Azure Data Factory (ADF) and SSIS are both robust data integration tools driven by the graphic user interface (GUI) while Azure Databricks is not. They are used for ETL operations and tasks that involve several sources and sinks. In this blog, we will read in detail about the difference between SSIS, Azure Data Factory, and Azure Databricks so that you will be able to decide which tool you must use based on the respective requirements.
We aim to cover the following topics in this Azure Data Factory vs SSIS vs Azure Databricks blog to give you a clear idea of the three tools and how they are different from one another:
Let’s get insight into the three popular tools before moving on to the difference between SSIS, Azure Data Factory, and Azure Databricks.
Introduction to Azure Data Factory, SSIS, and Azure Databricks
Data Engineers and Analysts are responsible for integrating and analyzing bulks of data from a plethora of data sources with the help of ETL tools. They use SSIS, Azure Data Factory, and Databricks for the same. Let’s briefly try to understand their use.
What is SSIS?
SSIS or SQL Server Integration Services is a significant part of the SQL Server Suite of Microsoft. It is a popular ETL tool with built-in transformations for aggregation, joins, splits, etc., used in the field of data integration.
To gain more insight into SSIS, you must watch the video below
What is Azure Databricks?
Azure Data Factory (ADF) is a data orchestration tool as well as an ELT (Extract, Load, and Transform) tool that enables professionals to develop pipelines that help in moving data across various layers in the cloud or from on-premise to the cloud. It is easy to use for professionals who are familiar with SSIS.
What is Azure Databricks?
Azure Databricks is one the latest and most trending tools among Data Engineers and Data Scientists whose main role is to deal with an ample amount of data. It is a fast and easy analytics platform based on Apache Spark which makes the data analytics process much more efficient and productive for business compared to other tools.
Now, it is time to move on to the differences. Let’s begin by reading in detail about Azure Databricks vs Data Factory.
Azure Data Factory vs Azure Databricks
You need to understand the difference between Azure Data Factory and Azure Databricks in order to figure out which tool to use in which situation and when you should use them both. These two popular tools have both similarities and their set of differences. Let us discuss a few of them.
Azure Data Factory and Databricks are both popular data integration tools based on the cloud. They are capable of dealing with all kinds of data, including big data, structured and unstructured data, and batch and streaming data.
ADF’s Mapping Data Flows does not allow connectivity to the data sources available on-premise at the moment while its original Copy Activity uses integration run-times instead of Spark clusters and enables connectivity to the SQL Servers on-premise. Databricks, on the other hand, are able to connect to the on-premise data sources and can also perform better than Data Factory while dealing with Big Data since Databricks support Spark clusters.
Go through our blog on Azure Data Factory tutorial to learn more.
Data Factory does not offer the capability to work with real-time streaming and requires Azure Stream Analysis for this. While, in the case of Databricks, it has Apache Spark API which allows it to support structured streaming and deal with real-time streaming analytics.
ADF offers drag-and-drop functionality for GUI which is similar to the one in SSIS which makes it easy to learn for developers who are familiar with SSIS since it does not require coding knowledge. However, Databricks requires you to use languages, such as Java, Scala, Python, R, etc. This makes it difficult to learn and work with Databricks as compared to Azure Data Factory.
The last and most significant difference between the two tools is that ADF is generally used for data movement, ETL process, and data orchestration whereas; Databricks helps in data streaming and data collaboration in real-time.
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Now that we have covered Azure Databricks vs Azure Data Factory, you will read about the difference between SSIS and Azure Data Factory.
SSIS vs Azure Data Factory
While making a choice between ADF and SSIS, it is important that you know if the company has an Azure footprint and if it does, then is it possible to host the respective project on Azure. If so, then ADF is the best choice of tool. However, if one of the project requirements is that it needs to be finished on the premise due to an existing SSIS ecosystem or for security reasons, then, the best option is SSIS.
Another difference between the two tools is that SSIS is a licensed tool while ADF follows the pay-as-you-go plan. The price of SSIS ranges from free of cost for Express and Developer versions to approximately US$14,000 per core for the Enterprise version. Besides, the integration runtime node in SSIS starts at about US$0.84 per hour on Azure. In the case of ADF, the plan starts from US$1 for every 1,000 orchestrated runs and goes up to US$1.5 for every 1,000 self-hosted runs.
While ADF supports tumbling window and event-based triggers, along with scheduled batch triggers, SSIS supports only batch triggers and the ability to develop custom triggers for real-time data streams.
Further, you will read in detail about the differences between SSIS and Databricks.
Azure Databricks vs SSIS
While Databricks supports both structured data and unstructured data, SSIS only supports structured data. So, in projects where you need to work with data sources containing both types of data, you must choose Databricks over SSIS. Moreover, SSIS only supports batch data whereas Databricks supports batch, streaming, and real-time data.
Azure Databricks uses web browsers while SSIS makes use of SQL Server development tools. Further, SSIS has built-in drag-and-drop user interface functionality, but in Databricks, you are required to familiarize yourself with one or more programming languages and use them.
SSIS uses languages and tools, such as C#, VB, or BIML but Databricks, on the other hand, requires you to use Python, Scala, SQL, R, and other similar developing languages. Also, unlike SSIS, which is a licensed tool, Databricks follows a pay-as-you-go plan.
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You have learned the differences between every two tools in detail. Now, let us get an insight into the Azure Data Factory vs SSIS vs Azure Databricks comparison.
Azure Data Factory vs SSIS vs Azure Databricks: Feature-wise Comparison
|Data Variety||Structured and unstructured data||Structured data||Structured and unstructured data|
|Data Velocity||Streaming, batch, and real-time||Batch||Streaming, batch, and real-time|
|Tools for Development||Web Browser||SQL Server Development tools||Web Browser|
|Languages for Development||Net, PowerShell, or Python||C#, VB, or BIML||Python, Scala, R, or SQL|
|Cost||Follows pay-as-you-go plan||Licensed with free and paid versions||Follows pay-as-you-go plan|
|Use||ETL or ELT, orchestration, and movement of data||ETL, integration, and transformation of data||Preparation and collaboration of data|
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Choose the Best Tool
In this blog on Azure Data Factory vs SSIS vs Azure Databricks, we have learned briefly about the three tools, we also covered individual differences between tools pair-wise, like SSIS vs Azure Data Factory, Azure Data Factory vs Databricks, and Databricks vs SSIS. After learning about that, we learned in brief about the similarities and differences between the three tools to help you get acquainted with them. So based on the requirements of the projects, you can select any one of them or use a combination of two or use all three to perform the necessary operations.
Check out this YouTube video on Azure Databricks
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