12 Best NoSQL Databases Reviewed For 2024
Best NoSQL Databases Shortlist
Below is my recommended list of NoSQL databases, along with a summary of what they do best:
Our one-on-one guidance will help you find the perfect fit.
With so many different NoSQL databases available, figuring out which is right for you is tough. You know you want to invest in a non-relational database, your data sources, but need to figure out which tool is best to help you out. I've got you! In this post I'll help make your choice easy, sharing my personal experiences using dozens of different NoSQL databases with large teams and projects, with my picks of the best NoSQL databases.
This article will help you select the best options available to you today, by considering each tool's unique strengths and answers to common questions.
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Best NoSQL Databases Summary
| Tools | Price | |
|---|---|---|
| IBM Cloudant | From $1/month | Website |
| RavenDB | From $789/core/year | Website |
| CASSANDRA | No paid plan | Website |
| OrientDB | Enterprise edition from $5,970 | Website |
| Oracle Coherence | Fixed license from $15,456/year | Website |
| Couchbase Capella | From $0.28/hr per node | Website |
| Neo4j | Pricing upon request | Website |
| Amazon DynamoDB | From $1.25/million requests; read operations from $0.25/million requests. | Website |
| MongoDB | Dedicated from $57/month or serverless from $0.10/million reads | Website |
| Elasticsearch | From $95/month | Website |
| Redis | From $7/month or $0.881/hour | Website |
| HBase | No paid option | Website |
Compare Software Specs Side by Side
Use our comparison chart to review and evaluate software specs side-by-side.
Compare SoftwareHow To Choose a NoSQL Database
As you're shortlisting, trialing, and selecting NoSQL databases, consider the following:
- What problem are you trying to solve - Start by identifying the NoSQL database feature gap you're trying to fill to clarify the features and functionality the tool needs to provide.
- Who will need to use it - To evaluate cost and requirements, consider who'll be using the platform and how many licenses you'll need. You'll need to evaluate if it'll just be the data team, or the whole organization that will require access. When that's clear, it's worth considering if you're prioritizing ease of use for all, or speed for your technical power users.
- What other tools it needs to work with - Clarify what tools you're replacing, what tools are staying, and the tools you'll need to integrate with. This could include your existing data infrastructure, various data sources, and your overall tech stack. You might also need to decide if the tools will need to integrate together, or alternatively, if you can replace multiple tools with one consolidated NoSQL database.
- What outcomes are important - Consider the result that the tool needs to deliver to be considered a success. Think about what capability you want to gain, or what you want to improve, and how you will be measuring success. You could compare NoSQL database features until you’re blue in the face, but if you aren’t thinking about the outcomes you want to drive, you could be wasting a lot of valuable time.
- How it would work within your organization - Consider the solutions alongside your workflows and data management methodology. Evaluate what's working well, and the areas that are causing issues that need to be addressed. Remember every business is different — don’t assume that because a tool is popular that it'll work in your organization.
Best NoSQL Database Reviews
Here are my recommendations of the 12 best NoSQL databases and the scenarios where I think each one performs best.
IBM Cloud is a fully managed, distributed database that runs on a serverless schematic, meaning you don’t have to manage server configuration and scaling. This lets you build and run databases without worrying about the backend.
Why I Picked IBM Cloudant: I picked IBM Cloudant because it’s serverless and incredibly secure. You only pay for the resources you use, and you can scale up or down as your data needs change. For security, I was impressed by IBM Key Protect, which gave me total control and visibility over encrypted keys.
IBM Cloudant Standout Features and Integrations:
Features that I liked include the powerful API, which lets you store JSON documents using an HTTP API. Although MongoDB and Couchbase Capella can do the same, neither match the sophisticated security of IBM Cloudant.
Integrations that are possible with IBM Cloudant include IBM App Connect, which helps you connect all of your applications and data in any environment. Cloudant natively integrates with IBM Cloud Log Analysis, so you can analyze logged information such as the number of page views for a web application.
