August 10, 2021
As the globe grapples to deal with heaps of structured and unstructured data collected by them, the smart way to manage this bulk of data and extract the insightful information out of it is through big data solutions and database services. One popular name in the world of big data technologies is MongoDB.
MongoDB has been a popular open-source database management system that leverages a document-oriented database model. Released in the year 2009, as a non-relational database and NoSQL database program, MongoDB has been utilized for big data applications, supporting various forms of data. It uses collections and documents, instead of tables and rows. It is written in C++ and has been considered ideal for mobile apps, real-time analytics, IoT-based applications, etc. MongoDB has been figuring as one of the most wanted database skills among software developers worldwide as of 2021.
Some of the key features of MongoDB include effective storage of data, ad-hoc queries for optimized and real-time analytics, high insert rate for cases with a high write load, appropriate indexing for good query executions, replication for improved data accessibility and stability, sharding and load balancing.
Even with its salient features and growing community, there are certain areas where it lacks – high data consumption due to denormalization, lack of functions to bind logic, not ACID-compliant, etc. Even though NoSQL has the best of database features, there are certain areas that are not taken care of, which is why developers are trying to search for other alternatives.
Through this article, we are attempting to lay down a few popular alternatives and competitors of MongoDB that can fulfill your requirements.
Here is an overview of each of these MongoDB competitors and why they are so popular today:
Considered as one of the leading open-source databases, PostgreSQL is a free DBMS that is also known as Postgres. It focuses on extensibility and conformity to SQL. It has earned a lot of popularity owing to its over two decades of experience. It offers support for multi-version concurrency control (MVCC), SQL for relational, and JSON for non-relational queries. It facilitates the addition of customized functions for different languages like Java, C++, etc.
Good Read: PostgreSQL vs MySQL: A Detailed And In-depth Comparison
PostgreSQL supports client-server network architecture and offers high-end accessibility to standby servers. It is ANSI-SQL2008 compatible and shows great performance with structured and unstructured data. It can execute smoothly on almost all platforms and is well known for its speed.
Apache Cassandra is a great open-source NoSQL database that is competent to manage a large amount of data in a speedy and effective manner. For applications that demand more scalability and high-end availability, Cassandra is considered ideal. It gives support to replicate amongst varied data centers and multiple nodes to give a fault-tolerant system. It showcases linear scale performance, simplified data distribution, and cloud accessibility zones.
Written in Java, originally developed by Facebook, and now owned by Apache, Cassandra offers a smooth network flow since there are independent nodes in clusters. It offers you the flexibility to opt from synchronous and asynchronous replication features for all updates. Hence it offers a great deal of security and stability with equal effectiveness.
Good Read: Cassandra vs MongoDB: Comparing Two Popular NoSQL Databases
Powered by IBM, Cloudant is a cloud-based, distributed DBMS that is non-relational. It depends on the Apache-backed CouchDB project and BigCouch project. It is ideal for mobile, web, IoT-driven, and serverless applications. It is a full management JSON document that manages and maintains data accessibility at all levels.
It enhances applications through its key-value pairs, full-text search feature, MapReduce, queries supporting geospatial data, and other features. It lessens dependency internally since data nodes are deployed on clusters that are scattered across zones. There is a synchronized model that replicates data in its readable/writeable form amidst many clusters, hence becoming more durable.
Managed by Amazon, DynamoDB is a NoSQL document database service that is fully managed and supports key-value, document data structures. It offers great performance irrespective of the scale it lies in and thereby, offers reliable potential and inbuilt security features with in-memory caching. Since it is serverless, there is an automated scaling and backup facility to ensure the security of data.
DynamoDB has a seamless integration with AWS Lambda to offer triggers. It also provides secondary indexes that allow querying on any attribute, that the developer wishes to. It has good support for sharding and load balancing. This helps developers to easily handle the high storage of data. Because of its linear scalability, it is easy for managing growing data requirements.
RethinkDB is a known, open-source, DBMS system that assists in creating real-time and modern apps with ease. It has scalable and flexible features that are easy to set up and learn. Its simplistic yet powerful query language is a delight to developers as it has a seamless pairing with other peer technologies and the latest updates in databases.
It expresses relationships with the help of joins, performs API monitoring and interactive operations, and creates location-aware apps. It is very fast to install and has a Docker file to execute on Google Cloud, AWS, and other technologies. Developers can listen to changes and makes necessary changes through change listeners.
JaguarDB is a fast, scalable NoSQL database especially for IoT-based applications since it handles a huge volume of data with ease. In this tool, scaling is done instantly and hence it doesn’t take long to scale multiple nodes. There is just no migration of data and so, it hardly takes a few seconds to scale database cluster to hundreds of nodes, with lightning speed.
JaguarDB is known to be a perfect choice for faster analysis of time series data, location data, and geospatial data. It can easily scale out huge database clusters. Automated index building is done when the time series data is consumed. It is considered the best IoT database since it can handle time series and location data, all put together.
