Key Considerations on SAP HANA


If you are thinking of deploying SAP S/4 HANA, there are many vital considerations you need to be aware of. These considerations include real-time analytics, scalability, flexibility, and performance.

In-memory technology enables data to be rapidly processed and accessed. It helps ensure business transactions and analytics are accessible in real-time.

Real-time Analytics

Using SAP HANA for real-time analytics is crucial to digitalizing business processes and improving customer experience. With live data, businesses can optimize their operations by gaining real-time insights into customer behaviour, operational efficiency, and future product direction.

Like SAPinsider, real-time data analysis enables companies to make informed, immediate decisions that improve operations. For example, online retailers use real-time analytics to serve up the next best offer to a customer. Likewise, banks use real-time analytics to detect fraudulent activity and fraud alerts. Using SAP HANA for real-time analysis can help organizations identify risks, optimize business models, and increase productivity and revenue.

However, this type of analytics requires a high-performance architecture to handle data at scale and speed. In addition, it must adapt quickly to changes in data volume and source.

To achieve this, companies must choose a solution to process data quickly and efficiently without sacrificing data quality or user experience. In addition, it is essential if the solution involves much user interaction, such as an online gaming platform.

The need for real-time analytics has driven many organizations to use SAP HANA as a data foundation. It enables them to eliminate data redundancy and reduce hardware requirements and management costs.

In addition, it allows organizations to integrate business applications with third-party systems and custom applications. In this way, they can create an in-memory data platform for analyzing and recording data across systems in their organization.

Streaming analytics is a critical feature that enables companies to perform real-time analysis on high-volume, high-velocity event streams. This kind of analytics enables companies to extract and analyze data faster.

To achieve this, it would be best to have a high-performance server that can handle SAP HANA at scale and speed. In addition, you need an infrastructure that supports high availability (HA) and disaster recovery (DR).


The ability to scale up and grow resources is crucial for a company that wants to keep up with an ever-growing data volume. Solutions like SAP HANA Cloud can accommodate this growth without requiring a costly upgrade process.

It is because HANA databases can store large volumes of data and process it in memory. As a result, it allows users to access this data and make quick decisions quickly.

In-memory data storage also gives SAP HANA a competitive edge over other database platforms. It allows it to perform 3600 times faster than traditional databases and has near-zero latency, allowing for real-time analytics and data visualization.

SAP HANA can integrate various data sources and use machine learning to analyze them in real time. As a result, it helps businesses make better decisions and improve their processes in a more timely manner.

Having a database that can grow to accommodate your data is essential when it comes to digital transformation, as it will allow you to make the most of your investments in technology. In addition, it will help you achieve your business goals and stay ahead of the competition.

Another benefit of a cloud-based database is adding new resources to your system as needed. It is beneficial if you have a growing amount of data to store and retrieve simultaneously.


As organizations move to SAP S/4HANA, the need for flexible infrastructure becomes increasingly critical. Flexibility allows your organization to rapidly adapt to fluctuating business requirements and demands on your SAP HANA environment and supports user and application needs.

It is a significant advantage over traditional EDI solutions, which can be costly to implement and maintain in-house. Instead, a managed cloud-based solution can streamline business data processes and free up internal resources for more strategic tasks.

With S/4HANA, companies can acquire smaller brands and sell them as new products or lines of business without folding them into their core ECC system.


SAP HANA is a powerful database platform requiring high-end hardware to perform at peak performance. It is why SAP has worked with partners to ensure that customers can access state-of-the-art hardware designed to handle the heavy load of large databases in a real-time business environment.

In addition to hardware, you must provide SAP HANA with an appropriate amount of RAM and storage capacity. It is particularly true if you are running an SAP HANA cluster.

It would be best if you also provisioned a separate volume for /hana/data and /hana/log, which should be at least 1.5 times the memory size available to the VM instances you have installed SAP HANA. 

Another critical consideration is the type of storage media that you use for the /hana/data and /hana/log volumes. 

Similarly, you should consider the type of storage that you use for the /hanabackup volume. For this volume, you should consider a Compute Engine SSD or a hybrid balanced persistent disk. It will ensure the volume performs at the same speed as a Compute Engine SSD or a Hybrid Balanced Persistent Disk for the /hana/data and the /hana/log volumes.

As a result, it is essential to plan your VM instance size and storage capacity carefully when using a Compute Engine SSD or a balance disk for the /hana/data and the /hana/log volumes. It will ensure that you have the proper amount of SAP HANA data storage capacity for your VM instance and enough memory for all vCPUs in your VM.

The SAP HANA database is built around storing data in columns rather than rows. It allows the SAP HANA engine to optimize performance by minimizing the number of rows read when executing queries. As a result, it can make the query process up to 150 times faster than a row-storage database.

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