When to Use SAP HANA Cloud for Data Storage

SAP HANA Cloud is a cloud-native, in-memory database platform built for data integration, application development, and advanced analytics.

In this blog post, we'll look at the primary use cases for SAP HANA Cloud and when it makes sense for your data storage needs. We’ll explore the following:

• Integration use case: SAP HANA Cloud is a powerful hub for integrating disparate data sources, creating a sin­gle, logical data layer without the need for complex, costly data replication. This capabil­ity is known as data federation or data virtualization.
• Business applications use case: SAP HANA Cloud provides a high-performance foundation for building modern, intelli­gent applications. Its multi-model capabilities and real-time processing allow for appli­cations that are responsive, context-aware, and able to handle complex data types.
• Analytics use case: SAP HANA Cloud is a premier platform for advanced analytics, offering unparalleled speed for complex queries and enabling organizations to move from descriptive to pre­dictive and prescriptive insights.

SAP HANA Cloud provides a robust and flexible platform for a variety of analytics use cases, enabling organizations to transform their data into actionable insights. Its cloud-native architecture facilitates seamless integration, powerful predictive analytics, and a stream­lined transition from legacy on-premise systems. We’ll explore a few analytics use cases too.

Integration with Business Intelligence Tools

SAP HANA Cloud integrates seamlessly with leading BI tools, particularly SAP Analytics Cloud. This integration allows business users to create dashboards and reports on top of the unified data layer in SAP HANA Cloud, enabling them to explore data in real time with­out needing technical expertise.

Following are the high-level usage instructions:

• Model the data: Use SAP HANA database explorer or SAP Business Application Studio to create data views and models that unify disparate data sources (e.g., combining on-premise sales data with cloud-based marketing data).
• Connect to the BI tool: In SAP Analytics Cloud, create a live connection to the SAP HANA Cloud instance.
• Build dashboards: Build dashboards and reports directly from the live connection, using the premodeled data for fast and accurate insights.

Predictive Analytics

The in-memory architecture of SAP HANA Cloud, combined with its powerful libraries, makes it an ideal platform for predictive analytics. Organizations can move beyond histori­cal reporting to a proactive, predictive approach.

A manufacturing company uses sensors on its machinery to monitor performance. To avoid costly breakdowns and unplanned downtime, it needs to predict when a machine is likely to fail. The company streams real-time sensor data into SAP HANA Cloud. It then uses one of its libraries—PAL or APL—to train ML models on this data. These models can analyze pat­terns, identify anomalies, and predict potential failures, triggering an alert for a mainte­nance worker.

Following are the high-level usage instructions:

• Ingest the data: Use SAP HANA, streaming analytics option to ingest real-time data from IoT devices into SAP HANA Cloud.
• Prepare the data and model: Within SAP HANA Cloud, use SQL scripts or a graphical interface to prepare the time-series data. Then, call the relevant PAL or APL function to train a predictive model.
• Operationalize: Embed the trained model into a stored procedure or an application to automate predic­tions. The results can be visualized in a dashboard that shows which machines are at high risk of failure.

Transition to Cloud

Migrating from an on-premise SAP HANA system to SAP HANA Cloud offers a seamless path to modernization. It allows businesses to gain the benefits of a cloud-native platform while preserving their existing data models and investments.

A company decides to migrate its data warehouse to SAP HANA Cloud. The company has a legacy data warehouse built on an on-premise SAP HANA database. The hardware is aging, and the IT team spends significant time on maintenance tasks such as backups, updates, and scaling.

They can use the Self-Service Migration tool to automate the data migration process, per­forming compatibility checks and guiding the user through the migration of database objects, schema, and data. After the migration, the company can use SAP HANA Cloud’s elastic scalability, paying only for the resources they use. The IT team is freed from infra­structure management and can focus on more strategic initiatives.

Following are the high-level usage instructions:

• Assess and plan: Use the Self-Service Migration tool to perform a compatibility check on your on-premise database to identify potential issues.
• Prepare for the migration: Create a migration user and ensure your target SAP HANA Cloud instance is provisioned and ready.
• Execute the migration: Use the tool to automatically migrate your schemas, tables, and data. This process can be executed in a phased approach to minimize downtime.
• Validate the data and finalize: After the migration, run validation checks to ensure data consistency and accuracy before cutting over to the new cloud environment.

Conclusion

Whether you're unifying data sources, building intelligent applications, or moving from descriptive to predictive analytics, SAP HANA Cloud offers a flexible foundation for it all. With built-in migration tooling and seamless SAP Analytics Cloud integration, it's a strong option for organizations ready to modernize their data strategy.

Editor’s note: This post has been adapted from a section of the book Data Management and Analytics with SAP BTP by Dhirendra Gehlot, Jeff Gericke, Shibajee Dutta Gupta, Antony Isacc, Homiar Kalwachwala, Rick Markham, Asim Munshi, and Chris Sam. The authors of this book bring together decades of collective expertise across SAP data management, analytics, enterprise architecture, and information management. Their experience spans data integration, master data management, business intelligence, data migration, and AI-driven transformation, with each contributor having guided organizations through complex modernization efforts. United by a shared passion for turning data into actionable insight, they draw on more than 100 combined years of experience to offer practical, real-world perspectives on building trusted, scalable data foundations.