SAP on Google Cloud Platform Meets BigQuery

„It doesn't really matter what an SAP system is running on. The main thing is that the infrastructure provides sufficient performance.“ Companies that rest on such statements today and do not look beyond their core processes will probably disappear from the market in a few years. With the migration of the SAP landscape to the Google Cloud Plattform (GCP), on the one hand you use a highly available, flexible infrastructure licensed by SAP, and on the other hand you also have an innovative set of tools and services at your disposal with which you can transform your business in a forward-looking way.
 

The development of a digital platform can be significantly advanced by migrating the SAP landscape to the GCP. However, the decisive factor for further success is that the available innovative services are used to optimize the company's own business on the basis of the opportunities thus gained. With the Google Cloud Platform, companies have a wide range of services at their disposal that start precisely there. An important step towards an intelligent and data-driven company is the combination and analysis of its own data with further information from customers, suppliers and competitors. With the BigQuery tool, it is possible to create precisely this required central data platform.

By linking SAP and BigQuery, the SAP database can be streamlined through targeted data management.

SAP HANA is an in-memory database. As a result, the RAM requirements for SAP installations with HANA DB are exceptionally high and increase as the data volume grows. To reduce RAM requirements and thus also save costs, it makes sense to think about targeted data management. BigQuery can provide support here and simplify the handling of warm and cold data.

Data that is one to two years old is referred to as Hot Data. These are used daily, so they are active data. In HANA, this data is stored in memory and is therefore quickly available. Here, outsourcing to BigQuery makes little or no sense.

Warm data, on the other hand, is data that is three to five years old. This data is accessed less frequently. This data is needed for annual reports or comparative figures, for example. For warm data, HANA enables the non-active data to be swapped out of the main memory into the file system via Hana Dynamic Tiering, thus slimming down the required memory. However, if the data is to be used again, it must first be loaded back into the database, for which a buffer must be left. Therefore, slimming via dynamic tiering is not ideal. BigQuery, on the other hand, can be integrated into the SAP system. Among other things, warm data can be stored in BigQuery and made available again in SAP via corresponding views.

Data that is already more than five years old usually only exists for archiving purposes. Therefore, this data, in particular, should be loaded into BigQuery because it takes up storage space in the conventional database and unnecessarily inflates it.

With the SAP Data Service, data management can be automated, and Warm Data can be moved to BigQuery and deleted from the SAP database. This way, storage capacities are automatically updated, managed, and kept lean by the SAP database.

Besides the quick win of a leaner HANA DB, the combination of SAP and Google BigQuery also offers opportunities to create business value for enterprises.

BigQuery and SAP can be easily integrated and used in both directions. Real-time transfer of data from SAP to BigQuery is also feasible. In BigQuery, centralized company data from SAP can be enriched with additional information from other sources. By centralizing data from, for example, CRM, online stores, branches, and production facilities, BigQuery becomes the company's central data platform. In addition to internal data, the data platform can also be extended to include public data such as Google search trends.

Using SAP data extended in this way, it is possible for companies to gain a 360° view of their customers. Using dashboards, which can be created via the Data Studio, for example, individual departments can create relevant, interactive reports for themselves. Key figures can be called up live, and companies can be controlled in real-time via this. In addition to internal use, it is feasible to give suppliers access to the data that is relevant to them. Dedicated access control to the own data platform can be realized via Looker.

With the introduction of a central data platform in the company, the further use of innovations to increase business value is facilitated. In addition to the ML capabilities built into BigQuery, using GCP also provides access to a comprehensive and proven ML toolset. Companies use Google's tools, for example, for intelligent maintenance of their production facilities (predictive maintenance), for visual product recognition in returns processing, or for predicting financial ratios.

In this way, the transformation to an intelligent, data-driven company is gradually becoming a reality.

By combining SAP with tools from the Google Cloud Platform, companies can create impressive opportunities to transform into intelligent enterprises.

Using the serverless data warehouse BigQuery with SAP enables the creation of a central data platform. This allows for high-performance analysis of data and a resulting overall picture of the business in real-time.

With the innovative Google ML toolset, the central data platform can further optimize SAP-based processes and control the company in a data-driven and intelligent way.