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The database remains the core concept of storing a business’ information as well as querying that information to generate insights. However, as time marches on, the traditional database of the past with SQL and PHP queries have become clunky for the kind of data we have and the sort of connections we want to make. To deal with this bottleneck, SAP changed the database paradigm and created something that resembles what we know, but is ever so slightly different – a graph database. These types of databases aren’t new to technology but their re-emergence stems from how a company can deal with the kind of complexity that large volumes of unspecialized data  can introduce into their database system. SAP Hana is tasked with making this data more accessible and generating insights from the relationships created.

Understanding what SAP Hana Graph Can Do

Basically, SAP Hana Graph is a database that stores information in a graph format allowing for relational tables based on the interaction of graph elements. Graph tables can have connections made both along rows and columns allowing for increasingly complex relationships to be developed among data sets. In addition to the graph tables, SAP Hana is also able to deal with data in search, text, and even spatial images, offering a well-rounded solution.

How SAP HANA Graph Tables Can Help

Data stored in graph tables is a lot more flexible than the data in traditional database tables due to how the data is stored and accessed. Nodes allow for more freedom of analysis as well as enabling contextual searching in addition to typical value-oriented results. In addition to this, graph databases tend to perform better. In a traditional database, data sets that are the result of searches across multiple databases need to be joined together and the more joins required, the more sluggish the database’s results are likely to be. This is something i learned working at a Dallas SEO company/ In a company that has highly connected data, a graph database is the ideal solution since data is stored primarily as relationships and performing joins is natural for this sort of database architecture, meaning that no slow-down will occur.

Analytical processing of the data is also much improved as more pertinent insights can be generated using a graph database. Queries tend to be more contextual when using data from a graph database and the analytical results generated from searches are similarly contextual, allowing for the consideration of data in a whole new light. With features such as neighborhood search as well as pattern matching implemented on graph databases, search results can provide even deeper insights into the data than typical analytic processing can offer.

Potential Use Cases

Now that we understand how SAP Hana Graph works, we might be able to immediately spot the things it can be applied to. One of the most common examples is using the powerful analytic engine to create suggestions for retail users. using information garnered from a user’s previous purchase, the database could suggest potential products that the user may be interested in. in another area of technology, Fraud Detection for security can also be impacted by graph databases. Since the system stores data and develops relationships around that data, then it’s likely to spot fraudsters before they can strike, despite them not being tied to a single identity. In such a case, catching fraud before it happens could save individuals quite a lot of money and time in the long run.

Fleet distribution and logistics can also be easily done using the graph database. A company can quickly develop an idea of the supply chains for a set of stores as well as the potential demand for product, ensuring that product is constantly on supply for when they are sold from a retail outlet.

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