Pros and cons
Pros:
- Free version available
- Serverless schema for easy configuration
- Comprehensive security
Cons:
- Slow time to index large databases
- Some documentation is out of date
RavenDB is a multi-document database that supports ACID (Atomic, Consistency, Isolation, and Durability) transactions. This helps prevent data from accidentally falling into an inconsistent state. The platform is used by nationally recognized companies such as Toyota, Verizon, and Medicaid.
Why I Picked RavenDB: Aside from the visually appealing interface, I like that RavenDB supports ACID transactions. This ensures transactions can only change data in predictable ways and that those changes are always saved, even during a system crash.
RavenDB Standout Features and Integrations:
Features that make RavenDB shine include for a wide variety of network environments. You can use RavenDB in the cloud with AWS and Azure and on-premise with Windows, Linux, MacOS, and more.
Integrations with RavenDB are possible through pre-built tools and libraries, such as Fast Report and Bold BI, the latter of which lets you embed Analytics into your software applications, helping you gain insight into your business and products.
Pros and cons
Pros:
- On-premise and cloud versions
- ACID transactions ensure greater data consistency and time-saving
- Easy-to-use interface
Cons:
- Lacking community support and documentation
- Enterprise version is expensive
Apache Cassandra is an open-source, wide-column database that supports hybrid cloud and on-premises database replicating and audit logging. Because the platform is freely available and used by thousands of companies, I think it is a great solution for businesses that want to manage large active data sets at low cost.
Why I Picked Apache Cassandra: Cassandra stands out to me as a powerful wide-column database with lots of scalability. You can simply add more servers horizontally to the cluster as your data needs get bigger. Also, Cassandra uses a column-family data model, so it’s accessible to traditional relational database users.
Cassandra Standout Features and Integrations:
Features that impressed me were its ability to support high workloads and maintain data consistency. Cassandra has been tested on large clusters of up to 1,000 nodes, with better performance than similar platforms like MongoDB. I also like the hinted handoff feature, which corrects for missed data writes if a replica node is unavailable to accept a change. This maintains data consistency across multiple servers.
Integrations include a variety of pre-built connectors, such as New Relic, IRI Voracity, DbVisualizer, Sematext Cloud, Flex83, Retool, and DbSchema. You can connect Cassandra to Google Cloud through the Datastax Astra DB platform, which I found to help streamline Cassandra's steep learning curve with user-friendly development tools.
Pros and cons
Pros:
- Horizontal scaling to accommodate growing data needs
- Can handle large volumes of unstructured data
- Support for hybrid cloud (private and public) and on-premises
Cons:
- Requires periodic manual maintenance
- No ad-hoc queries
OrientDB is an open-source, multi-model database. It supports all types of NoSQL database, including graph, key-value, object, and document models. So your business can build and manage multiple NoSQL databases on one system, with no need to invest in multiple products.
Why I Picked OrientDB: I chose OrientDB because its engine is built from the ground up to natively support the full functionality of each NoSQL database type. I found this to be a better approach than merely replicating the interfaces of other models, which achieves limited speed and scalability.
OrientDB Standout Features and Integrations:
Features that make OrientDB great are the multi-master architecture and Java support. The multi-master distributed architecture lets you distribute multiple databases across multiple servers and then configure those servers individually. Also, since OrientDB is written entirely in Java, I found it easy to run on different platforms without manual installation.
Integrations OrientDB has its own Java Database Connectivity (JDBC) drivers, which lets you integrate many pre-built tools into the platform. Some of the most popular native integrations are Teleporter, Orient DBL, Spark Connector, and Neo4j Importer.
Pros and cons
Pros:
- Free community edition is great for small businesses
- Easy to setup and use
- Native support Graph, Key-Value, Document, and Object databases
Cons:
- Small community and limited documentation
- High upfront cost for Enterprise edition
Oracle Coherence is an in-memory, key-value database that delivers strong scalability and performance for enterprise applications. Using low-latency data storage to read, write, and query latencies allows businesses — running multiple applications in different languages — to quickly maintain data consistency in real-time.
Why I Picked Oracle Coherence: What I love about Oracle Coherence is its support for asynchronous event streaming. This means you can incorporate event models into your event-driven architecture, allowing for efficient communication between microservices like servers and clients.
