In addition to numerical and statistical algorithms, HANA can perform text analytics and enterprise text search. HANA's search capability is based on “fuzzy” fault-tolerant search, much like modern web-based search engines. Results include a statistical measure for how relevant search results are, and search criteria can include a threshold of accuracy for results. Analyses available include identifying entities such as people, dates, places, organizations, requests, problems, and more. Such entity extraction can be catered to specific use cases such as Voice of the Customer (customer's preferences and expectations), Enterprise (i.e. mergers and acquisitions, products, organizations), and Public Sector (public persons, events, organizations). Custom extraction and dictionaries can also be implemented.
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Ignite innovation with cloud Our strategy leverages any cloud to drive innovation and deliver a consistent experience with composability across containers, clouds, and bare metal. Learn more Redefine experiences at the edge We deliver the Intelligent Edge—powering new immersive mobile experiences and delivering real-time intelligence in industrial environments. Learn more Unleash the power of your data We help enterprises accelerate insights from data everywhere with an approach that’s AI-driven, built for cloud, and delivered as a service. Learn more
The column-store database offers graph database capabilities. The graph engine processes the Cypher Query Language and also has a visual graph manipulation via a tool called Graph Viewer. Graph data structures are stored directly in relational tables in HANA's column store. Pre-built algorithms in the graph engine include pattern matching, neighborhood search, single shortest path, and strongly connected components. Typical usage situations for the Graph Engine include examples like supply chain traceability, fraud detection, and logistics and route planning.
Before you start proceeding with this tutorial, we assume that you are well-versed with basic database concepts. You should have a good exposure to SQL, as SAP HANA is like a relational database. If you are not aware of these concepts, then we recommend that you first go through our short tutorials on SQL.
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The key distinctions between HANA and previous generation SAP systems are that it is a column-oriented, in-memory database, that combines OLAP and OLTP operations into a single system; thus in general SAP HANA is an OLTAP system. Storing data in main memory rather than on disk provides faster data access and, by extension, faster querying and processing. While storing data in-memory confers performance advantages, it is a more costly form of data storage. Observing data access patterns, up to 85% of data in an enterprise system may be infrequently accessed therefore it can be cost-effective to store frequently accessed, or "hot", data in-memory while the less frequently accessed "warm" data is stored on disk, an approach SAP have termed "Dynamic tiering".
In a scale-out environment, HANA can keep volumes of up to a petabyte of data in-memory while returning query results in under a second. However, RAM is still much more expensive than disk-space, so the scale-out is only feasible for certain, time critical, use-cases.
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