Relational database (RDBMS) like SQL has been the primary model for database management during the past few decades. But today, non-relational, “NoSQL” databases are gaining prominence as an alternative model for database management. But let’s discuss why this evolution in database management is happening.
SQL (Structured Query Language) is the standard programming language used to communicate with a relational database. You can use SQL for different database operations. It is used to manage, store, and retrieve data in relational databases through applications and queries either on the same computer or over those on the network. An SQL server consists of a relational database which comprises of a set of tables containing data with predefined categories or columns. It contains structured data like names, email addresses, and phone numbers. A relational database matches data by using common characteristics found in the dataset and the resulting group is termed as Schema.
As we now need to handle immense amount of data of different categories, the “one size fits all” approach of SQL is in question. This has led to the emergence of NoSQL commonly referred to as “Not Only SQL”. With NoSQL, unstructured data can be stored across multiple processing nodes and it does not require fixed table schemas, usually avoids join operations, and typically scales horizontally.
NoSQL – A Substitute for the Limitations of SQL?
In present day, as we are handling s humongous amount of data, data being organized and well-structured actually creates a problem, especially at extremely large volumes. The structured approach of RDBMS database like SQL slows down performance as data volume or size gets bigger and it is also not scalable to meet the needs of Big Data.
So NoSQL was conceived as a completely different framework of databases that allows for high-performance, agile processing of information at a much bigger scale. This is the database well-adapted to the high demands of big data. The new version of NoSQL runs the database MongoDB, which stores unstructured data. This means that you don’t need to know in advance exactly what kind of data you’ll be collecting and storing. You can collect a lot more data of different kinds and can access and analyze data much faster.
NoSQL is centered on the concept of distributed databases, where unstructured data may be stored across multiple processing nodes, and often across multiple servers. This distributed architecture allows NoSQL databases to be horizontally scalable; as data continues to explode, just add more hardware to keep up, with no slowdown in performance.
Benefits of NoSQL
Earlier with relational database or RDBMS, database administrators always relied on scaling up or buying bigger, expensive, multiple servers as database load increased rather than scaling out or distributing the database across multiple hosts. The new breed of NoSQL databases are designed to expand transparently and horizontally to take advantage of new nodes, and they’re usually designed with low-cost commodity hardware in mind. For NoSQL, servers can be added or removed from the data layer without application downtime.
Bigger Data Handling Capability
RDBMS capacity has been growing to match the increase in volumes of data, but the limitations of data volumes that can be handled by a single RDBMS are intolerable for some enterprises. Hadoop, an enabler of certain types of NoSQL distributed databases, allow data to be spread across thousands of servers with little reduction in performance and it outstrips that which can be handled by the biggest RDBMS.
Maintaining NoSQL Servers is Cheaper
Maintaining high-end RDBMS systems is expensive and can be only done with the assistance of expensive, highly trained DBAs. On the other hand, NoSQL databases require less management. Features like automatic repair, easier data distribution, and simpler data models make administration and tuning requirements lesser in NoSQL.
Lesser Server Cost
NoSQL databases typically use clusters of cheap commodity servers to manage the exploding data and transaction volumes, while RDBMS tends to rely on expensive proprietary servers and storage systems. So the storing and processing data cost per gigabyte in case of NoSQL can be many times lesser than the cost of RDBMS.
No Schema or Fixed Data model
Data can be inserted in a NoSQL database without first defining a rigid database schema. So the format or data model being inserted can be changed any time, without application disruption. This provides immense application and business flexibility. On the contrary, change management is a big headache in SQL. Here, even minor changes to the data model have to be carefully managed and may necessitate downtime or reduced service levels.
Integrated Caching Facility
In order to increase data output and performance advance NoSQL techniques cache data in system memory. This is in contrast to SQL database where this has to be done using separate infrastructure.
Limitations of NoSQL
Though NoSQL database has generated a lot of enthusiasm, but there are several obstacles it has to overcome it becomes appealing to mainstream companies.
NoSQL alternatives and solutions are still in nascent and pre-production stages and many key features are yet to be implemented.
Customer support is also better in RDBMS systems like SQL and vendors provide a higher level of enterprise support. In contrast, NoSQL system support is provided by small start-up companies without the global reach, resources, or credibility of Oracle, Microsoft, or IBM, the big names associated with SQL.
NoSQL databases have evolved to meet the scaling demands of modern Web 2.0 applications and are oriented to meet the demands of these applications. They offer few facilities for ad-hoc query and analysis. It is much easier to code an SQL query, but in NoSQL even a simple query requires significant programming expertise and commonly used BI tools do not provide connectivity to NoSQL.
SQL and NoSQL have been great inventions over time in the area of data management and have been used to keep data storage and retrieval optimized and smooth. It’s still hard to criticize one and completely go with the other option. Both technologies are best in what they do and it is up to a developer to put them to better use depending on the business situations and needs. Though NoSQL databases are becoming important part of the database landscape, however, enterprises should proceed with caution and be aware about the legitimate limitations associated with these databases.