therefore, you need diverse data factories to handle the workloads of various contexts.
However, the ‘Switch’ action in Azure Data Factory enables the management of many environment set ups with a single data platform. Each environment has a unique work cluster coupled to a central variable control to switch between several activity paths.
Go for Good Naming Conventions
The significance of establishing appropriate naming conventions for any resource cannot be overstated. You must be aware of the permitted characters while using naming conventions.
Consider Automated Testing
With taking testing into account, the Azure Data Factory implementation is complete. One essential component of CI/CD deployment strategies is automated testing. You should consider automating end-to-end testing on your pipelines and associated repositories in Azure Data Factory. This will make tracking and verifying how each pipeline action is carried out easier.
Azure Data Factory Use Cases
The following are some examples of how Azure Data Factory might be used:
- Supported by using in projects involving advanced analytics data migration.
- Can be used to switch the EEL process from using SQL server integration services to additional data.
- It is a potential method for transferring data from a client’s server or internet data to an Azure Data Lake. To coordinate the flow of data from the source to the target, you establish pipelines.
- It is one of the most well-known ETL tools that may be used to perform a variety of data integration operations.
- In addition to supporting analysis and reporting through Power BI, it can be used to integrate data from various ERP systems and load it into Azure Synapse for reporting.
- The extremely well-integrated ADF tools enable speedy development of ETL, big data, data warehousing, and machine learning solutions with the flexibility to grow and adapt to new or enhanced requirements. You just pay for what you use when using Data Factory. In actuality, pipeline orchestration and execution are what determine pricing for data pipelines.
You only pay with Data Factory for what you require. In reality, pipeline orchestration and execution are what determine data pipeline pricing.
- Pipeline orchestration
- Executing and troubleshooting data flows
- Functions performed by the Data Factory, including pipeline creation and pipeline monitoring
Cloud data and on-premises data may be readily integrated with Azure Data Factory. Any data platform, cloud, and machine learning project must use the tool.
Data Factory offers advantages, including improved security, productivity, and cost-cutting. It is also highly automated and simple to use.