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Author's profile photo Alexander Korneev
Alexander Korneev
October 27, 2021 1 minute read

Building analogy between SAP, Microsoft, Google and Amazon cloud analytical services and tools

As more and more customers are migrating to cloud and starting to use different cloud services, there is more and more frequent situation, when it is hard to understand, whether there is already service or tool in the existing landscape, which covers a business scenario or it is necessary to buy and implement a new one.

This short blog post with only one table is aimed to draw high-level analogy between SAP, Microsoft, Google and Amazon cloud services and tools in the area of analytics.

It is important to say, that it is not a comparison or statement, what to use in which situation, but only an attempt to take a look at the current analytical enterprise needs from technology perspective, rather than from marketing.

The listed in the first column business cases, for sure, do not cover all possible big scenarios, but only main of them.

Business need SAP Microsoft GCP Amazon
Limitless analytics service with unmatched time to insight Data Warehouse Cloud Azure Synapse Analytics Cloud Data Fusion/BigQuery/Dataproc Redshift
Apache Spark based analytics platform  SAP HANA Cloud Azure Databricks BigQuery/Dataproc/Cloud Dataflow EMR
Fully managed cloud Hadoop and Spark service SAP HANA Cloud HDInsight Dataproc EMR
A data integration service to orchestrate and automate data movement and transformation Data Intelligence/BTP Integration Suite Data Factory Cloud Data Fusion Data Pipeline/Glue
Open and elastic AI development Data Intelligence and AI Core Azure Machine Learning Vertex AI SageMaker
Real-time data stream processing  SAP Streaming Analytics Azure Stream Analytics Cloud Pub/Sub Kinesis
On-demand analytics service with enterprise-grade security, auditing, and support Data Intelligence Data Lake Analytics Dataproc/BigQuery S3 + Athena
Enterprise grade analytics engine as a service Data Warehouse Cloud Azure Analysis Services BigQuery Kinesis
A hyper-scale telemetry ingestion service that collects, transforms, and stores millions of events Event Mesh Event Hubs Cloud Pub/Sub Kinesis Firehose + Kinesis Streams
Scalable data exploration service Analytics Cloud Azure Data Explorer BigQuery Amazon Timestream
Service for sharing big data with external organizations Data Hub Azure Data Share Datashare Toolkit Lake Formation
IoT analytics platform to monitor, analyze, and visualize industrial IoT data at scale SAP Internet of Things Azure Time Series Insights BigQuery Amazon Timestream

 

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      2 Comments
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      Author's profile photo Peter Baumann
      Peter Baumann

      Hi Alexander,

      thank you for this table which is really a interesting thing and some work to put that together in the right way.

      Just to sync my understanding, some questions:

      • For "Apache Spark based analytics platform" you show Vora from SAP.  Isn't Vora just available within SAP Data Intelligence and running out there in November with the hint to use SAP HANA Cloud instead?
      • For "Fully managed cloud Hadoop and Spark service" - as my understanding of Vora is a Spark-like functionality, which aspect brings SAP Analytics Cloud in here? Would also rather see SAP HANA Cloud here.
      • For "A data integration service to orchestrate and automate data movement and transformation" - if we emphasize on moving (big) data, SAP Data Intelligence could be the better option from my perspective.
      • For "Open and elastic AI development" - SAP AI Business Services are really business specific what from my perspective differentiate strongly from the others. I would see a better fit with SAP Data Intelligence and SAP AI Core?
      • For "Enterprise grade analytics engine as a service" - Not sure about the definition. SAP BW/4HANA seems a little bit unusual here as it is no cloud solution (or only maybe IaaS-possible) and therefore no 'service', Kinesis is a streaming analytics solution and Azure Analysis Services is somehow just a part of the whole compared to BW or Big Query....
      • For "Scalable data exploration service" it seems to be a little bit mixed as AWS Timestream and Azure Data Explorer are more in the streaming field while SAP DWC and Google Big Query are Data Warehouse services. With data exploration I would more understand Analytics solutions. What is your definition?
      • For "Service for sharing big data with external organizations" - not sure here as SAP had a solution which is now SAP Data Intelligence, on-premise edition. This service is in general called Data Hub so this could just lead to a little bit confusion what is meant.

      I would be happy if you could show clearer definitions for this business needs. I think it is not easy to build such a table as not all offerings are comparable from the different vendors.

       

      Thank you for further discussion.

       

      Peter

      Author's profile photo Alexander Korneev
      Alexander Korneev
      Blog Post Author

      Hello Peter,

       

      thank you a lot for your detailed comments. I updated the table accordingly.

      "Scalable data exploration service" - I changed to Analytics Cloud

      With Data Hub I mean this one - would it work?

      Best regards,

      Alexander