NoSQL is a comparatively newer approach to enterprise database management, which has the capacity to accommodate a huge volume of structured and unstructured data. The various formats of such database management include the key-value pair, columnar, document, and graph, etc.
NoSQL is synonymous to ‘not only SQL,’ which is now deemed to be a more effective alternative to the conventional relational databases (RDBMS) with which the data is placed in rows and tables and the data schema has to be carefully designed before building the database. Now, the NoSQL databases are largely useful while working with enormous data sets and distributed data.
Relational Database Management Systems vs. Not only SQL
The term NoSQL can be generically used for the modern-day databases which predate RDBMS, but it more specifically refers to the databases which were built in the early years of the 2000s with an objective of handling big-scale databases in the cloud and cluster computing, etc. In such web applications, the need for scalability and quick performance outweighed the scope for consistency with rigid data as managed by the RDBMS.
Noticeable at the first point, NoSQL database systems don’t need to follow any relational schemata. Huge data management organizations like Amazon and Google started using NoSQL databases to better focus on their narrowed down operational goals and to deploy supplementary relational databases where there is a necessity for top-notch data consistency.
The first-generation NoSQL databases for cloud and web applications were primarily focusing on certain characteristics of data management. Slowly, it evolved with the ability to manage huge volumes of data and quick distribution of data across clusters. The developers who were focused on web systems and cloud were also looking forward to creating flexible data schema or no schema, which will help in enabling faster changes to the applications which are updated continually. The primary classifications of the NoSQL architecture are as follows.
4 NoSQL classifications
– 1) Key-value store
Key-value databases are very simple models of databases which pair a unique key against the value. As this model is so simple to structure, it may also further lead to the development of key-value pairs, which ensures high performance and high scalability for session management. It Implementations of key-value stores differ based on their work orientation with the RAM, disk drives, or solid-state drives.
– 2) Document DBs
– 3) Wide-column data stores
In this mode of database storage, the data is organized in columns instead of rows. The concept is used both in SQL as well as NoSQL databases. Wide-column data stores can be used to query for huge volumes of data and can ensure a faster performance than RDBMS. Wide-column stores can also be used in catalogs, recommendation engines, fraudulence detection, and other major data processing applications as listed at RemoteDBA.com.
– 4) Graph data stores
Graph stores can organize data as data nodes. It is more like records in the relational database concept as well as edges representing the connection between different nodes. The graph system stores information about the relationship between the nodes, which further supports a richer data relationship representation. Unlike the RDBMS model of the strict schema, the graphical data model can flexibly evolve with the user over time.
MongoDB as a NoSQL option
MongoDB has become so popular lately as an open-source DBMS, which is more of a kind of a document-oriented database system. It used a dynamic schema to store business data, which is much more flexible and scalable compared to the RDBMS systems. One major reason why MongoDB is scoring high among the new-generation databases is its ability to calculate with MapReduce and the scope of largely distributed key-value data stores.
As a front-line NoSQL DB, MongoDB offers many benefits to the users. Apart from its scalability to handle from minimal to huge bundles of data, it will also help organizations to streamline their data management practices and run the business application with more efficiency. MongoDB is open-source and objective oriented as we have seen below and it is also largely scalable, though simple and dynamic. Its built-on C++ and some other notable advantages of MongoDB are:
MongoDB is highly versatile in comparison with the conventional RDBMS systems. It doesn’t require any unified formats of data.
All data is stored at a single point, which makes MongoDB faster. However, it works fast only if all data handled is there in a single document.
– Sharing/load balancing
While you deal with large data volumes with a need to manage huge inflow into a distributed system, then MongoDB is of significant advantage. Sharding is the highlight of MongoDB to help meet the increasing data-related demand for horizontal scaling.
Some other random benefits also include:
– The new approach to document model helps to map application codes to the corresponding database objects better.
– Built-in capabilities along with multi-node distribution model and horizontal scaling approach put MongoDB as a priority choice for distributed databases.
– Analyzing data is also easy with MongoDB with its features like real-time data aggregation. There are also built-in options for indexing and random queries etc.
– Real-time data streaming and reporting are easier with MongoDB with its ability to recall the data spread across multiple servers.
– Changes in existing data stores can be quickly implemented with MongoDB with its flexibility.
– Strong companion for business CMS by ensuring optimum flexibility in the construction of database and management of it over time.
MongoDB is now widely used in a variety of industries to ensure a better database performance as in mobile, social infrastructure, data center management, content delivery, etc. to name a few. Considering the above factors, database administrators getting on to new enterprise database planning can consider NoSQL also as an options and the new-age database management solutions like MongoDB for your purpose.
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