What is Azure Data Factory?

Azure Data Factory is a service that helps streamline and speed up data-driven application lifecycle management. Behind the scenes, this automation tool can work with different database technologies to automatically generate scripts, which can be either batch or streaming jobs.”.

Data-driven applications have become increasingly common as digital transformation drives change across industries. To manage such applications, traditional data warehouse toolsets, such as web services or RDBMS, are becoming increasingly overburdened and complex. In addition, the need for real-time data daily.

Introduction to Azure Data Factory

Azure Data Factory is a cloud-based fully managed serverless data integration service.

• It integrated data sources with more than 90  built in, maintenance free connectors with no additional cost.
• Easily creates ETL and ELT pipelines.
• It allows users to create data processing workflows in the cloud, either through a graphical interface or by writing code, for automating data movement and data transformation.

What is the purpose of a data factory?

Data Factory is data integration, quality, and transformation service for moving data between systems. It is a component of Azure Data Management services.

The Data Factory service enables you to transform and enrich data at scale via a graphical designer, manage all your pipelines in one location through an intuitive UX, and perform complex multi-step workflows with parallelism and looping. Data Factory also supports the ability to schedule jobs, monitor and alert on job status, capture detailed logs and monitor job execution history.

Data Factory Service is built on Azure Storage, HDInsight, Azure SQL Data Warehouse, and Machine Learning Services so that you can use these other services within your Data Factory. For example:

REST API for creating custom data transformation tasks or workflows using .NET SDKs or via other programming language SDKs (Python/Ruby/NodeJS).

Using Azure Functions in Azure Data Factory

The Azure Data Factory solution template requires an owned or shared service connection to the Microsoft SQL Server that contains the destination table. The destination table must exist before you create a pipeline.

Azure SQL Data Warehouse is a comprehensive, fully managed, petabyte-scale big data platform that allows you to run massively parallel processing (MPP) query engine on up to hundreds of terabytes of data stored in Azure blob storage. Azure SQL Data Warehouse supports table and column security, flexible join optimizations, and automated querying. It is built on the same technology that powers Azure SQL Database, so it can be used to manage your data similarly.

Azure HDInsight is a managed service that delivers global scale analytics on highly-available systems running Linux or Windows Server (continuous deployment) at a petabyte-scale. HDInsight scales horizontally with no limit on the number of cores per node.

Evaluate key Azure Data Factory benefits and limitations

Azure Data Factory is an Azure service that makes it easy to create and manage data integration and transformation pipelines. In this article, we will cover how you can use the Azure Data Factory service to move data between systems.

How can you use a data factory? What is the purpose of Azure Data Factory?

Data Factory is a unique combination of services that pulls together several key capabilities.

Data Factory provides a data integration and transformation layer that works across your digital transformation initiatives. Enable citizen integrators and data engineers to drive business and IT led Analytics/BI. Prepare data, construct ETL and ELT processes, and orchestrate and monitor pipelines code-free.

What is the purpose of data refinery service?

The Data Refinery is a data transformation platform to execute ETL workloads powered by Azure Data Factory. It provides a single tool to execute transformational tasks for moving between disparate sources to create one unified dataset enabling faster time-to-insight

What are the best practices to implement the azure data factory?

Here are few basic practices to implement ADF

1. Metadata Driven Ingestion Patterns
2. ADF Ingestion to ADLS Landing Zones and Auto Loader or Directly to Delta Lake.
3. Ingestion using Auto Loader
4. Ingestion directly to Delta Lake
5. Executing an Azure Databricks Job
6. Pools + Job Clusters
7. ADF Managed Identity Authentication

Benefits of Azure Data Factory:

Mostly ADF helps to design and run data transformations in a code-free environment helping companies to focus on business logic.

• Easy to Use: Rehost SQL Server Integration Services (SSIS) in a few clicks and build ETL and ELT pipelines code-free, with built-in Git and CI/CD support.
• Cost Effective: Pay-as-you-go, fully managed serverless cloud service that scales on demand.
• Powerful: Ingest all the on-premises, and software as a service (SaaS) data with more than 90 built-in connectors. Orchestrate and monitor at scale.
• Intelligent: Use autonomous ETL to unlock operational efficiencies and enable citizen integrators.

Trusted Global Presence of Azure Data Factory:

• Data Factory has been certified by HIPAA and HITECH, ISO/IEC 27001, ISO/IEC 27018, and CSA STAR.
• Available n more than 25 regions globally to ensure data compliance, efficiency, and reduced network egress costs.
• Connect securely to Azure data services with managed identity and service principal. Store your credentials with Azure Key Vault.
• Managed virtual network gives you an isolated and highly secure environment to run your data integration pipelines.

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