Azure Data Factory VS Data Bricks

What is Azure Data Factory?

Azure Data Factory is an enterprise-grade data pipeline service that enables you to orchestrate and automate the movement of your structured and unstructured data. It provides a graphical user interface that you can use to create data pipelines; specify connections, transformations, and actions; monitor progress; and manage your processes.

Azure Data Factory (ADF) is a cloud-based tool that allows you to orchestrate data movement, transformation, and load processes. It’s designed to be used for both ETL (extract, transform and load) operations as well as ELT (extract, load and transform). In this post we will focus on the first use case: moving data from one place to another.

Azure Data Factory is a service that automates data movement and transformation processes. It provides powerful capabilities for managing data movement pipelines, including the ability to orchestrate complex workflows. You can use it to automate tasks such as ETL and ELT, extract data from various sources (such as SQL databases or Hadoop), transform it, and load it into another system (such as Azure SQL Database). Data Factory is one of the several ways to perform ETL (Extract, Transform, and Load) operations in Azure.

It’s an enterprise-grade data integration service that helps you connect to any data source (such as SQL Server, Oracle, or MongoDB), transform it into another format, then load it into a target like Azure SQL Database or HDInsight. Data Factory is a managed service that helps you transform and move data between different cloud services, on-premises systems, and business applications.

 It is a fully managed service that allows you to create and manage data pipelines that can be used to extract, transform, load (ETL), or stream data from one system into another. Data Factory also provides capabilities for managing your stateful services such as SQL databases or Apache Spark clusters.

Data Factory is part of Azure Databricks, a new product announced by Microsoft at Ignite 2018. You can use Data Factory to create, manage and run data pipelines. You can also use it to create, manage and run stateful services such as SQL databases or Apache Spark clusters.

Data Factory is available as a standalone service and also as part of Azure Databricks. As a standalone service, Data Factory can be used to create data pipelines that are managed by Microsoft. You can use these pipelines to move data between different cloud services, on-premises systems, and business applications.


If you want to use Azure Data Factory, first create an account. Then, sign in to Azure and access the Azure portal at In the left pane of the portal, click New > Data + Analytics > Data Factory.

  1. Create a data factory
  2. Create a pipeline in the data factory
  3. Run the pipeline from the Azure portal or command line

To create a data pipeline, you first need to create a linked service in the Azure portal. This is an entry point for your data, such as a SQL database that you want to access from Data Factory. Next, create a pipeline using the Azure portal or through the command line interface (CLI) tool. You can then use this pipeline to import and transform your data before moving it into another system.


Azure Databricks

DataBricks is an open-source data processing platform for developers and data professionals, allowing them to focus on business problems and not on the underlying infrastructure. A DataBricks application starts with a simple AWS Lambda function that defines the computation that needs to be done. Next, the user adds metadata describing the input sources, parameters, and outputs to generate the streaming pipeline that performs its job. In other words, with DataBricks you import your existing data flows into our platform, we apply transformations (map) using existing libraries or all our math & machine learning capabilities and we export it back out to any destination of your choosing.

DataBricks is an advanced data analytics platform that provides the best way to manage your data science projects. DATABRICKS allows you to build and share data science projects with your team, track work in progress, monitor costs, and more. DataBricks is an open source serverless data platform, built on Apache Spark, with built-in support for machine learning and analytics.

DataBricks provides a unified view of your data across all stages of the ETL pipeline and offers a range of tools to build data pipelines and perform analysis. DataBricks is a fully managed Hadoop, Spark, and HDFS service. It offers an on-premises solution that you can either run yourself or let DataBricks manage for you.

It is a powerful cloud-based platform that helps you connect, manage and analyze any data source on your terms. It’s built on the principle of simplicity, which means it eliminates all the complexity associated with managing data at scale. With Databricks, you can focus on what matters most: solving problems quickly through easy-to-use tools and integrations. is a free, open-source tool that helps you build pipelines. It’s available for Windows, macOS, and Linux.


Let’s take a look at how to use Databricks to help you manage and analyze your data. We’ll start by looking at how it works, then move on to how you can get started with your own account.

You can install it directly from the Apache Software Foundation (ASF). Alternatively, you can get a Docker image or download the latest binaries from here. Once you’ve installed DataBricks, simply use this command to start your cluster:

To get started with Databricks, you can sign up for an account on their website. Once you’re logged in, you’ll be presented with a dashboard that shows your projects and datasets. You can create new projects or import existing ones from AWS S3 or Google Cloud Storage.

You can use Databricks to analyze your data in a variety of ways. You can use it as an ETL tool to move data from one system to another, or you can use its SQL engine to create reports and perform ad-hoc analytics. If you need help building custom applications that integrate with the rest of your infrastructure, then DataBricks is probably not for you.


Azure Data Factory is Microsoft’s take on cloud-based ETL and analytics. Like Databricks, it uses Apache Spark as its foundation. It also supports many of the same file formats that you can use in Databricks, such as CSV and JSON.

