Big Data, NoSQL Databases Make Their Grand Entrance

Companies are faced with storing, processing, and analyzing vast amounts of data. In contrast to relational databases, which are struggling with the new data world, NoSQL databases show their advantages in the big data age.

The rise of the IoT and other connected data sources has led to a tremendous increase in data companies collect, manage and analyze. Big data promises big insights for companies of all sizes and in all industries – it’s not about how much data a company has but what it makes of it. Used correctly, it can reduce costs, develop new products and optimized offerings, and make smarter business decisions.

However, the volume of data quickly reaching the terabyte range is no longer necessarily clearly structured but also unstructured as e-mails, documents, photos, or videos. The classic relational database management systems ( RDBMS ) with fixed structures can also cope with this wild mix, which cannot or not so easily be brought into table form, but only via detours and workarounds. However, these detours increase costs while performance decreases at the same time.

NoSQL Solutions Are More Flexible

NoSQL databases emerged as a reaction to the weaknesses of relational database management systems: At some point, the classic systems were too slow, not sufficiently scalable, and not agile enough because they could not work in a distributed manner. Apart from vertical scaling (scale-up), most RDBMS only handle rudimentary forms of horizontal scaling (scale-out). In this case, failover clustering is based on shared storage, while always-on availability groups are limited to replication. If the data volume grows, administrators have to install a larger system and, with increasing user numbers, a more powerful server. Otherwise, such a system not only becomes a bottleneck but a single point of failure.

In contrast, NoSQL solutions are more flexible in data aggregation because they work with objects rather than fixed tables, and they can be processed using an object-oriented format. JSON document – JSON is an acronym for JavaScript Object Notation – can contain different, changing data types, and the length may vary depending on the available data. Also, NoSQL databases typically scale-out, spreading data across additional inexpensive servers. Also, more users are split across more servers to keep latency low. Benchmarks show that Couchbase constantly maintains an increase in performance over any number of nodes.

To ensure high availability, Couchbase also supports unidirectional and bidirectional replication between geographically separated data centers with a standard dedicated Cross Data Center Replication (XDCR) function. On the other hand, many RDBMS require additional software for replication, which means higher license costs. Modern NoSQL data managers also keep a large part of the aggregated data in fast random access memory (RAM), speeding up analysis.

Ecommerce And NoSQL Databases

E-commerce is a good example of using NoSQL databases. Online shops keep attracting customers with special offers such as Black Friday or Cyber ​​Monday. During this time, the number of users and thus the amount of data are exploding, so high scalability is a basic requirement to handle this workload. This is where NoSQL databases show their advantages. They are finely scalable via nodes that are organized in clusters. This allows limitless flexibility since you can easily increase the cluster and reduce it after the peak.

The Classic JOIN Eats Up A Lot Of Time

The SQL operator JOIN can become a real brake on performance in complex analyses. It is necessary in classic RDBMS because, for more extensive evaluations, several relational tables usually have to be connected via key indices – only in rare cases is all the data to be evaluated located in a single table. On the other hand, Document databases can store the data from multiple relational tables in a single JSON object.

JSON documents allow nesting and can therefore also map complex structures very well. The advantage: Only a single read operation is necessary, and the analysis algorithm has access to all the data it needs. There is also no incompatibility (impedance mismatch) between application objects and JSON documents. The NoSQL database Couchbase Server stores JSON documents in “buckets,” Organizations can deploy them across multiple server nodes.

Also, developers and business analysts don’t have to learn new technology. With N1QL, users can continue to use the familiar syntax and semantics of the query language SQL to carry out search queries via JSON documents, create new databases and documents, or maintain existing documents. There are also features such as a full-text search or ad-hoc analytics. Modern databases should also be able to run either on-premises or in the cloud. Organizations today are combining the cloud services that best meet their unique needs and don’t want to be held back by proprietary hurdles.

Even with big data becoming more important, not all companies will migrate all legacy RDBMS to a modern NoSQL database in one step. This is unnecessary because NoSQL and RDBMS form a strong team: A NoSQL database, for example, acts as a high-performance cache server and takes on the new modern data types, while an RDBMS handles the classic, transactional database business. Customers thus benefit from the best of both worlds.

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