Analyzing Connected Data with SAP HANA Graph
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This free online course introduces SAP HANA graph and describes possible use cases, as well as showing you how to expose data to SAP HANA graph via nodes, edges, and workspaces, and much more. This course is aimed at data scientists, application developers, and technical business analysts.
Course information
Today, enterprises and organizations collect and analyze huge volumes of data that is increasingly connected. Traditional approaches of storing and processing data in relational databases are insufficient when it comes to analyzing “connections” efficiently. In the recent past, graph databases have proven their suitability for novel types of use cases. For example, manufacturing companies analyze their value chains (supply – production – sales) and ask questions like “how does a change in the price of raw materials impact the margins of my finished products?” Utility companies need to understand their network and plan and evaluate outages within an electricity grid. Public safety organizations draw conclusions from suspects and their relations in a social network.
SAP HANA graph provides built-in algorithms and programming models to analyze connected data. As an integral part of the SAP HANA platform, SAP HANA graph combines and bridges the worlds of relational and connected data, thereby reducing system landscape complexity and providing real-time graph analysis. Furthermore, graph analysis can be easily combined with other SAP HANA engines, for example full-text search and geospatial analysis, making SAP HANA an ideal platform for multi-modal applications.
This course starts with an introduction and use case descriptions. After explaining how to expose data to SAP HANA graph via nodes, edges, and workspaces, we will walk you through pattern matching queries and built-in algorithms. Unit 5 is about GraphScript, a domain-specific language for custom graph algorithms. After taking a side-step into SAP HANA hierarchies in SQL, we will talk about some integration options with adjacent technologies like full-text search and spatial. The learning units will be accompanied by system demos.
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