Written in Java, OrientDB is a known, open-source, NoSQL database management system that has a multi-model database. It has good support for document-based, graph, object models, without having to deploy multiple systems for different data types. It features good performance criteria, security, and scalability aspect.
Its multi-model API is comprehensive and supports faster deployment. Through TinkerPop3, there is a quick upgrade, and it provides a modern query planner. As a leading document-graph database, it helps developers unleash the real potential of graph databases with complete graph competencies along with features found in document databases.
MySQL is a completely managed RDBMS that is open-source in nature, for the deployment of cloud-native applications. It is based on SQL and leveraged for a wide range of activities such as eCommerce, data warehousing, and maintaining a web database. It is used on the web and runs on the server. There is a lot of data that needs to be handled and must be used in huge applications.
Scalability is high and so is the speed of execution. Since MySQL has been a popular one, there are many third-party applications that assist in development work. There is a lot of help and documentation available that helps novices learn the language easily. Even the installation is easy, and the availability of different GUI tools makes it interesting to work with.
Supported by Google, Firebase has been assisting organizations – ranging from startups to enterprises, to create and execute successful mobile and web apps. It is popular because of its salient feature of integrating seamlessly into the team’s preferred tools. It is recommended for its feature to enhance app development, engagement, and performance.
Firebase helps in developing Android, iOS, and web apps, with its effective tools to track analytics and reporting capabilities. It can manage real-time in the database with ease and swiftness. It can easily embed facilities like chat messaging, live streaming, etc. The Firebase database, stored as JSON objects, offers a lot of optimizations and features.
Apache CouchDB is a clustered, open-source, document-oriented database that is implemented in Erlang. It facilitates the execution of a single logical database server on multiple servers or virtual machines. It is a NoSQL database that makes use of varied formats for storing, transferring, and processing data. It works easily between physical network partitions.
ArangoDB is a native, multi-model database system, especially for graphs and much more. There is an effective support for three data models that consist of one database core and an integrated query language – AQL. It supports document and graph models and has a declarative query language that easily compares various data access patterns through a unified query. It is easy for developers to model data like documents or graphs or no data model lock-in.
It has proven to be of great scalability for database clusters across all data models. Replication of data is possible from one distributed database to another, without causing any disturbance to the data. It also showcases good security features that can assure the safeguarding of data. It has an easy cluster setup configuration.
Founded in 2015, written in Go, CockroachDB is a distributed, cloud-native, commercial DBMS that is considered apt for creating, scaling, and managing modernized, data-centric applications. It has been inspired by Google Scanner and has a standard SQL for cloud applications. Its key features include a distributed SQL system supporting ACID transactions, with full-on scalability.
CockroachDB has good support for containerized, multi-region, and multi datacentre deployments. It is considered ideal for developing applications that need availability, reliability, the accuracy of data, and a good response time. There is the least configuration and operation overhead involved. It has a flexible architecture that seamlessly distributes data and workload.
Redis is a known open-source database, an in-memory data structure store that also acts as a message broker and cache. It offers great support for various data structures like maps, sets, lists, bitmaps, spatial indices, etc. There is voluntary durability, and it is called a data structure store because keys have lists, strings, sets, and hashes.
Released about 12 years ago, Redis is considered a high performer in the field of databases with the competence to enhance the speed of current applications. It is a scalable option especially when data must be shared across varied servers, procedures, and applications.
Supported by Google and the cloud, Firestore is a popular NoSQL document database that helps in storing, synchronizing, and querying data for the applications, globally. Developers can create enriched applications through a completely managed, serverless, scalable document database.
It offers direct connectivity to the database, inbuilt live synchronization, ACID transactions, flexible security features, and smooth integration with services like BigQuery, etc. It supports the ‘pay as you go’ model and can automatically scale, based on requirements. It has a powerful query engine that helps in creating successful apps.
Written in C, C++, Erlang and Go, CouchBase is a well-known NoSQL cloud database that offers superior performance, flexibility, and scalability across different cloud platforms. It is an open-source, multi-model platform that is considered ideal for interactive apps that can be operated for concurrent users.
CouchBase displays a quick key-value store with managed cache, powerful query engine, and inbuilt indexers for swift queries. As a popular database engine and document-based database mainly for enterprise applications, it provides the N1QL language that has the SQL type feel. It has a distributed, in-memory database on the cloud, at the edge, and is considered the architect’s selection for NoSQL.
Whichever alternative we look at, there are certain organizational requirements that must be kept in focus, while selection. MongoDB has its own set of characteristics that has made it so popular today and with technology progressing rapidly, the above MongoDB alternatives have picked up the pace, competing heavily with MongoDB.
Finally, it depends upon what are the variety of tools, functions, and features available and which of them suits you best. Only then can you finalize which big data solution to choose!
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