Azure Data Factory – Key to migrate data in Azure Cloud

Posted by Madhavan on December 8th, 2021

Introduction

Data sources ingest data in different sizes and shapes across on-premises and in the cloud, including product data, historical customer behaviour data, and user data. Enterprise could store these data in data storage services like Azure Blob store, an on-premises SQL Server, Azure SQL Database, and many more.

This blog will highlight how users can define pipelines to migrate the unstructured data from different data stores to structured data using the Azure ETL tool, Azure Data Factory.

What is ETL Tool?

Before diving deep into Azure Data Factory, there is a need to know what the ETL tool is all about. ETL stands for Extract, Transform and Load. The ETL Tool will extract the data from different sources, transform them into meaningful data and load them into the destination, say Data warehouses, databases, etc.

To understand the ETL tool in real-time, let us consider management with various departments like HR, CRM, Accounting, Operations, Delivery Managements, and more. Every department will have its datastore of different types. For instance, the CRM department can produce customer information; the Accounting team may keep various books, and their Applications could store transaction information in Databases. The organization needs to transform these data into meaningful and analyzable insights for better growth.  Here comes the ETL tool like Azure Data Factory. Using Azure Data Factory, the user will define the data sets, create pipelines to transform the data and map them with various destinations.

What is Azure Data Factory?

As cloud adoption keeps increasing, there is a need for a reliable ETL tool in the cloud with many integrations. Unlike any other ETL tools, Azure Data Factory is a highly scalable, increased agility, and cost-effective solution that provides code-free ETL as a service. Azure Data Factory consists of various components like:

• Pipelines: A pipeline is a logical grouping of activities that performs a unit of work. A single pipeline can perform different actions like ingesting data from the Storage Blob, Query the SQL Database, and more.
• Activities: Activity in a Pipeline represents a unit of work. An Activity is an action like copying a Storage Blob data to a Storage Table or transform JSON data in a Storage Blob into SQL Table records.
• Datasets: Datasets represent data structures within the data stores, which point to the data that the activities need to use as inputs or outputs.
• Triggers: Triggers are a way to execute a pipeline run. Triggers determine when a pipeline execution should start. Currently, Data Factory supports three types of triggers:
  • Schedule Trigger: A trigger that invokes a pipeline at a scheduled time.
  • Tumbling window trigger: A trigger that operates on a periodic interval.
  • Event-based trigger: A trigger that invokes a pipeline when there is an event.
• Integration Runtime: The Integration Runtime (IR) is the compute infrastructure used to provide data integration capabilities like Data Flow, Data Movement, Activity dispatch, and SSIS package execution. There are three types of Integration Runtimes available, they are.
  • Azure
  • Self-hosted
  • Azure SSIS

How Serverless360 enhances the Azure Data Factory Experience?

Azure Data Factory is one of the best ETL tools in the market. It simplifies the data migration process without writing complex algorithms. Though it offers remarkable features, it also has some limitations for operation and support teams in:

1. Lack of Application-level grouping
2. No Consolidated Monitoring

These challenges may arise when there are multiple Azure Data Factories with different pipelines, data sources across various Subscriptions, Regions, and Tenants. Managing all of them using Azure Portal becomes cumbersome. To solve these critical challenges, Enterprise can leverage tools like Serverless360.

Serverless360 is a one platform solution for Operation and Support Teams to efficiently manage and monitor Azure Serverless services.

Serverless360 offers,

1. Application-level of Visibility
2. Consolidated Monitoring
3. Process Automation
4. End to End Tracking
5. Unified Azure Documentation

To know more:

1. Manage Azure Data Factory
2. Manage Pipeline Runs
3. Consolidate Monitoring for Azure Data Factory

Closing

In this blog, we learned why the Azure Data Factory is a key to migrate data across different data stores by creating pipelines and activities.  In our upcoming blogs, we will talk more about Integration Runtimes, Data Flows, etc., Stay tuned to learn more!

Originally published at https://www.serverless360.com/blog/azure-data-factory-key-to-migrate-data-in-azure-cloud