The world of data is constantly changing and evolving every second. This in turn has created a completely new dimension of growth and challenges for companies around the globe. By accurately recording data, storing, updating and tracking them on an efficient and regular basis, companies can address their challenges on one hand and make use of the immense potentials offered by this sector on the other hand.
A database management system stores, organizes and manages a large amount of information within a single software application. The use of this system increases efficiency of business operations and reduces overall costs.
In another words…
A database is a collection of data stored in an orderly manner. To run a system efficiently, you’d need an effective memory of the past and present records that went into and/or came out of that particular system. The same applies to a business or organization that would require the cooperative efforts of a number of individuals. For this purpose, companies, large and small, and organizations like hospitals, schools, and universities utilize a very useful method of acquiring, assembling, and sharing the data in systematic ‘Entities’ that are stored within different types of databases.
What Are Databases Used For?
Databases are used to keep the mountains of data collected in an orderly manner and easily accessible to an authorized user. They can important for a business’s growth in numerous ways:
- Allows a business to make more informed business decisions.
- Efficiently store and retrieve related information.
- Helps analyze and aggravate business data.
- Collect and store crucial customer data from different applications.
- Delivers data-driven timely, personalized applications, and real-time analytics.
- Ensures accurate, reliable, and immediate access to crucial business data that can be used by different business departments to comprehend data patterns, generate reports, predict future trends.
- Often data Is mapped through hierarchical databases used by legacy systems to relational databases used in the data warehouses.
Types of Databases Explained:
Databases are widely divided into two major types, namely, Relational or Sequence Databases and Non-relational or Non-sequence databases. These may be used by an organization individually or combined, depending on the nature of the data and the functionality required.
But we’ll talk specifically about NoSQL in this particular blog.
Non-relational databases are more forgiving in their structure and form than relational databases. Instead of tables with columns and rows, they have collections of different categories like users and orders, for example. These collections are illustrated by documents. So, there can be multiple documents in one collection and they can or cannot follow any particular pattern or schema.
A document can have a name, address, and product in a collection; at the same time, another document can have just a name and product in the same collection as there are no particular schema to these documents. Also, different collections might not necessarily have relations among them.
The different types of non-relational databases are:
This type only stores and provides quick and simple knowledge regarding key-value pairs. This is a simple and easy way to store and access the data. Some examples are Amazon DynamoDB and Redis.
Wide Column Stores
This type can be also called a multidimensional key-value store as it stores and manages humongous amounts of data in tables or multiple columns, each column of which can act as a record. This helps in the scaling of multiple petabytes of data. Notable examples are Scylla, HBase, and Cassandra.
Here, the uniform structure is not a necessity for records to have. They can have a wide array of types and values and all of them can be nested. The data gets stored in JSON documents, and these documents resemble those of key-value and wide-column. Some of the most famous NoSQL databases fall into this category, namely, Couchbase and MongoDB.
They are distinguished from document stores in a way that they help in making the data available by simple text-based searches. Some examples are Solr, Splunk, and Exasticsearch.
Graph databases show the connections between different data points. They are used mostly when there is a requirement of analyzing different types of data and their relationship with each other. These are represented in the form of a network of objects or nodes that are related. Examples are the Datastax Enterprise Graph and Neo4J.
Why should you use a NoSQL database?
NoSQL databases are a great fit for many modern applications such as mobile, web, and gaming that require flexible, scalable, high-performance, and highly functional databases to provide great user experiences.
- Flexibility: NoSQL databases generally provide flexible schemas that enable faster and more iterative development. The flexible data model makes NoSQL databases ideal for semi-structured and unstructured data.
- Scalability: NoSQL databases are generally designed to scale out by using distributed clusters of hardware instead of scaling up by adding expensive and robust servers. Some cloud providers handle these operations behind-the-scenes as a fully managed service.
- High-performance: NoSQL database are optimized for specific data models and access patterns that enable higher performance than trying to accomplish similar functionality with relational databases.
- Highly functional: NoSQL databases provide highly functional APIs and data types that are purpose built for each of their respective data models.
Among all NoSQL databases MongoDB is one of the most popular NoSQL databases out there today and the question is why it is so? Lets explore MongoDB each part in depth
What is MongoDB?
As a definition, MongoDB is an open-source database that uses a document-oriented data model and a non-structured query language. It is one of the most powerful NoSQL systems and databases around, today.
Being a NoSQL tool means that it does not use the usual rows and columns that you so much associate with the relational database management. It is an architecture that is built on collections and documents. The basic unit of data in this database consists of a set of key–value pairs.It allows documents to have different fields and structures. This database uses a document storage format called BSON which is a binary style of JSON documents.The data model that MongoDB follows is a highly elastic one that lets you combine and store data of multivariate types without having to compromise on the powerful indexing options, data access, and validation rules. There is no downtime when you want to dynamically modify the schemas. What it means that you can concentrate more on making your data work harder rather than spending more time on preparing the data for the database.
