Big Data Platform Comparisons

Choosing a platform for doing your Big Data processing tasks is not an easy choice. At one side you want to be flexible and open, but at another you would like a stable and robust platform that can handle your critical business workloads.

This is the reason that I decided to create a comparison of the different Big Data platforms from a Microsoft perspective.

Note: I am confident that other major cloud vendors such as Google and AWS, or even other vendors also have have excellent Big Data platform products, but seeing that I am not a specialist in these, I would like to keep it to the ones I am proficient in.

Note 2: This will also include a lot of assumptions to make the comparison as fair as possible.

The products that we will be covering are:

Note: I will not include a detailed overview of the services but rather a comparison. For a detailed overview, feel free to check the associated links for each service.


As in any comparison, some assumptions were made. In this case the following assumptions were made:

  • 1 Web Node was utilized where a web interface is required
    • Size: D2v3
    • Solutions: Cloudera Cloudbreak
  • 2 Head Nodes were utilized where required for HA purposes
    • Size: A3
    • Solutions: HDInsight, Cloudera Cloudbreak
  • 3 Worker Nodes were utilized
    • Size: D13v2
    • Solutions: Azure Databricks, HDInsight, Machine Learning Services, Cloudera Cloudbreak, Apache Spark on Kubernetes (K8S)
  • No Data Disks were selected
  • For Pausing enabled clusters, 8h was included (240h/mo) was taken, for others 24h (720h/mo)
    • Note: 8h pricing is also included in others, but has been wrapped with () for clarity reasons. They can pause but extra work will be required to support this.

Comparison Matrix

- Spark on K8S Azure Databricks HDInsight Cloudera Cloudbreak Azure Machine Learning Services
Multi Cloud Yes Yes No Yes Yes (1)
Deployment Model IaaS / Half PaaS PaaS PaaS (with full cluster control) IaaS PaaS, with integrated support for compute on ML Services, VMs, Databricks, HDI and K8S
Auto Scale Yes (will require manual configuration) Yes Yes (preview) Yes Yes (on Machine Learning Compute or Databricks)
Compute Pause Support No (but scale-down yes and can be automated) Yes No (but scale-down yes, and can be automated) Yes Yes
Language Support Scala, Python, R, SQL, Java, .NET Scala, Python, R, SQL, Java Scala, Python, R, SQL, Java Scala, Python, R, SQL, Java Python & REST
Notebook Support No Yes Yes Yes Yes
Scheduling Support No Yes Yes, through Oozie Yes, through Oozie Yes, Through Platform or SDK integration
Tooling Re-training Required Server management through K8S Databricks Interface HDP Components HDP Components & Cloudbreak Interface SDK Interface OR GUI Interface in Azure Portal
Extensibility No No Yes Yes Yes
Performance Gain Out-Of-The-Box 0% 40% 0% 0% N/A
Cost 24h: 1,662.21 USD
(8h: 546.48 USD)
24h: 2,409.00 USD
8h: 803,88 USD
Note: perf increase added (2)
24h: 2,084.00 USD
(8h: 685.15 USD)
24h: 2,100.36 USD
8h: 749.43 USD
+375 license cost (3) / mo
Depends on K8S, HDI, Databricks VMs implementation


  • (1): Multi Cloud since this is an offering that can be implemented through an SDK and is more on the Model Training and Operationalization part. Notebook support however has been included recently, making this a viable solution now. For Spark workloads, I however recommend to include another service with it.
  • (2): Databricks offers an out of the box performance increase - see: website1 and website2 for more details
  • (3): For enterprise support, licenses are required. See this website for more information. For our comparison, we took a price of 1.500 USD per license for only the worker nodes (so 3 worker nodes * 1.500 USD / 12 months). Exact pricing needs to be checked with Cloudera and this is purely indicative!


More references can be found for the following products at these links:


Spark on K8S


Azure Machine Learning Services


Xavier Geerinck

Xavier works as a Cloud Solution Architect at Microsoft, helping its customer unlock the full potential of the cloud. Even though he is still considered a young graduate, he achieved his first success at the age 16, by creating and selling his first startup. He then took this knowledge to create and help more startups in different markets such as technology, social media, philanthropy and home care. While in the meantime gaining more enterprise insights at renowned enterprises such as Nokia, Cisco and now Microsoft.

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