Oracle Coherence Standout Features and Integrations:
Features that really shine in Oracle Coherence are the data source integration and multi-site data federation. Data source integration helps keep the data you are working on up to date by replicating any changes you make to a HotCache, and multi-site data federation helps keep your cached data synchronized with all your clusters.
Integrations for Oracle Coherence include Spring, which enables you to configure the Coherence cache for applications running on Spring. Coherence also has native support for Oracle WebLogic Server and Oracle NoSQL database.
Pros and cons
Pros:
- Strong community support and documentation
- Many features to maintain data consistency
- Free to use
Cons:
- Difficult upgrade process
- Limited default security
Couchbase Capella is a cloud database platform, combining the speed and flexibility of a NoSQL database with the benefits of an SQL database. It also has an app development solution called Capella App Services, which you can use to design and deploy IoT, mobile, and edge applications.
Why I Picked Couchbase Capella: I chose Couchbase Capella because it is accessible to those who are familiar with SQL databases. It uses the SQL++ query language for operations and analytics. I like how this streamlines the move from traditional relational databases to non-relational databases, with familiar features like ACID transactions and hierarchical schemas.
Couchbase Capella Standout Features and Integrations:
Features that make Couchbase Capella stand out include the Couchbase Playground, which lets you run snippets of code across multiple languages in a secure sandbox. This gave me a better idea of what Capella could do without the free trial period. Also, Capella App Services is easy to use, with preset backend infrastructure to fast-track the development process.
Integrations include native support for Confluent Cloud, Workato, and Microsoft Azure.
Pros and cons
Pros:
- Great value for money
- Built-in app development and deployment service
- Combines the benefits of SQL and NoSQL databases in one platform
Cons:
- Limited integrations
- Steep learning curve
Neo4j is a graph-based NoSQL database used to develop applications, create and deploy Artificial Intelligence (AI) and Machine Learning (ML) pipelines, and perform detailed analytics. It is particularly suited to data scientists, application developers, and similar enterprises. With the intuitive graph-based user interface, establishing patterns and relationships with large amounts of data is easy.
Why I Picked Neo4j: Neo4j grabbed my interest because of its clear user interface and advanced graphing capabilities. I found it was helpful for uncovering hidden insights in complex data relationships, where trends and patterns may not be obvious right away. I was also impressed by how easily the platform scales horizontally. This is thanks to Autonomous Clustering, which automatically copies your datasets to the most optimal servers based on your rules and guidelines.
Neo4j Standout Features and Integrations:
Features that I like in Neo4j are the advanced analytics and ML solutions. The platform offers a range of visualizations, graph algorithms, and ML models, which you can use to create network maps and identify relationships between different objects.
Integrations include a range of native universal connectors, such as Apache Spark, Kafka Connect, and the Neo4j Data Warehouse connector.
Pros and cons
Pros:
- Surprisingly easy to learn and use
- Strong community and documentation
- ACID transactions ensure database operations happen across all nodes
Cons:
- New versions can introduce new errors
- Custom pricing for Enterprise edition
Amazon DynamoDB is a NoSQL database that supports both document and key-value data models. It is a cloud-based service that lets you store items, tables, and attributes. You can use the service on pay-as-you-go or provisioned pricing model, whichever suits your workload.
Why I Picked Amazon DynamoDB: Aside from being a fully managed NoSQL database with high availability and durability, what I like the most about Amazon DynamoDB is its Amazon Web Services (AWS) integration. This helps you do more with your data, for example, by letting you import and export from your S3 buckets. I also appreciated the ease of use, thanks to excellent documentation and a user interface that is clean and intuitive.
Amazon DynamoDB Standout Features and Integrations:
Features that I like include the DynamoDB Accelerator (DAX), an in-memory cache that lets you process millions of requests per second. This minimizes the time your database spends on each query. I also found DynamoDB much easier to set up compared to MongoDB, as the AWS cloud is easier to navigate.
Integrations are native with other AWS services. From Amazon S3 to Cognito and Redshift, there is a lot you can add to make DynamoDB better, such as automation for repetitive tasks and managing SQL databases in the same dashboard.
