One of the greatest gifts of our day is the availability of so much data. But, does this impact a company when it is moving into the cloud. You must plan to move to the cloud. Then your traditional on-premise data becomes a hindrance. You can use Azure data factory in that case.
What is a Data Factory in Azure?
Azure Data Factory is a cloud-based data management tool. It lets you create data-driven cloud workflows. This is for data movement and data transformation orchestration and automation.
Azure Data Factory does not themselves store any data. It enables you to create data-driven workflows to orchestrate data movement. This is between data stores and data processing through resources in other regions or in an on-premise environment. This also allows the control and management of workflows. Therefore, it uses both programmatic and UI frameworks.
You could enhance the data produced in the cloud. This is by the use of on-premise reference data or other disparate data sources.
Microsoft Azure has responded to these concerns with a dedicated framework. This allows users to create a workflow that can absorb data from on-site and cloud data stores. Besides, it converts or process data using existing database resources such as Hadoop. Then you can release the findings for business intelligence (BI) applications. This is to access in an on-premise or cloud data store, known as the Azure Data Factory.
How does Data Factory work?
The Data Factory service enables you to create data pipelines. It uses transfer and transforms data. Then run the pipelines on a particular schedule (hourly, regular, weekly, etc.). This means the data consumption and generation are by workflows by time and data. You can define the pipeline mode as scheduled. So, Azure Data Factory CLI works on pipelines.
Azure data pipelines
In Azure Data Factory, the pipelines usually perform the following three steps:
- Link and Collect:
You can link to all the necessary data and processing sources such as SaaS services, web services. Then transfer the data as required to a centralized location. This is for processing using the Copy Operation in a data pipeline to move data for further analysis. This is for both on-premise and cloud data stores to a cloud data centre for centralization.
- Transform and Enrich:
You can transform once data is present in the cloud's central data store. You may use computing tools like the following. HDInsight Hadoop, Spark, Data Lake Analytics, and Machine Learning are some tools.
- Publish:
Transfer transformed data from the cloud to on-premise sources. The sources such as SQL Server or hold it for use through BI and analytics software. Besides, there are other applications in your cloud storage.
Data transfer activities with Data Factory
Data migration happens between two cloud data stores. This is between an on-premise data store and a cloud data store by using Data Factory.
Copy operation
Copy Operation in Data Factory transfers data from a data store of source data into a data store of sinks. Azure supports various data stores such as following
- Blob storage
- Cosmos Database (DocumentDB API),
- Data Lake Server,
- Oracle, Cassandra, and so on.
Transformation:
Azure Data Factory supports transformation activities. You can add this either to the pipelines or combined with other activities. Turn data in Azure Data Factory.
If you want to move data to or from a data store that does not allow Copy Activity. You can use your own logic to copy/transfer data using a. Net custom activity in Data Factory.
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Key components of Data Factory
Data Factory components have four components that function together to identify the following.
- input and output datasets,
- processing events, and
- schedule and
- Resources needed to conduct the data flow desired.
How the components work together?
The following shows us the components of the Dataset, Event, Pipeline, link services.
- Data sets represent data structures in data stores. An input dataset represents the input for pipeline operation. An output data collection for the operation represents the output. For example, an Azure Blob dataset specifies the Azure Blob Storage blob container. The folder from which you read the data out by the pipeline. A dataset from Azure SQL Server defines the table to which the operation writes the output data.
- The pipeline is an Action Group. You may use it to group activities into one unit that performs a task together. One or more pipelines may be on a data factory. A pipeline can include, for example, a group of activities that ingest data from an Azure blob. Then run a Hive query to partition the data on an HDInsight cluster.
- Activities describe the activities you want your data to perform. Data Factory currently supports two types of activities: transferring data and transforming data.
- Linked services describe the information needed to connect Data Factory to outside resources. For example, a service associated with Storage specifies a link to connect to the Azure.
Supported regions
You can currently create data factories in the eastern US, US 2, western European regions. In other Azure regions, a data factory can access data stores and computing resources. This is to move data between data stores or process data using computer services.
It helps you to create data-driven workflows to orchestrate data movement. This is between supported data stores. Besides, it processes data using computing resources in other regions or in an on-site. It also allows you to check and manage workflows using mechanisms. These may be both programmatic and UI.
While Data Factory is only available in the Eastern U.S, U.S. 2, and Western Europe areas. The Data Factory data transfer service is available in many areas worldwide. The data store is behind a firewall. Then you can transfer data by a self-hosted Integration Runtime. This is an available on-premises system.
Azure Data factory Versions:
There are computing environments like the Azure HDInsight cluster and Azure Machine Learning. You may operate this from the region of Western Europe. It can build and use an Azure Data Factory instance in East US or US 2. Then you may use it to schedule jobs in Western Europe in your computing environments. It takes Data Factory a few milliseconds to activate the job on your computing system. But the time it takes to run the job in your computing environment doesn't alter.
The service is currently available in two versions: version 1 (V1) and version 2 (V2).
Azure Data Factory Version 1
Data Factory version 1 (V1) service lets you build data pipelines that transfer and transform data. Then run the pipelines on a specified schedule (hourly, monthly, weekly, etc.). It also provides rich visualizations to show history and dependencies. This is between your data pipelines and tracks your data pipelines. Therefore, it is a single unified view to identify issues and warnings to setup control.
Azure Data Factory Version 2
You may base Data Factory version 2 (V2) on the V1. This embraces a wide range of data integration scenarios in the cloud. Data Factory's additional features include control flow.
Control Flow:
The control flow in the Azure Data factory goes through the following steps.
- Deploy branching, looping, and conditional loading. Then run SQL Server Integration Services (SSIS) packages in Azure.
- It goes through Virtual Networking Technology (VNET) environments.
- You need to scale-out with power to process on request.
- Spark cluster assistance on-demand.
- Flexible scheduling to accommodate tons of incremental results.
- Executing data pipelines causes.
Conclusion:
I hope you reach to the conclusion about the Azure Data factory. You can learn more from Industry trained experts from Azure Online training.