However, Azure Data Factory doesn’t support SQL queries; instead, it relies on a drag-and-drop interface to create pipelines that allow you to move data between different systems.

Azure Data Factory is an open-source tool from Microsoft that lets you build data pipelines. It’s a little more complex than Databricks, but it also has more features.

Azure Data Factory lets you create jobs that move data between different systems, perform transformations on the data and send it to various destinations. It comes with built-in connectors for different cloud services as well as on-premise databases and applications.

Azure Data Factory and Databricks both offer powerful tools for streamlining your data processes.

Both are cloud-based services that allow you to set up pipelines that automate tasks such as ETL, data cleansing, and analytics.

The two platforms differ in their pricing structure and capabilities.

Azure Data Factory is Microsoft’s take on a data pipeline. It can do most of what Databricks can, with the added benefit of being integrated into your Azure infrastructure.

If you already have a lot of Microsoft products in use at your company, then it may be easier to integrate Data Factory into your existing systems than it would be to switch over to Databricks.

Azure Data Factory is Microsoft’s take on a data integration tool, and it has a lot of similarities to Databricks. Both products provide an ETL engine that allows you to move data from one place to another without writing custom scripts or code.

They also both integrate with other services in the cloud ecosystem, including Azure SQL Database, Azure Machine Learning, and Power BI.

While Databricks is a great tool for performing ad-hoc analytics and creating custom applications, it’s not the best choice for enterprise data management. This is because DataBricks doesn’t have many of the features that are required to manage large datasets at scale, like security or governance tools. If you need to integrate your data warehouse with other systems in your environment then it makes sense to use Azure Data Factory instead.


  • Azure Data Factory is a fully managed data integration service that allows you to automate the movement and processing of data. It’s designed to help enterprises manage their data lake, while also giving them the ability to leverage Azure services such as Machine Learning and Power BI. Azure Data Factory is a cloud-based data integration tool that can be used to move, transform and analyze data.
  • It supports many different types of workloads and has the ability to integrate with other services in the Azure ecosystem, including Azure SQL Database, Azure Machine Learning and Power BI.
  • Azure Data Factory enables you to perform complex workflows on your data by using pipelines which can be configured using code or through a GUI tool.
  • EASY MIGRATION OF ETL WORKLOADS TO CLOUD :Azure Data Factory allows you to migrate your existing ETL workloads to the cloud. It supports a wide range of data sources and destinations including Azure SQL Database, Azure Blob storage, HDFS, Oracle and Amazon S3.
  • Azure Data Factory can be used to migrate existing ETL solutions from on-premises servers or other cloud services such as Amazon Redshift into Azure. This can be done by using the Copy Activity, which copies data from one or more source locations to a destination location in an Azure Data Lake Store account.
  • LOW LEARNING CURVE : Azure Data Factory is easy to learn and use. You can get started with just a few clicks, without any coding or scripting required. It has a simple user interface that allows you to create, monitor and manage pipelines from within the Azure portal.
  • BETTER PERFORMANCE AND SCALABILITY : Azure Data Factory is built on top of Azure HDInsight and Azure Blob Storage, which are two of the most popular products in Microsoft’s cloud platform. This allows it to offer better performance and scalability than many other data integration tools.
  • COST EFFICIENT : Azure Data Factory is a pay-as-you-go service, which means that you only pay for resources that are actually used. There are no upfront costs or long-term contracts, so it’s easy to get started and evaluate the benefits of using Azure Data Factory.
  • INTEGRATION WITH AZURE SERVICES : Azure Data Factory can be used to integrate with many other Azure services. This means that you don’t need to learn new tools or APIs if you already have experience developing applications on the Microsoft platform.
  • EASY TO SET UP : To get started with Data Factory, you need to create a data pipeline. You can do this using the Azure portal or through the Azure Resource Manager (ARM) template language. The service also includes an SDK for Java, .NET, Node.js and Python so that you can build custom integrations for your own applications.


  • LIMITED DATA INTEGRATION CAPABILITIES : Azure Data Factory is not suitable for complex data integration tasks. For example, you can’t use it to transform data or perform any kind of ETL (extract, transform and load) operations.
  • LIMITED SUPPORT FOR NON-AZURE SERVICES : Azure Data Factory does not support integration with non-Azure services, so if your enterprise uses a different data integration tool (like Informatica), then you will need to find another solution for integrating those systems.
  • LIMITED FUNCTIONALITY : While Azure Data Factory has many useful features, there are some limitations on what kinds of pipelines can be created using this service.
  • LIMITED SUPPORT FOR EXTERNAL SOURCES : Azure Data Factory only supports a limited number of external data sources. If your source data isn’t stored in an Azure database or file storage, you’ll need to use another tool for integration.
  • NO OUT-OF-BOX DATA GATEWAY : Azure Data Factory does not include a built-in data gateway, which means that if your source and target systems are on different networks, you’ll need to build one yourself using other tools such as SQL Server Integration Services (SSIS).
  • LIMITED SCALE : Azure Data Factory has a maximum throughput of 10,000 transformations per month. If you have a large data integration workload, this may not be sufficient for your needs.
  • INACCURATE TIMESTAMPS: Azure Data Factory uses the UTC time zone by default and does not provide any way to change it.


