The term “NoSQL” refers to every database that doesn’t follow the SQL relational paradigm.
NoSQL broke into the mainstream in 2009. Sites like MySpace were among the first to promote it. There are wide disparities between NoSQL and earlier database architectures that might lead to record loss and performance degradation:
However, as good as NoSQL is, it lacks some key elements that enable databases to be more appealing and simple to use. As a result, several developers consider MongoDB alternatives like PostgreSQL, Redis, DynamoDB, Cassandra, and a few others as replacements.
Here are the benefits and drawbacks of some common Mongodb alternatives.
Best MongoDB Alternatives: Our Top Pick👌👌
Of MongoDB alternatives, PostgreSQL ranks high on our list. It is also called Postgres, for short, and it is a renowned, free SQL database that a large number of developers utilize.
Relational databases have evolved, with proponents pointing to the continued prevalence of SQL design and the enhanced ability to execute operation metrics on SQL databases.
With Postgres, developers can manage the bulk of workloads while still providing the scalability, stability, and dependability that modern applications demand.
Before Oracle, PostgreSQL was the premier database management platform to introduce a multi-version concurrency control (abbreviated to MVCC) capability; this was quite innovative at the time.
Because it is an object-relational management information system used for any purpose, it enables you to incorporate custom functions written in various programming languages, including C/C++, Java, and others.
PostgreSQL is an extremely powerful database that is well-suited for the financial industry. In addition, PostgreSQL is ACID compliant, which makes it an excellent choice for On-Line Transaction Processing (OLTP) applications.
Nevertheless, PostgreSQL is a fantastic choice because of its superior online transaction processing capabilities. It is an extremely powerful analytic database that connects with mathematical tools, including Matlab and R.
Apart from being a financial sector solution, PostgreSQL also has a reasonably sophisticated GIS extension known as “PostGIS,” which contains hundreds of operations for processing geodata in various formats.
As one of the benchmarks in the Open-sourced GIS field, PostGIS is extremely standard adherent and one of the most widely used GIS software packages. When used in conjunction with QGIS or GeoServer, this Open Source community supports powerful tools for dealing with geodata.
In addition to accelerating innovation and driving growth surge through customer-centric operations, many global industrial manufacturers use Postgres as a storage backup to improve supply chain efficiency and maximize logistics performance.
PostgreSQL is a dependable, long-term data repository that provides you with trustworthy storage at a reasonable price. In the industrial production industry, dependability is essential. If a manufacturing facility closes due to a breakdown, it can potentially cost millions of dollars.
PostgreSQL is built for zero downtime updates, full redundancy, and automatic failover; PostgreSQL is the appropriate solution in this situation.
To summarize, A flexible SQL database is desired over NoSQL databases, and PostgreSQL is in this group of databases.
First and foremost, it is a very powerful instrument that has undergone continuous improvement for more than 30 years; this indicates that a large number of developers are using it at this time.
The third advantage is that it is not dependent on platforms. As a result, it may be run on a wide range of media, like Google Cloud and AWS.
In addition, PostgreSQL is well-known for its rapidity. It is one of the fastest MongoDB alternatives, and it outperforms both MongoDB and MySQL.
Because Postgres is a DBMS, it has the penalty of increased complexity.
Transferring data from a NoSQL system to a PostgreSQL database is not an easy task. They are vastly different structures from one another.
Redis might be exactly the best call if you are a developer who wants to prioritize speed. It allows users to do atomic transactions on data structures such as bitmaps, hashes, strings, lists, sorted sets with a range of queries, sets hyper logs, and geospatial indexing with radius queries because it works with an in-memory database.
Redis, however, performs best when working with precisely defined and constrained datasets. If you are involved in the design of prototypes, this could be a problem.
However, if you have a decent structural plan in place that you desire to execute, such as pre-existing services, this will be a benefit.
The flexible data store Redis may be the solution for anyone with an application that demands data to be transferred across several servers, activities, and other services.
Here are a few examples of popular use cases that businesses might benefit from when collaborating with Redis:
The ability to analyze info with sub-millisecond lag makes Redis more suitable for real-time statistics, machine learning, and online marketing campaigns, processes driven by artificial intelligence compared to other Mongodb alternatives.
Location-based apps: By offering operations, sets, and geospatial indexing, Redis makes location-based services and applications much more straightforward.
With Redi, a user can offload time-consuming sorting and querying of location-based services while also utilizing a practical geo-hashing approach thanks to filtered sets in Redis.
In-memory data storage features allow Redis to handle massive quantities of real-time data, enabling extremely responsive database architectures by utilizing its in-memory digital storage features.
Caching with Redis reduces the number of database queries, which helps to minimize the volume of instances and traffic needed to run the application.
Development teams can achieve sub-millisecond performance by employing Redis for caching, increasing the throughput of their applications significantly.
In addition, because Redis’ caching layer is scalable and cost-effective, enterprises can construct these extremely responsive apps while simultaneously lowering their total costs.
- Redis is a database with exceptional performance and responsiveness.
