11/10/2023 0 Comments Goodsync google drive![]() ![]() It evolves as new business cases arise and new features are added to the products. ![]() Please note that the architecture discussed here is not static. We illustrate the reference architecture here using a few sample business use cases. Today, we provide prescriptive guidance on reference architectures for building data integration between SAP BTP Data and Analytics solutions (Datasphere and SAP Analytics Cloud) and Google Cloud Data Analytics solutions ( BigQuery, Google Cloud Cortex Framework and VertexAI). We’re seeing strong adoption of SAP Business Technology Platform (BTP) on Google Cloud, as captured previously in examples SAP BTP on Google Cloud Announces 5 new capabilities and SAP Build Process Automation is better with Google Document AI and Google Workspace. Google Cloud and SAP have been partnering for many years to help customers run their business critical SAP workloads on Google Cloud. Google Cloud and SAP see customer value in these hybrid approaches and we are working together to make it easy for our customers to use our data and analytics solutions. We now see customers looking more and more at hybrid approaches where “time-sensitive” and “access-sensitive” data is federated from the source while other data is replicated to the target data platform. While this approach (data replication) solves the need to bring all the data into one single landing zone, it typically creates other challenges around data governance, data freshness, reconciliation and loss of semantic context, all of which comes at a cost to manage. Traditionally, to solve this problem, enterprises first build a data lake to bring data from all the data sources and then apply data cleansing, deduplication and normalization to build a data and analytics platform. During a major weather event, the electric utility will want to have a 360 degree view of their customer impact and this usually involves connecting data from SAP and other non-SAP systems to provide a consolidated view of their customers.Ĭustomers want a single pane of glass to their data. For example, an electric utility company may be using SAP for their customer relationship management and billing functions and a non-SAP solution for outage management and connected meters. Our customers are continuously looking for better ways to gain insights into their business and drive innovations, but are challenged with the prototypical fragmentation of data across multiple applications and data warehouses. ![]() Google and SAP are jointly addressing all the above, and more, with our recently announced open data offering across SAP Datasphere and Google BigQuery. Over the last 6 months, our conversations with customers have evolved from “how do we best manage all of our data across all these disparate systems of record and public data signals” … to “how should we modernize our overly customized and mission critical SAP BW stack?” … to more recently “I have an idea using GenAI within my business process, how do I get this implemented?”. ![]()
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