Pros and cons
Pros:
- AWS integration to extend functionality
- Strong documentation and support
- Easy to set up and use
Cons:
- Limited to AWS cloud
- No on-premises option
MongoDB is an open-source, document-based database. It can store structured data in the popular JavaScript Object Notation (JSON) format or in a proprietary format called Binary JSON (BSON). BSON can store more data types than JSON, and it can encode the type and length of a piece of information, making it easier for a machine to parse (convert formatted text into a specific data structure).
Why I Picked MongoDB: I chose MongoDB because it excels at what document databases do best: providing flexible data models that can evolve as your application needs grow. Since MongoDB scales horizontally, you can quickly add new servers to accommodate larger amounts of data clusters, and it works well for applications that require high performance.
MongoDB Standout Features and Integrations:
Features that make MongoDB stand out include sharding and replication. Sharding splits up larger datasets into smaller chunks, then shares those chunks with other machines, spreading the workload. The replication feature lets you provide data redundancy across multiple servers, minimizing server disruption.
Integrations Although you can integrate third-party APIs into MongoDB, there are also partner integrations that are specific to MongoDB, such as Vercel, Netlify, SAML SSO Providers, HashiCorp Terraform, and HashiCorp Fault.
Pros and cons
Pros:
- No predetermined schema improves flexibility and scalability
- Comprehensive documentation and large community support
- BSON widens data type support while reducing parsing
Cons:
- Some performance issues with larger databases
- BSON uses up more storage space than JSON
Elasticsearch is a distributed database that has a uniquely fast search function. Based on the Apache Lucene library, you can use Elasticsearch across a range of industries to improve the customer experience, streamline the DevOps lifecycle, and gain insights into production environments.
Why I Picked Elasticsearch: I chose Elasticsearch because it’s a powerhouse for searching data. Aside from being able to store and analyze large amounts of data in real-time, Elasticsearch can provide answers in milliseconds. It achieves this speed by converting unstructured data into specially configured documents optimized for language-based searches. This allows users to find the information they need quickly and easily.
Elasticsearch Standout Features and Integrations:
Features that I like about Elasticsearch include the advanced analytics, which help you fine-tune search results to improve the experience. The ML capabilities are also very powerful, and can personalize search experience based on a user’s location, purchase history, and customer account.
Integrations with Elasticsearch are a strong point. Some of the best pre-built integrations include 1password, Amazon CloudWatch, AWS ECS, Graphite, HA-Proxy, and LastPass.
Pros and cons
Pros:
- Affordable basic plan
- Huge range of integrations
- Unique search capabilities
Cons:
- Lack of security features
- Concept can be confusing at first
Redis is an open-source, in-memory, key-value database. It delivers sub-millisecond response times, which makes it ideal for all kinds of real-time applications, such as updating the leaderboard in an online multiplayer video game or providing real-time shipping information to a customer awaiting a delivery.
Why I Picked Redis: I was impressed by Redis’ fast performance. It stores data in memory instead of on a physical disk or Solid-State Drive (SSD), so when you request data, there is no need for it to pass through the disk, making it more efficient than many competitors.
Redis Standout Features and Integrations
Features that stood out to me include the automatic failover. When the master database doesn’t perform as expected, Redis promotes a replica of that database to master status, which minimizes server downtime by automatically switching operations between servers.
Integrations Redis integrates with native modules to enhance the platform’s capabilities. Some of the most popular integrations include RediSearch, RedisJSON, RedisGraph, RedisBloom, redis-cell, RedisTimeSeries, and RedisAI.
Pros and cons
Pros:
- Automatic failover guarantees high availability
- Useful for real-time applications such as gaming leaderboards and analytics
- In-memory data storage delivers fast performance
Cons:
- Lack of documentation
- No Graphical User Interface (GUI)
HBase is a column-oriented, NoSQL database that runs on the Hadoop Distributed File System (HDFS). This means the HBase platform is particularly good at managing large data, as the system is almost entirely fault-tolerant.
Why I Picked HBase: HBase stood out to me because it is extremely good with large datasets. It can scale across thousands of servers and can accommodate data up to terabytes in size. I also appreciated that HBases uses HDFS to detect faults across all servers and automatically recover quickly. It excelled at minimizing server downtime for large systems.
