Azure Synapse vs Data Factory: 4 Major Differences

Organizations use Azure Synapse Analytics and Azure Data Factory to streamline data handling processes. Both tools offer numerous features to quickly connect, transform, and centralize data. However, since both platforms are similar in a few aspects, it becomes difficult to pick between the two solutions. 

In this article, we will deep-dive into the similarities and differences between Azure Synapse and Azure Data Factory. This will allow you to understand the capabilities of the solutions and identify the best solution for your business requirements. 

Azure Synapse Overview

Azure Synapse is a unified analytics service that includes capabilities for data integration, data warehousing, and data analytics. For integration, Azure Synapse supports over 95 native connectors that allow you to gather data from multiple sources. After the data is collected, you can transform the data and store it in a data warehouse. Eventually, you can leverage Azure Synapse for analysis and visualization.

As it comes with numerous features to manage end-to-end big data workflow, it has quickly become popular among analytics professionals. Synapse’s ability to handle unstructured data stored in a data lake makes it even more powerful. Synapse also supports analyst-friendly languages like T-SQL, Python, Scala, Spark SQL, and .Net.

How Can I Build a Pipeline in Synapse?

1. Sign in to the Azure portal.
2. Click on the icon [>_] to create Cloud Shell to get a command line interface environment.

3. In the command line interface (PowerShell), provide the following command to clone a repository from GitHub:

rm -r dp-000 -f

git clone https://github.com/MicrosoftLearning/mslearn-synapse dp-000

4. Navigate to the desired folder (../16) and run setup.ps1 script to set up the project.
5. When prompted, provide the password for your Azure Synapse SQL pool.
6. Now, open the dp000-xxxxxxx resource group created after running the setup.ps1 script.
7. Open the resource group, and select Open to start Synapse Studio. 
8. Create a dedicated SQL pool (an enterprise data warehouse). Go to Manage > SQL pools. Now, select the sqlxxxxxxx dedicated SQL pool and press the run icon. 
9. You can now start building pipelines. Head to Home > Ingest > Copy Data. When prompted, provide the necessary details to set up the tool.
10. You can build a transformation pipeline by navigating to Home > Integrate.

Azure Data Factory Overview

Azure Data Factory is a fully managed, serverless data integration service. It has more than 90 built-in connectors to collect data from different sources. You can also use Azure Data Factory to transform data without writing a single line of code. Since it is a no-code platform, it enables non-technical professionals to gather and transform data efficiently.

Azure Data Factory also supports Git and CI/CD to build ETL/ELT pipelines incrementally. With Azure Data Factory, you can even monitor your pipelines without its no-code capabilities.

Azure Synapse and Azure Data Factory – Similarities

Azure Synapse and Azure Data Factory have similarities in data integration and transformation practices. Both services have built-in connectors that allow you to move data between different databases with UI-based workflows. 

You can even find similar features for ETL or ELT on both platforms. While building data pipelines, both platforms allow similar connectors to pull data. You can also manage how the solutions scale up or down based on the size of the data to ensure better performance flexibility.

And to extend data engineers’ capabilities, both solutions offer linked services. It can be used to connect with other Azure services for handling the data based on downstream requirements. As a result, if you are familiar with Azure Data Factory, you can swiftly move to Azure Synapse, as both have similar data integration and transformation features.  

Azure Synapse vs Data Factory – Major Differences

Data Transformation Capabilities

Data transformation can be carried out through the no-code capabilities of both platforms. But, with Azure Synapse, you can use programming languages like Python to write custom code for transformation. Based on the business requirements, data transformation can get complex over time. As the complexities increase, you will require more flexibility to modify and optimize data pipelines. 

However, with Azure Data Factory, you would have to primarily rely on no-code features. As a result, Azure Synapse becomes a go-to platform for your strenuous data transformation requirements. As Azure Synapse supports numerous programming languages, it empowers a broader range of professionals to transform data.

Support for Machine Learning

Azure Data Factory only helps you with data integration and data transformation. On the other hand, Azure Synapse allows you to perform analysis with numerous programming languages. You can use Python or Spark to leverage its broader ecosystem of machine learning libraries to build robust models for generating insights. Since Azure Synapse also supports unstructured data, leveraging machine learning becomes crucial to gain in-depth insights. 

Azure Synapse also supports AutoML workflows. You can quickly train machine learning models without writing code. To build no-code AutoML models, you only have to select the model type. You can choose between classification, regression, and time series to get a set of machine-learning models. Based on the business requirements, you can select the best model for your use case.  

Security and Access Control

Microsoft ensures that you get better security and access control over your resources on the cloud. With Azure Synapse, you get to control access to data, code, and execution based on the roles. You can even control version control and continuous integration capabilities. To ensure you manage the access to different users effectively, create security groups and apply the permissions.  

Azure Data Factory also offers you to manage access control of resources. Generally, you add a user with the contributor role in the Azure Data Factory. A contributor has permission to create, edit, and delete resources like datasets, pipelines, triggers, and more. However, you can customize roles to serve your business requirements. For instance, you can allow users to access only a few data pipelines or let users monitor the pipelines.

Pricing

Pricing of Azure Synapse Analytics is complex since the cost would depend on the types of services you use. For instance, the cost would be calculated based on the storage, I/O requests, and compute units. Generally, the pricing is categorized as follows: Data Exploration and Data Warehousing, Apache Spark Pool, and Data Integration. Since the workflow in each of these categories is different, pricing varies based on workflow. You can use the Azure pricing tool for Synapse to understand its cost.

Azure Data Factory comes in two versions: V1 and V2. Azure Data Factory V1’s cost depends on the status of pipelines, frequency of activities, and more. Azure Data Factory V2’s price considers data flow, pipeline orchestration, and the number of operations. 

Both services’ cost depends on your business use cases. As a result, Azure recommends you request a pricing quote for more clarity and a custom deal.

Category	Azure Synapse	Azure Data Factory
Data Transformation	Data pipelines are built with no-code features and Python programming	Data pipelines are primarily built using no-code features
Machine Learning	Build machine learning models with or without code	Mostly used for building data pipelines only
Security and Access Control	Has a wide range of access control for Azure roles, Synapse roles, SQL roles, and Git permissions	Generally, users are added with contribution role
Pricing	Pricing is categorized based on the resources you use for data warehousing, data exploration, and more	Pricing is dependent on the status and usage of data pipelines

Conclusion

Azure Synapse Analytics is widely used among professionals that are looking for an end-to-end solution for analytics. Synapse allows you to collect, transform, and analyze data from just one platform. However, Azure Data Factory is only suitable for data engineers to streamline data collection processes with in-built processes.

If you only want to connect and transform data without writing code, you should embrace Azure Data Factory. However, it doesn’t allow you to customize your data pipelines beyond its no-code capabilities. In case you are looking for more flexibility while working with data, you must embrace Azure Synapse Analytics.

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