Architecture of MongoDB NoSQL Database
Database: In simple words, it can be called the physical container for data. Each of the databases has its own set of files on the file system with multiple databases existing on a single MongoDB server.
Collection: A group of database documents can be called a collection. The RDBMS equivalent to a collection is a table. The entire collection exists within a single database. There are no schemas when it comes to collections. Inside the collection, various documents can have varied fields, but mostly the documents within a collection are meant for the same purpose or for serving the same end goal.
Document: A set of key–value pairs can be designated as a document. Documents are associated with dynamic schemas. The benefit of having dynamic schemas is that a document in a single collection does not have to possess the same structure or fields. Also, the common fields in a collection’s document can have varied types of data.
What makes it different from RDBMS?
You can directly compare the MongoDB NoSQL with the RDBMS and map the varied terminologies in the two systems: The RDBMS table is a MongoDB collection, the column is a field, the tuple/row is a document, and the table join is an embedded document. The typical schema of a relational database shows the number of tables and the relationship between the tables, but MongoDB does not follow the concept of relationship.
Go through the following table to understand how exactly an expert NoSQL database like MongoDB differs from RDBMS. This blog has elucidated nine different comparisons between the two.
Important Features of MongoDB
- Queries: It supports ad-hoc queries and document-based queries.
- Index Support: Any field in the document can be indexed.
- Replication: It supports Master-Slave replication. MongoDB uses the native application to maintain multiple copies of data. Preventing database downtime is one of the replica set’s features as it has self-healing shard.
- Multiple Servers: The database can run over multiple servers. Data is duplicated to foolproof the system in the case of hardware failure.
- Auto-sharding: This process distributes data across multiple physical partitions called shards. Due to sharding, MongoDB has an automatic load balancing feature.
- MapReduce: It supports MapReduce and flexible aggregation tools.
- Failure Handling: In MongoDB, it’s easy to cope with cases of failures. Huge numbers of replicas give out increased protection and data availability against database downtimes like rack failures, multiple machine failures, and data center failures, or even network partitions.
- GridFS: Without complicating your stack, any sizes of files can be stored. GridFS feature divides files into smaller parts and stores them as separate documents.
- Schema-less Database: It is a schema-less database written in C++.
- Document-oriented Storage: It uses BSON format which is a JSON-like format.
Why do you need MongoDB technology?
This technology overcame one of the biggest pitfalls of the traditional database systems, that is, scalability. With the ever-evolving needs of businesses, their database systems also needed to be upgraded. MongoDB has exceptional scalability. It makes it easy to fetch the data and provides continuous and automatic integration. Along with these benefits, there are multiple reasons why you need MongoDB:
- No downtime while the application is being scaled
- Performs in-memory processing
- Text search
- Graph processing
- Global replication
Moreover, businesses are increasingly finding out that MongoDB is ticking all the right boxes when it comes to meeting the business requirements. Here is how:
- MongoDB provides the right mix of technology and data for competitive advantage.
- It is most suited for mission-critical applications since it considerably reduces risks.
- It increasingly accelerated the time to value (TTV) and lowered the total cost of ownership.
- It builds applications that are just not possible with traditional relational databases.
Listed below are the six aspects through which you can realize the benefits of MongoDB:
Distributed Data Platform: Throughout geographically distributed data centers and cloud regions, MongoDB can be run ensuring new levels of availability and scalability. With no downtime and without changing your application, MongoDB scales elastically in terms of data volume and throughput. The technology gives you enough flexibility across various data centers with good consistency.
Fast and Iterative Development: Changing business requirements will no longer affect successful project delivery in your enterprise. A flexible data model with dynamic schema, and with powerful GUI and command line tools, makes it fast for developers to build and evolve applications. Automated provisioning enables continuous integration and delivery for productive operations. Static relational schemas and complex operations of RDBMS are now something from the past.
Flexible Data Model: MongoDB stores data in flexible JSON-like documents, which makes data persistence and combining easy. The objects in your application code is mapped to the document model, due to which working with data becomes easy. Needless to say that schema governance controls, data access, complex aggregations, and rich indexing functionality are not compromised in any way. Without downtime, one can modify the schema dynamically. Due to this flexibility, a developer needs to worry less about data manipulation.
Reduced TCO (Total Cost of Ownership): Application developers can do their job way better when MongoDB is used. The operations team also can perform their job well, thanks to the Atlas Cloud service. Costs are significantly lowered as MongoDB runs on commodity hardware. The technology gives out on-demand, pay-as-you-go pricing with annual subscriptions, along with 24/7 global support.
Integrated Feature Set: One can get a variety of real-time applications because of analytics and data visualization, event-driven streaming data pipelines, text and geospatial search, graph processing, in-memory performance, and global replication reliably and securely. For RDBMS to accomplish this, there requires additional complex technologies, along with separate integration requirements.
Long-term Commitment: You would be staggered to know about the development of this technology. It has garnered over 30 million downloads, 4,900 customers, and over 1,000 partners. If you include this technology in your firm, then you can be sure that your investment is in the right place.
Thank you for your valuable time, keep learning!