HBase Standout Features and Integrations:
Features that I like are its scalability and integrations. Since HBase stores data like a relational database in columns and rows, this makes it easy to scale horizontally across multiple servers. I found it incredibly easy to use HBase with other platforms like Apache Hadoop.
Integrations include Hive, a database solution used to analyze time-sensitive data in the same Hadoop cluster. Hbase can leverage Hive's analytical abilities, while Hive can leverage Hbase's real-time querying abilities.
Pros and cons
Pros:
- Horizontally scalable across thousands of servers
- Integration with Apache Hadoop
- Free to use
Cons:
- CPU and memory intensive
- Fewer built-in features than Cassandra – relies on third-party integration
Other NoSQL Databases
Below is a list of alternative NoSQL databases that I also recommend:
- MarkLogic Server
For simplifying complex data
- AstraDB
For real-time app building and scaling
- Riak
For unstructured data management
- Apache Drill
Schema-free database
- Dgraph
For fault tolerance
- InterSystems Caché
For managing transactional and historical data
- ScyllaDB
Fastest distributed database
- DataStax Enterprise
For zero server downtime
- Aerospike
For reducing server and cloud footprint
Related Software & Tool Reviews
If you still haven't found what you're looking for here, check out these other types of tools that we've tested and evaluated.
- Network Monitoring Software
- Server Monitoring Software
- SD-Wan Solutions
- Infrastructure Monitoring Tools
- Packet Sniffer
- Application Monitoring Tools
Selection Criteria for NoSQL Databases
In my experience of trying and researching NoSQL databases, I try my best to find systems that meet key use cases and provide helpful features. I've developed the following set of criteria to guide my selection process:
Core Functionality: 25% of total weighting score
To be considered for inclusion on my list of the best NoSQL databases, the solution had to support common use cases:
- Real-time data processing
- Full-text search
- Data analytics
- User profile management
- Content management
Additional Standout Features: 25% of total weighting score
- Scalability: Identifying databases that effortlessly scale out to meet increasing data demands.
- Flexible Data Models: Looking for databases that easily adapt to changes in data structure.
- High Performance: Testing for rapid data access and processing capabilities.
- High Availability: Checking for features that ensure data is always accessible.
- Security Features: Evaluating encryption, access controls, and other security mechanisms.
Usability: 10% of total weighting score
- Intuitive user interface: Prioritizing databases with clean, understandable UIs that simplify complex operations.
- Documentation quality: Seeking comprehensive, clear, and helpful user guides and documentation.
Onboarding: 10% of total weighting score
- Training materials: Evaluating the availability and quality of training resources like videos, tutorials, and product tours.
- Setup process: Assessing how easy it is to migrate data and start using the database effectively.
Customer Support: 10% of total weighting score
- Response time: Comparing how quickly customer support responds to inquiries.
- Support quality: Judging the effectiveness and accuracy of the support provided.
Value For Money: 10% of total weighting score
- Cost-effectiveness: Analyzing pricing structures in relation to the features and benefits offered.
- Scaling costs: Examining how costs change as data volume and access needs grow.
Customer Reviews: 10% of total weighting score
- Satisfaction levels: Looking for consistent positive feedback across various users.
- Reliability feedback: Gauging user experiences regarding the database's reliability and performance.
This approach ensures that each NoSQL database is evaluated on a comprehensive set of criteria that matters most to businesses dealing with vast and varied data sets. This methodical evaluation process helps pinpoint solutions that provide robust, flexible, and user-friendly data management capabilities tailored to the diverse needs of modern organizations.
Trends in NoSQL Databases For 2024
In 2024, the landscape of NoSQL databases continues to evolve rapidly, reflecting broader software trends and the increasing complexity and volume of data managed by organizations worldwide. Reviewing the latest product updates, press releases, and release logs from the most prominent and swiftly evolving NoSQL tools reveals insightful trends about how these databases are adapting to meet the needs of data specialists. Here's what I've been observing:
Trends in NoSQL Database Technology
- Emphasis on Real-time Data Processing: There's a growing trend towards supporting real-time analytics and processing capabilities within NoSQL databases. This reflects the market's increasing need to make instantaneous decisions based on live data, driven by the rise of IoT, streaming data sources, and online transaction processing.
