The question that remains:
Will NoSQL graph database technology live up to the expectations?
NoSQL databases are slowly catching up with the market traction. It all started way back in 2007 when Amazon launched its NoSQL database system (Dynamo Distributed) to handle all its internal functions. In fact, Amazon was probably the first company to use the no-relational database to store its crucial and highly important corporate data. This approach proved to be beneficial and hence, the awareness to access non-relational databases started to grow. The dependency on relational databases slowly started fading down.
Why Graph Database?
Any graph comprises of 2 key elements – Node and Relationship. Any entity, be it place, thing, person, category or any other data, is represented by a Node. If you consider a general purpose graph database structure, it would allow you to model every type of scenario, be it a network of devices, population’s medical history or anything else that’s being defined by a relationship.
Understanding Graph Database
Graph database can be defined as a certain kind of database management system that works online to CREATE, READ, UPDATE, DELETE (CRUD) operations on any graph data model. In this concept, Relationships definitely take first priority. Therefore, your application need not have to infer any data connection using out-of-band processing or foreign keys.
Considering the data model being used with a graph database, it is considered to be more expressive and much simpler when compared to those used in relational and other NoSQL databases. This is where NoSQL Graph Database Technology scores big time. They are perfectly suited to be used with transactional systems (OLTP). They have been engineered with operational availability and transactional integrity in mind.
Properties of Graph Database Technology
There are basically 2 crucial properties associated with a graph database technology. Have a look:
There are certain graph databases that prefer using native graph storage being specifically designed for storing and managing graphs. On the other hand, there are also those that prefer using object-oriented or relational databases too. Speaking of the latency factor, non-native storage is always considered more latent than the other options available.
Graph Processing Engine
Also known as Index Free Adjacency, the Native Graph Processing concept is probably the most efficient way to process graph data. It is primarily due to the relationship of connected nodes that physically point to one another in the database. At the same time, Non-native graph processing also uses several other means for processing the CRUD operations.
3 Benefits of NoSQL Graph Database Technology over Relational Database Management Systems
Minimal need for ETL
With NoSQL databases, you can store data as it is (no need for encryption or processing). Document NoSQL databases provide you with the option to handle a wide range of nested or flat structures while key value stores offer you the ability to store simple and basic data structures. In fact, Oracle Remote DBA experts are turning towards NoSQL graph database technology concept because of their minimal use of ETL. In a NoSQL database, data usually takes any of these 3 formats:
- XML document
- JSON document
- Binary object that can be passed through any or a set of layers
Handling these formats in a NoSQL database minimizes the coding factor and effort to convert data from a source format to a storing format. This entire process is commonly termed as Extract, Transform and Load (ETL). This approach helps in reducing the total amount of code required for using NoSQL database. Also, in NoSQL Graph database technology, the ongoing maintenance costs are much decreased, since there are no requirements to pay for coding updates.
Total ability to scale horizontally on any commodity hardware system
NoSQL graph databases can easily handle database portioning across different servers. Therefore, if you find your data storage requirement going too high, you can easily include inexpensive servers while connecting them to the database cluster (aka horizontal scaling) in order to ensure they work as single data service. Now, if you take into account relational database, then you need to purchase expensive and highly powerful hardware to carry out the scaling part (vertical scaling). This would increase the costing part by a massive rate. Offering high availability and durability of a database through the usage of storage and inexpensive hardware is definitely the biggest assets of NoSQL Graph database technology.
Supporting unstructured text
When working with data in an enterprise, most of it is found to be unstructured. A lot of NoSQL databases have the ability to handle indexing of unstructured text, either by using the integrated set of services like, Elasticsearch or SOLR or as a sort of native feature using MarkLogic Server. The ability to manage unstructured text is a great asset for any organization. It increases information collecting capability, thus helping organizations to take better decisions within quick succession. It allows an organization to use advanced features like, multiple language support, with facetted search, work stemming support, snippet functionality, support for dictionaries, etc.
Additionally, with NoSQL graph database technology, you can also use Search Alert Actions for several data ingestion actions and extract named entities from different directories. This helps in making data perfectly organized, tagged, and categorized. It also helps in making the data searching process simpler and better.
Remember, NoSQL databases are developed in the era of Hadoop, a highly distributed file system offering a strong batch-processing environment to store and signal data storage capability while ensuring fast query and processing. With NoSQL, data processing and query tasks are now passed to several servers that make the best use of parallel processing while handling ingest as well as query workloads. Calculating and handling aggressive data-sets become easier with NoSQL database technology.
With NoSQL graph database technology, there is no requirement of a separate data warehouse system. It offers fast query handling and aggregations that allow the analysis process to be passed to database immediately with data uploading. Now, you need not have to ship the vast amount of data around a network for best analysis. As a result, everything takes place in a more organized and simpler way.