- Applications that are already running quickly can benefit from this enhancement.
- This feature is unenabled on Windows.
- It necessitates extensive pre-planning before use, making it unsuitable for prototyping.
DynamoDB, is a product of Amazon Web Services (AWS) and a NoSQL method of storing JSON-like content with an optional schema, similar to MongoDB. DynamoDB is among the top MongoDB alternatives in that it stores data with an optional schema.
With the inclusion of “encryption at rest,” DynamoDB is deemed a step ahead of MongoDB in terms of security and performance. However, because they both use the same NoSQL format, the transition from MongoDB is smooth and uncomplicated.
DynamoDB has built-in functionality for load-balancing and automatic sharding. This enables programs to store ever-increasing volumes of data transparently.
The continuous scaling of DynamoDB is ideal for applications that must cope with expanding datasets and IOPS demands as they grow in size.
Here are some popular uses for DynamoDB that businesses can profit from when implementing the technology:
Those applications that deal with massive amounts of data and have stringent latency requirements should be avoided. As the quantity of data in your system grows, JOINs and complex SQL operations might become more time-consuming, however with DynamoDB, your operations always have consistent latency, no matter how large your database becomes.
Additionally, DynamoDB is accessed via an HTTP API; hence, it conducts authorization and authentication by using IAM roles, making it ideal for developing serverless apps.
The fundamental access structures of DynamoDB give it a fast and reliable solution for generating suggestions and delivering them to the user. It is also a relatively straightforward process to do so.
Since DynamoDB is hosted and propagated on AWS, it instantly benefits from all of the AWS platform’s features.
Migration is likewise a straightforward and straightforward process.
The NoSQL database continues to have significant difficulties, which are detailed below.
It is an AWS-only product, which means that to use it, you must be on the AWS infrastructure, unlike all other MongoDB alternatives.
There are also some scale concerns to consider, which you should be aware of.
4. Apache Cassandra: Open Source NoSQL Database
This application is written in Java on the Linux, BSD, Windows, and OS X platforms. Cassandra is a flexible and scalable non-relational open-source database that was initially conceived by Facebook and has become a property of Apache software foundation.
Cassandra has the advantages of being available at all times, having linear scale output, being operationally simple, and is easily distributed across multiple data centers and cloud availability areas.
When it comes to real-time insights, Cassandra beats other Mongodb alternatives. When designing applications that demand massively scalable structures to handle large amounts of data, it is worthwhile to take a peek at Cassandra as a data storage option to consider.
A few use cases that businesses find beneficial when collaborating with Cassandra are as follows:
Initially using MongoDB to serve Adobe’s Behance action feed, they ran into a scalability problem and switched to Apache Cassandra because of its linear longitudinal scaling capabilities. This and fault-tolerance and performance were also increased; this resulted in a 70 percent reduction in overall database expenses.
Growing time-series datasets: Apache Cassandra is used to enabling Quandl’s collaboratively curated gateway of above 8 million time-series datasets, which is driven by a distributed database system.
After evaluating several NoSQL technologies, such as MongoDB, Cassandra was selected for deployment due to its convenience, scalability, read and write performance, large user base, and failover tolerance.
Content analysis and lead identification: Social Artisan’s journey to Cassandra’s platform for content analysis and lead identification was difficult.
When they first started, they used MongoDB and, afterward, HBase. Still, they ran into management and scalability concerns on both platforms, so they switched to Cassandra in their infrastructure, which has 72 nodes.
It can scale without sacrificing its dependability.
Cassandra’s concept was designed to operate on a cluster-based infrastructure, making it simple to perform functions across several servers (for example, Hadoop).
With version 2.0, it has only restricted ACID features due to Lightweight Transactions, which have been implemented.
If users need ad-hoc queries, they would be out of luck because it does not offer this feature at present.
Additionally, it only provides limited assistance for aggregations that contain a single partition. However, even when this is possible, they could be extremely resource-intensive.
There are also some scale concerns to consider, which you should be aware of.
5. IBM Cloudant
As a global NoSQL database platform for deployment on client apps in the Internet of Things domain, IBM Cloudant is used by many enterprises.
IBM Cloudant enables second-by-second writing of unprocessed JSON data retrieved from the Internet of Things devices using the IBM Cloudant platform.
It also enables the efficient use of map-reduce to gather results from collections specified within IBM Cloudant systems, allowing for the consolidated and productive use of millions of datasets for reporting and analysis.
Compared to CouchDB or other NoSQL databases, IBM Cloudant is arguably the best solution users choose if they want to manage extremely fast writes to a system without bothering about transactional security and manage exceptionally fast writes to a database.
The CouchDB replica and multi-node needs are abstracted away by IBM Cloudant, which also provides high accessibility on its own. Map-reduce filtering will also be enabled, which will allow enormous databases to be pooled and analyzed in a very short period.
It should not be utilized in situations where you need structured data that is arranged according to a model or if you want to keep the ACID features of your database.
