**Introduction to Azure Data Factory:** Data generated by several applications of products is increasing exponentially day by day. As the data is coming from many sources, it is very difficult to manage it. **To analyze and store all this data...
Introduction to Azure Data Factory:
Data generated by several applications of products is increasing exponentially day by day. As the data is coming from many sources, it is very difficult to manage it.
To analyze and store all this data, we can use Data Factory which:
1.Stores data with the help of Azure Data Lake Storage 2.Analyzes the data 3.Transforms the data with the help of pipelines (a logical grouping of activities that together perform a task) 4.Publishes the organized data 5.Visualizes the data with third-party applications like Apache Spark or Hadoop.
Azure Data Factory is a cloud-based data integration service that allows you to create data-driven workflows in the cloud for orchestrating and automating data movement and data transformation. We can make use of Azure Data Factory to create and schedule data-driven workflows that can ingest data from various data stores. It can process and transform the data by using compute services such as Azure Data Lake Analytics, Azure Machine Learning, and Azure HDInsight Hadoop. We can publish the output data to data stores such as Azure Data Lake for Business Intelligence(BI) applications to perform visualisation or analytics. For better business decisions, we can organise the raw data into meaningful data stores. Interested in learning Azure Data Factory? Click here to learn more in this Azure Certification Course
Flow Process of Data Factory
In this Azure Data Factory Tutorial, now we will discuss the working process of Azure Data Factory. The Data Factory service allows us to create pipelines which helps us to move and transform data and then run the pipelines on a specified schedule which can be daily, hourly or weekly. The data that is consumed and produced by workflows is time-sliced data, and we can specify the pipeline mode as scheduled or one time.
*Input dataset: *
It is the data we have within our data store, which needs to be processed and then passed through a pipeline.
Pipeline operates on data to transform it. Data transformation could be anything like data movement. Data transformation is possible with the help of USQL, stored procedures, or Hive. If you have any doubts or queries related to Azure, do post on Azure Community.
*Output dataset: *
It will contain data that is in a structured form because it is already been transformed and made structured in the pipeline storage. Then, it is given to linked services like Azure Data Lake, blob storage, or SQL.
These store information that is very important when it comes to connecting an external source. For example, consider the SQL server. We need a connection string to connect to an external device. We need to mention the source and the destination of our data.
The Gateway connects our on-premises data to the cloud. We need a client installed on our on-premises system so that we can connect to the Azure cloud.
Our data can be analyzed and visualized with much different analytical software like Apache Spark, R, Hadoop, and so on.
In the article, we will go to the next step to create a subscription and use webhook event handlers to view those logs in our Azure web application.
How to create, build, deploy and configure an Azure Function using Azure DevOps, Azure CLI and Powershell.
In this video, see how to configure your Azure SQL connectivity leveraging the Azure CLI and PowerShell notebooks in Azure Data Studio. For the full Azure SQ...
Azure Private Link is a secure and scalable way for Azure customers to consume Azure Services like Azure Storage or SQL, Microsoft Partner Services or their own services privately from their Azure Virtual Network (VNet). The technology is based on...
We can configure the Azure Advisor Recommendation rules for Azure Virtual Machine. Azure Advisor allows us to configure for Right-size or shutdown low usage virtual machines. Azure Advisor analyzes the recent usage patterns of your virtual machines and uses the configured rules in the Azure Advisor to identify low usage virtual machines.