Choosing the right data platform: Pros and Cons of SAP Datasphere and Non-SAP Cloud Datawarehouse Platforms

Today, when data drives everything, the selection of the right data platform gets very strategic for any organization seeking to derive insights, efficiency, and competitive advantages from its data. The sea of options available makes it difficult to decide which platform is better aligned to the business need. In this article, I will compare mainly pros and cons of each SAP Datasphere, Azure Synapse, Microsoft Data Fabric, AWS Redshift, Google BigQuery, and Snowflake.

SAP Datasphere

Benefits: SAP Datasphere is a great fit if the investment in the SAP ecosystem is heavy for an organization. The system has data native integration with SAP S/4HANA, SAP BW/4HANA, and other SAP offerings, which enables easy handling of data across all these environments. In the area of complex data types and scenarios, especially within the heterogeneous landscape, it can work well. Also, the end-to-end analytic capabilities, cuts loose all the data management in one place, from ingestion to visualization.

Disadvantages: SAP Datasphere comes with a steep learning curve, particularly to teams who have not used any of the tools that SAP has offered in the past. This would add to the implementation cost and take more time to deploy. Moreover, licensing is costly for small organizations, and integration with non-SAP tools is not easy relative to other native cloud platforms.

Azure Synapse

Advantages: The uniqueness of Azure Synapse comes from the fact that it provides end-to-end data management in a single service for big data processing, data integration, and advanced analytics. This clearly tells the tale of why it is so performant and scalable, given the query processing is distributed. Deep integration into the Azure services means a big plus for companies already working in the Microsoft ecosystem as processes get strung across platforms harmoniously.

Disadvantages: The downside really is that Azure Synapse can be complicated to set up and manage, particularly for teams without deep Azure expertise. Its pricing model is flexible in theory, but it can get out-of-control if not carefully managed. Of course, shifting data in and out of Azure Synapse introduces more latencies and complexity when organizations, all too often, adopt a multi-cloud strategy.

Microsoft Data Fabric

Benefits: Microsoft Data Fabric is designed to bring a common data management framework over diverse data sources, enabling easier management, governance, and data analysis across the enterprise. Not to forget, the interoperability of on-premises, Azure, and multi-cloud environments becomes possible through integration with AI and machine-learning tools. Businesses and organizations are capable of driving smart insights from their data.

Shortcomings: Since this is a relatively new product offering from Microsoft, its maturity and stability are less compared to other, already established platforms in this domain. Hence, this may lead to disadvantages like less community support and third-party integrations. The complexity of managing and optimizing data across these different cloud providers can potentially be high, and implementation would probably require a high level of technical resourcefulness, which probably rules out the best use of such a platform for smaller organizations.

AWS Redshift

Pros: AWS Redshift is best recognized for being performance-optimized, especially in executing large-scale data warehousing workloads. Parallel processing of its column-based storage ensures quick operations on queries. Redshift's pricing model also proves to be very effective when looking at reserved instances, making it affordable and predictable for long-running workloads. Deep integration with the AWS ecosystem brings a great set of cross-benefits that enhance its versatility.

Cons: High concurrency in AWS Redshift may lead to some performance bottlenecks for complex workloads. Indeed, data loading, especially huge-volume and high-frequency update scenarios, could be a hard task. In fact, there could be considerable overhead in tuning, scaling, and maintenance compared to the tools AWS provides for managing Redshift, particularly with teams not having specialized AWS expertise.

Google BigQuery

Benefits: Since Google BigQuery is serverless, one does not need to worry about handling the infrastructure, thus letting organizations focus much more on working with data and analytics. Its real-time capacity and features, such as running SQL- type queries, are perfectly well-suited for rapid, interactive analysis. BigQuery provides an integrated analytic environment for organizations applying Google Cloud, especially with the seamless integration Big Query has with Google services, including Dataflow, AI Platform, and Looker.

Drawbacks: The only drawback is that the cost-based solution can turn out to be very expensive for companies with thick data and plenty of inquiries. Here, the prediction of costs is quite hard without proper attentive monitoring. BigQuery is another service that provides deep integration with the Google Cloud platform, which means companies that usually rely on thick on-premise data amounts or hybrid solutions probably shouldn't take this service into consideration. It also offers less ability for customization than traditional on-premise data warehouses or other solutions born in the cloud.

Snowflake

?Pros: It is cloud agnostic with AWS, Azure, and Google Cloud. Good flexibility for organizations that approach cloud with a multi-cloud strategy. By having more granular architecture, it breaks down the boundaries between compute and storage, and that's how businesses can optimize performance and cost efficiency in respect to criteria associated with their workloads. In addition, data can be shared across platforms in Snowflake, breaking down the silos, and it ensures that moving data between teams is smooth without moving it back and forth.

Cons: Pricing with Snowflake is, however, sometimes hard to predict and manage. Again, due to the carelessness in planning, the price can spike in magnitude, especially for workloads that require a lot of computes. However, on the flip side, there is an underlying cloud infrastructure on which Snowflake sits, meaning that just like with other native clouds, any problems with cloud providers affect the performance. Finally, and in comparison, to the likes of Azure Synapse or Google BigQuery, Snowflake sometimes offers less built-in advanced analytics or machine learning tools, which might mean that external solutions would be needed for those capabilities.

Conclusion

There is no such thing as a "one size fits all" data platform. Each of these platforms, including SAP Datasphere, Azure Synapse, Microsoft Data Fabric, AWS Redshift, Google BigQuery, and Snowflake, has some strong capabilities and, of course, associated challenges. The good fit for these variables depends on the type of ecosystem your organization already has in place, what data strategy you have at present, and what your long-term data goals are.

Finally, for SAP-centric environments, there is SAP Datasphere. Nevertheless, for organizations that are deep in the Microsoft or Google ecosystems, Azure Synapse and Google BigQuery are both very strong propositions. Finally, the solid AWS Redshift remains for performance-intensive workloads. With Snowflake's flexibility, it is very much the poster child for multi-cloud strategies.

As organizations evolve, ensuring they are best placed to provide maximum value to these platforms by adjusting and optimizing them to evolve with requirements will be of the utmost importance.