- Advanced Data Modeling Flexibility: NoSQL databases are enhancing their support for more complex and varied data models. This evolution caters to the diverse nature of unstructured data, allowing for more nuanced and sophisticated data relationships and structures to be efficiently managed and queried.
- Automated Data Optimization and Management: Some NoSQL databases are introducing AI-driven data management and optimization features. These innovative tools automatically adjust queries, indexes, and storage practices to improve performance and reduce costs, representing a novel approach to database management.
- Multi-Model Support: The ability to support document, key-value, graph, and column-family data models within a single database is increasingly in demand. This feature addresses the challenge of managing diverse data types and access patterns, streamlining operations for data specialists.
- Integrated Caching Mechanisms: With the acceleration of data access speeds as a priority, integrated caching mechanisms are more important than ever. These features improve the user experience and system efficiency by reducing data access times, highlighting the demand for performance optimization.
Features Becoming Less Important
- Manual Sharding and Data Partitioning: As NoSQL databases enhance their auto-scaling and partitioning capabilities, the need for manual sharding and data partitioning is diminishing. Automated management of data distribution reflects the move towards more user-friendly and less labor-intensive database maintenance.
These trends reflect the ongoing evolution of NoSQL databases, as they adapt to the increasingly complex needs of managing large volumes of structured and unstructured data.
What Are NoSQL Databases?
No Structured Query Language (SQL) databases are non-relational databases that allow for the storage, retrieval, and management of data without the need for a fixed schema. These tools are primarily used by software developers, data architects, and IT professionals who deal with large volumes of structured, semi-structured, or unstructured data.
The shift towards NoSQL databases is driven by the need to address the limitations of traditional relational databases in handling the volume, velocity, and variety of today's data. The overall value of these tools lies in their ability to provide robust, flexible, and cost-effective solutions for complex data management needs across various industries.
Features of NoSQL Databases
Here are the most important features I look for when I'm evaluating NoSQL databases:
- Scalability: This feature allows for the database to expand according to the growing data needs. The ability to scale out using distributed architecture is essential for handling vast amounts of data seamlessly, without compromising on performance.
- Flexible Data Models: NoSQL databases offer flexible data models for storing and managing diverse data types. This flexibility is crucial for accommodating the varied and dynamic nature of unstructured data without the need for predefined schemas.
- High Performance: Optimized for specific data models and access patterns, ensuring quick data retrieval and high throughput. High performance is key in scenarios where time-sensitive access to data is critical for decision-making and operational efficiency.
- High Availability: This feature ensures that the database remains accessible even in the face of hardware failures or maintenance events. High availability is critical for applications requiring constant uptime and real-time access to data.
- Data Replication: Facilitates the copying of data across multiple servers, enhancing data availability and disaster recovery. Data replication is important for maintaining data integrity and ensuring continuous access to data across geographically distributed systems.
- Partition Tolerance: The ability to continue operating despite network or partition failures. Partition tolerance is essential in distributed systems, ensuring that the system remains operational even when parts of it are not communicating effectively.
- Multi-Model Support: Supports various data models like document, key-value, graph, and column-family within a single database. Multi-model support provides the versatility to handle different types of data and access patterns, simplifying the data architecture.
- Schema-less: Allows the storage of data without a predefined schema, offering flexibility in handling changes to data structures. Schema-less databases are ideal for applications that require the ability to evolve rapidly without the need for frequent database redesigns.
- Integrated Caching: Improves performance by storing frequently accessed data in memory. Integrated caching reduces data access times significantly, enhancing the user experience and system efficiency.
- Security Features: Comprehensive security measures including encryption, access control, and auditing. Robust security features protect sensitive data from unauthorized access and breaches, which is crucial for maintaining trust and compliance.
Choosing the right NoSQL database for your company requires careful consideration of these features to ensure that the selected database aligns with the specific data storage and processing needs. The right NoSQL database can provide the foundation for innovative applications and insights, enabling organizations to leverage their data assets fully while accommodating future growth and technological advancements.