  • BROADER SUPPORT FOR DATA FLOW TYPES : Databricks supports more data integration scenarios than Azure Data Factory, including things like complex event processing (CEP), stream processing, and hybrid workloads that combine batch and streaming.
  • MANAGEMENT TOOLS : Databricks provides a web interface for managing your pipelines and runs on top of Apache Spark, which is a fully open source framework with a large community.
  • ON-DEMAND DATA INPUT: Databricks provides an on-demand data input feature that allows you to schedule an Azure Data Factory transformation at a specific time. This feature is useful when you want ensure that your pipeline doesn’t run more often than necessary.
  • DATABRICKS IS AN OPEN SOURCE PROJECT. It’s developed by a community of engineers and data scientists, and it will continue to be supported and improved over time.
  • BUILT-IN DATA GATEWAY : DataBricks has a built-in data gateway that can be used to connect to both on-premises and cloud data sources.
  • FLEXIBLE SCALE: DataBricks provides different pricing tiers for different workloads. If you have a large integration project, you can use the highest tier of service, which has no limit on throughput.
  • ACCURATE TIMESTAMPS: DataBricks uses the local time zone of your source and target systems by default, so timestamps are always accurate when they’re imported into other systems after being processed by DataBricks.
  • FASTER TIME TO MARKET : With Databricks, you can spin up an integrated environment for data analytics in just a few hours. No need to purchase hardware, set up operating systems or databases, or configure the application stack. You simply sign up for an account and start using it immediately.


  • IT IS EXPENSIVE: Databricks is not very cheap. It offers a free trial, but once you start using it, the costs can add up quickly. The company charges $500 per cluster hour and has an Enterprise package that costs $2,500 per month for 10 users.
  • LIMITED SCOPE: DataBricks is a great tool for big data processing, but it doesn’t do much else. If you need to do anything other than store and process large amounts of data, this solution isn’t right for you.
  • LACK OF FEATURES: While DataBricks offers some advanced features such as machine learning libraries and streaming analytics engines, they aren’t as robust as some other options on the market.
  • LIMITED SUPPORT FOR OPEN SOURCE DATA FORMATS: DataBricks has limited support for open source data formats. The only supported format is Avro, which means that if you’re using another format such as JSON or Parquet, then you’ll need to convert it before importing it into DataBricks.
  • LIMITED DATABASE CASTING: You can only cast from one database type to another by using the Spark SQL engine in Databricks.
  • LIMITED DATA ACQUISITION : DataBricks provides an option to pull data from a variety of sources, including HDFS, Amazon S3, Azure Blob Storage and other cloud storage services. However, if you need to extract data from on-premises storage systems or databases, you’ll have to do this yourself before uploading it into DataBricks.
  • You’re limited by the amount of data you can store in your account.
  • Analytics tools are only available for a limited number of languages, including Scala, Java and Python.


  • Azure Data Factory and DataBricks are two different services that provide similar functionality. Both are cloud-based data integration tools that allow you to automate data movement between on-premises systems and cloud storage services.
  • However, there are some key differences between the two that may affect which one is better suited for your business needs. DataBricks is a hosted service that makes it easy to: Build, deploy and manage pipelines. Schedule jobs and set up notifications. Use the DataBricks GUI or command line interface (CLI) to edit your code.
  • Azure Data Factory is a managed cloud service that can be used in conjunction with tools like DataBricks for end-to-end integration projects, but it also has several features that make it more powerful than DataBricks alone:
  • DataBricks is a cloud-based data integration service. It can be used to create pipelines that perform ETL, data analysis and machine learning tasks. DataBricks is designed for enterprises that need to run large-scale analytics jobs over their data. You can use it to build applications that combine multiple sources of data into one place and then analyze them using tools like Apache Spark and Hive.
  • The biggest difference is that Azure Data Factory is a fully managed service, while DataBricks is an on-premises software. This means that you have to take care of your own infrastructure with DataBricks, but it also allows you more control over the data pipeline because you can customize it to fit your needs.
  • DataBricks is a managed service that offers free trial accounts and charges based on usage. The company also provides additional services such as data engineering, analytics and machine learning.
  • Azure Data Factory is a free tool that allows you to build pipelines for moving data from one place to another, but it doesn’t provide any other services beyond this core functionality. Data Factory is a managed service that’s included with your Azure subscription. DataBricks is an on-premises solution that you have to install and maintain yourself.
  • Azure Data Factory is an enterprise-grade solution that can be used to automate data movement and process orchestration. It’s designed for organizations with large amounts of data and complex and repetitive operations that require high availability, reliability, security and compliance.
  • Azure Data Factory is focused on orchestrating the movement of your data, without having to write code. While also offering more low-level control than a Data Factory job. Data bricks are used in scenarios when you need more low-level control over the data movement.
  • For example, if you need to copy or transform data before it reaches your destination or add additional connectivity logic through Azure PowerShell or .NET.