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Data is one of the most valuable assets in the business world. How information is usually stored is though the use of relational databases. In small use-cases, this type of database is functional and fast. However, as the size of the database increases and the linkages between tables become more complex, the database's performance suffers significantly. Big data ensures that there is always an immense amount of data flowing into a company's databases. Businesses want to utilize this data, but there is simply no way for a relational database to process those entries and create real-time insights.

Enter the Graph Database

Relational databases are only one type of database. Another example initially proposed in 1960, but gaining prominence as the business world adopts more Big Data analytics, is the graph database. Graph databases don't contain model data as records as in a relational database. Instead, data is modeled as entities and relationships. Entities are data structures, and links show how those data structures interact and depend upon one another. The method of storage makes it a much easier process to query the data since there's no need to search through billions of entries sequentially. Instead, a search parameter analysis tool can simply collect relationships that are associated with the query and display them.

SAP HANA and the Graph Database

SAP HANA 2.0 utilizes a hybrid system for storing entries. The data is stored in relational tables but queried as if it's a graph database. SAP HANA's ability to support both row and column-based storage allows relationships to be built in two dimensions. The built-in processing engines extend the graph database search functions. By utilizing the text-based and spacial engines, a user can get access to data at a much faster rate.

Benefits of SAP HANA's Hybrid Processing

Because of the adaptability and agility of SAP HANA's architecture, a business can immediately notice certain benefits:

  •       Performance Increases: The lack of using relational searches means that records can be collected and displayed at a much faster rate. As a relational database becomes more complicated because of its joins, the system slows down. Thanks to the use of the graph system for searching, the cross-database relationships don't slow the user's search speed down.

  •       Better Insights: Relational databases can offer a skilled user the insights they need by performing relevant searches. However, the graph search function is a far more useful one if the business isn't aware of how the data relates to one another. Contextual relationships are easier to spot through a graph interface than through a relational database.

  •       Added Flexibility: Since data can easily be extracted and manipulated, the graph system allows for a more agile database. The use of a graph database translates into searches that can be executed, not just based on the value of records, but based on the context in which those records relate to one another.

Typical Use-Cases for Graph Databases

Highly-connected data forms the core use case for a Graph database, but in addition to this application, a few others stand out as possible ways to utilize a graph database including:

  •       Supply Chain Logistics: A fleet of vehicles within a supply chain can send data to a central processor, which can then combine it with the information from the local and regional warehouses. SAP HANA's graph engine can use this data and compare it to the user data in SAP ERP to come up with the most efficient manner of distributing goods.

  •       Retail Recommendation Engines: The sheer amount of data that comes from pint-of-sale and cashless transactions can offer valuable insight into what consumers are buying. By utilizing that payment information and combining it with the customer data from SAP, a business can anticipate the products that a user may enjoy even without doing the market research that is typical before bringing a new product to market.

  •       Fraud and Identity Detection: Data from multiple sources can be used to build a picture of a consumer. All of their preferences appear in a single place. By developing a buying profile for that consumer, it can be a simple matter to see if a consumer bought something that didn't fit the buyer's profile. While sometimes it might just be a one-off purchase, more often than not, it could alert a consumer to fraud regarding his or her financial instruments in real-time before any damage is done.

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