In order to cope up with these different needs of storage, computation and technology stacks public cloud providers have come up with different service offerings to cater the needs of data analytics domain. Sometimes it confusing to select which tools to use in order to work with the project/ experiment we have.

Analyzing data always involves with computations. It’s quite usual to have big datasets both in structures and unstructured formats sitting in enterprise level databases to perform analysis/ ETL tasks.  

In such scenarios, relying on traditional sequential processing and data storage options are not a viable option. We must use an approach that can write scalable applications that can do parallel processing to process a large amount of data.  

In this post, I’m going to share my experience and a comparison on some of the data related tools comes with Microsoft Azure and their usage in real world applications. It may help you to get an idea on the toolset to choose with the need you have.

Azure Data Factory Vs. Azure Synapse Analytics

Azure Synapse Analytics is a newly introduced analytics service that brings together data integration, enterprise data warehousing, and big data analytics. Azure Data Factory (ADF) is a fully managed, serverless data integration service. Both are ETL and data analytics tools with some different approaches and features.

If you building an analytics solution in Azure, Synapse would be the best choice since it provides a single pane where you can do data integration, management, monitoring and security on the same place. If you want the power of Apache Spark, Synapse would be the choice to go forward with.

Note that some integration features on Synapse is in public preview yet compared to matured ADF platform.  

In my personal experience, I have seen lot of industrial use cases are moving towards Synapse since it’s providing more unified experience of data engineering + data analysis in a single place.

For further references : https://docs.microsoft.com/en-us/azure/synapse-analytics/data-integration/concepts-data-factory-differences

Azure Data Factory Vs. Azure Databricks

ADF is primarily used for Data Integration services to perform ETL processes and orchestrate data movements at scale. In contrast, Databricks provides a collaborative platform for Data Engineers and Data Scientists to perform ETL as well as build Machine Learning models under a single platform.

There’s a big difference when it comes to the ways of working and flexibility. ADF offers a drag and drop feature to create and maintain data pipelines visually while Databricks offers a programmatic approach with a notebook environment which supports Python, Spark, R, Java, or SQL.

If an enterprise wants to experience a no-code ETL Pipeline for Data Integration, ADF is better. On the other hand, Databricks provides a Unified Analytics platform to integrate various ecosystems for BI reporting, Data Science, and Machine Learning.

Azure Synapse Vs. Azure Databricks

Apache Spark powers both Synapse and Databricks. With optimized Apache Spark support, Databricks allows users to select GPU-enabled clusters that do faster data processing and have higher data concurrency.

Both Synapse and Databricks consist smart Notebooks. Databricks Notebooks support real-time co-authoring along with automated version control. While Synapse environment is not supportive for local IDEs, Databricks can be remotely connected with VSCode or PyCharm IDEs.

Synapse Is having built-in support for .NET applications which is an advantage in end-to-end solution development.

Personally, I would say, Synapse is not mature yet compared to Databricks in certain areas.

Azure Machine Learning Studio Vs. Azure Databricks

While Databricks is built upon Apache Spark, Azure Machine Learning Studio can be defined as an integration of machine learning model development toolset with even no-code setups. If you are familiar with building machine learning models using python or R, you’ll be very familiar with the Jupyter Notebook like environment AML Studio offers. AML Designer and Automated-ML capabilities are good for prototyping and developing machine learning models with no-code.

In contrast, Databricks are more geared towards data engineers and data scientists, not for the general user as Azure machine learning.

For computer vision/ NLP kinda experiments which uses GPU based computations I would prefer using Azure machine learning with native Python support, rather than spinning up set of nodes on Databricks.

Selecting the best tool to develop your solution and perform the experiments is a decision to take after analyzing the project life cycle, resources, cost, and the capabilities of the team. Let me know what’s your favorite platform out from these list?

References :

• Azure Synapse Analytics : https://docs.microsoft.com/en-us/azure/synapse-analytics/overview-what-is

• Azure Databricks : https://docs.microsoft.com/en-us/azure/databricks/

• Azure Machine Learning : https://docs.microsoft.com/en-us/azure/machine-learning/