Benefits of NoSQL Databases
NoSQL databases offer a flexible, scalable, and efficient way to manage data, making them an attractive option for organizations and developers dealing with large volumes of diverse data types. Unlike traditional relational databases, NoSQL databases are designed to handle unstructured and semi-structured data, offering unique advantages that can significantly enhance business operations and data management strategies. Here are five primary benefits of NoSQL databases for users and organizations:
- Scalability: Easily manage data growth with distributed architecture. NoSQL databases are inherently designed to scale out across multiple servers and data centers, allowing businesses to handle increasing volumes of data without a hitch, supporting growth and ensuring performance is maintained.
- Flexibility: Adapt to changing data models without downtime. The schema-less nature of NoSQL databases allows for the storage of unstructured and semi-structured data, providing the flexibility to rapidly evolve your application without the need to modify a rigid database schema, thus accelerating development cycles.
- High Performance: Achieve faster data access and processing. NoSQL databases can provide superior performance for certain types of operations, including those involving large volumes of data and real-time applications, by leveraging optimized storage, caching, and retrieval mechanisms tailored to specific data models.
- Cost-Effectiveness: Reduce costs with efficient data storage and processing. The distributed nature of NoSQL databases, combined with their ability to efficiently manage large volumes of diverse data, can lead to significant cost savings in hardware, storage, and maintenance compared to traditional database systems.
- Data Variety Handling: Store and query a wide range of data types. NoSQL databases support multiple data models, including key-value, document, wide-column, and graph formats, enabling organizations to leverage a single database for a variety of data types and applications, simplifying data management and enhancing analytical capabilities.
For organizations looking to innovate and scale, NoSQL databases offer a compelling set of advantages that can lead to enhanced performance, agility, and cost savings. Adopting NoSQL technology can empower businesses to more effectively meet current and future data management challenges, unlocking new opportunities for growth and innovation.
Costs & Pricing For NoSQL Databases
NoSQL databases offer a variety of plan and pricing options designed to accommodate the needs of businesses of all sizes, from startups to large enterprises. These databases are tailored to manage large volumes of structured and unstructured data efficiently, offering scalability, flexibility, and high performance. The pricing models vary significantly among different NoSQL database providers, typically based on factors such as data storage, read/write throughput, number of transactions, and level of support required.
Plan Comparison Table For NoSQL Databases
Here's a summary overview of the different pricing plans and cost ranges typically available for these tools:
| Plan Type | Average Price | Common Features |
|---|---|---|
| Free | $0 | Basic access, limited storage and throughput, community support |
| Standard | $100 - $1,000/month | Increased storage and throughput, technical support, basic security |
| Professional | $1,000 - $10,000/month | Enhanced security features, higher throughput and storage limits, 24/7 support |
| Enterprise | Custom pricing | Customized solutions, dedicated support, unlimited storage and throughput |
When choosing a NoSQL database plan, consider both your current needs and potential future growth to ensure that the plan you select can scale with your business. It's also important to balance cost with the features and support that are critical to your operations, ensuring you get the most value from your investment.
NoSQL Database Frequently asked questions
Here are the most common questions that people ask when searching for information on NoSQL databases.
What is the difference between SQL and NoSQL?
SQL databases use defined tables, rows, and columns to store data, while NoSQL databases are unrestricted by the same framework.
What are NoSQL databases mainly used for?
NoSQL databases are used to collect and process structured, semi-structured, and unstructured data that a relational database typically cannot handle.
Is NoSQL faster than SQL?
Because NoSQL databases are used for very specific instances, they are typically faster at achieving those instances than a traditional SQL database.
Choose the Right NoSQL Database
According to a 2019 survey by Deloitte, only 18 percent of organizations were taking advantage of unstructured data.
Therefore, there’s a large opportunity for efficiency gains if you select better database tools that make it easier for your organization to collect and process large amounts of unstructured data.
This is why it’s important you understand what NoSQL databases are and their varying uses. Hopefully, this article will help you make the right choice for your organization.
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