By utilizing IBM Cloudant, businesses can save a significant amount of money on developing their scaling capabilities. Due to various difficulties and unknown bugs, scaling CouchDB systems can be a time-consuming and expensive endeavor in the long term, requiring significant work and resources.
Through their supplied service, IBM Cloudant provides the service of the operational requirements and scaling of operating a CouchDB database. This represents significant cost savings for organizations.
The advantages of using IBM Cloudant include that unprocessed JSON data may be written quickly and on a sequential basis.
It also enables the functional use of map-reduce to gather results from compilations built across IBM Cloudant systems, allowing for the comprehensive and productive use of millions of documents for reporting and analysis.
It is pretty straightforward to utilize and integrate into a program.
Python, R, and Perl are among the programming languages that have limited support and documentation.
It is necessary to reduce the amount of time it takes to index enormous databases.
6. RethinkDB: open-source database for the real-time web
As soon as users get a grasp of the ReQL programming language, they will find that it can do most SQL-type connections and other methods that are generally only accessible in SQL-related databases.
When it comes to other MongoDB alternatives, the only feature they provide that RethinkDB does not have is a longer track record of success. Additional features, such as its fluid and sophisticated ReQL query language and the most common feature, change feeds, are available on top of the ones already available.
The geospatial data connection features offered by the company RethinkDB, including the ability to retrieve, track, and evaluate geospatial data points, such as determining the true geodesic distance between two coordinates, are particularly appealing to me.
In providing and transmitting real-time data, RethinkDB interfaces seamlessly with other systems such as Deepstream.io. Another option is for users to create their REST API that adheres to change streams in the system, provides user traffic, or pushes them to WebSocket subscribers (see below).
As a result, both of these approaches transport data far more quickly than systems such as Firebase or using commercial services such as Pusher.
Additionally, because RethinkDB sends the data instead of polling for it, they significantly cut externally and internally server traffic.
RethinkDB is far more than an ordinary database: it allows users to easily build data delivery systems instead of paying for less effective cloud-based platforms, which can be just as time-consuming to configure as RethinkDB.
RethinkDB is only getting warmed up, and I expect that it will grow and make its way into widespread enterprise use in the not-too-distant future, given that the performance remains stable.
On a concluding note, the Slack channel for RethinkDB has a thriving community in which new users may obtain help with the period of adjustment of ReQL, setup, or just to talk about RethinkDB in general.
It only takes approximately 30 seconds to complete the installation. A docker file is also included for executing it on Google Cloud, AWS, Google, or your infrastructure.
Changes can be detected, and code can be triggered as a result of these changes.
In contrast to many other Mongodb alternatives, where when the master is unavailable, the entire service is down, in RethinkDB, when the master is down, another person is made master, making it much more peer to peer in nature.
It is unable to conduct queries using its command-line interface.
This is only the database; you will need to create your authentication and user account configurations.
OrientDB is a shared graph database that has the versatility of a document database while maintaining the performance of a graph database.
The Community Edition is provided free of charge under the Apache License, whereas the Enterprise Edition is accessible only under a commercial licensing arrangement.
In addition to multi-master replication, sharding, and the flexibility required for sophisticated, current use scenarios, OrientDB is an open-source multi-model relational database management system (DBMS).
OrientDB is a Java-based open-source database system that was developed for free. It can manage your financial information efficiently and effectively. Installation is fast, and you may get up and running in a short period.
It might be very costly to replace your database management system if you exceed it. OrientDB is the alternative since it reduces the need to engage in several applications to achieve your objectives.
It is possible to use an OrientDB design that is multi-master and sharded, which means that all servers can be masters. This ensures dependability as well as broad scalability. The program is capable of running on any platform without the need for configuration or installation.
It can be used as a drop-in substitute for the standard graph databases that are currently in use. The entire server package, excluding the demo database, is approximately 2MB in size.
In most cases, or traditionally, this database has been viewed as a graph database first and foremost. Nevertheless, it is essentially a multi-model platform that supports a range of NoSQL models in different configurations (key-value, document, etc.).
Apache 2’s license is considered to be among the most permissive licenses available. OrientDB is a free service that can be used for anything.
Many different querying methods are available, but the capability to search with SQL makes it a little more comfortable to use.
It is a bad situation, but it is a fact of life. Given that few people are aware of this hidden treasure, it can be challenging at times to get community support. It also makes it difficult to convince those who shop for brand names or flamboyant marketing to join the movement.
It also implies fewer code snippets, frameworks, and libraries, which take into account. While it has decent interoperability with SQL integration and Tinkerpop, this does not change the reality that many things are done without much thinking, resulting in you having to do extra work as a programmer at times.
When it comes to choosing from MongoDB alternatives, having many options allows you to be a little more selective. To make an informed decision, you must carefully consider your platform, requirements, and use case.
Postgresql and DynamoDB, on the other hand, are the databases that I prefer. While DynamoDB is the most popular option for those currently using the AWS infrastructure, Redis and Cassandra have their own set of applications and are more appropriate for particular workloads.