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A cloud database is a type of database hosted and managed on a cloud computing platform, allowing businesses to retrieve their data from anywhere in the world.
Cloud databases offer greater scalability, flexibility, and cost efficiency than traditional on-premises databases, enabling organizations to quickly grow or shrink their storage and processing resources as needed.
Cloud databases are managed by cloud service providers or vendors, eliminating the need for organizations to invest in their own infrastructure. In contrast, traditional databases require dedicated hardware and software and are managed by the organization’s IT department. This can be costly and time-consuming to maintain, whereas cloud databases offer a more convenient and cost-effective solution.
Cloud databases have several key attributes that make them a popular choice for businesses. For example, they:
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Cloud databases offer two deployment models: traditional self-management and managed database as a service (DBaaS).
Traditional self-managed cloud databases: In this deployment model, the cloud database is self-deployed and self-managed on one or more virtual machines, giving organizations more control over their database.
Managed database service: In this model, the cloud service provider or vendor is responsible for most of the operational, maintenance, and administrative tasks, such as automatic provisioning, scaling, security patching, updates, and health monitoring. This approach allows organizations to focus on using their database to drive business objectives rather than managing the underlying technology.
A cloud database stores and manages data on remote servers accessed over the internet rather than on local, on-premises hardware. It operates on cloud infrastructure, where data is distributed across multiple servers to ensure high availability and fault tolerance.
There are a few different ways to deploy a cloud database:
Most database types, such as relational database services and non-relational databases, work with these deployment models.
Because data is stored in a cloud platform, it can be accessed from anywhere (provided there’s internet connectivity), making it ideal for businesses with global operations and distributed teams. Cloud databases also support high-performance applications by spreading workloads across multiple regions or servers.
The best cloud database solutions for your company will depend on your company’s budget, resources, and business priorities. Here are some key factors to consider when weighing your cloud database service options:
Cloud databases offer several benefits over traditional on-premises databases. Here are some of the key advantages:
With increased traffic and the requirement for redundancy, on-premises databases may strain a company’s resources to grow infrastructure along with demand. Hardware, IT staff, and energy expenditures are factors. The more simplified scalability of cloud databases removes this potential burden, as cloud databases can scale up and down dynamically and quickly as demand changes. Theoretically, there are no boundaries to a cloud database’s scalability.
The accessibility of cloud databases offers advantages to that of on-premises systems, especially for enterprises operating on a global scale. If your cloud databases allow synchronization, your IT team can access data from anywhere with an internet connection. Moreover, they help streamline application development, as cloud databases enable your team to build and delete databases as required throughout the development phase. This advantage decreases complexity and speeds up the time required to bring new applications to market.
The cost of cloud-based database services varies based on the cloud service provider. Price methods include subscription pricing, in which customers pay the same amount each month, pay-per-use pricing, in which you only pay for what you use, and hybrid pricing, which incorporates features of the subscription and pay-per-use pricing models.
By using cloud databases, companies can avoid the upfront costs of purchasing and maintaining hardware and software for local server installations. Additionally, they can avoid the ongoing costs of hiring and training IT staff to manage these systems. Outsourcing storage to a cloud provider can potentially save additional costs, as cloud providers can often offer storage at a lower cost compared to purchasing and maintaining the same amount of storage locally.
However, it’s important to note that the cost-effectiveness of cloud databases depends on a variety of factors, including the specific needs of the company, the size and complexity of the database, and the pricing model chosen.
Disaster recovery is a set of procedures or actions taken by database administrators to restore data after an unexpected event and reduce downtime. Cloud databases, including both NoSQL and traditional SQL databases, offer various backup and recovery options. While cloud database vendors typically provide backup and recovery solutions, it’s still important for businesses to have their own disaster recovery plans in place to ensure business continuity in the event of a disaster.
Managed databases generally offer data replication and redundancy across multiple data centers or regions. This allows systems to remain fully operational and accessible to users even if network disruptions, hardware, or software issues occur. DBaaS providers often have multiple copies of the database running across different servers or nodes, in addition to means to distribute traffic among database instances.
While cloud databases offer several advantages, there are also some disadvantages that can occur with some DBaaS providers:
Complex queries with foreign key relations and joins can lead to unpredictably high bills due to the increased processing power, storage, and network bandwidth required. These operations often cause full table scans and higher resource consumption, driving up costs. Additionally, cross-region data transfers can add to expenses if data is spread across multiple locations. To mitigate this, companies must design efficient data models, optimize queries, and understand the provider’s pricing structure to avoid unexpected charges.
Cloud latency refers to the time it takes for a cloud service provider to respond to a client’s request. In cloud computing, cloud service latency is a critical issue, particularly given the exponential growth of data creation and linked devices. The time it takes for data to travel to cloud hosting centers for computing operations and back to the client side can impact cloud computing performance. That’s why having a DBaaS provider that knows how to achieve low latency through geo-specific deployments is of critical importance when hosting a database in the cloud.
In practice, however, this is not a commonly seen issue. For applications deployed in the same availability zone as the database for major cloud providers, the typical latency is between one and five milliseconds. For comparison, a complex query generally takes much longer to return results.
Related resource: How to Reduce Latency and Minimize Outages
White paper: Latency is the New Outage
Vendor lock-in is a potential issue in which an organization becomes too reliant on a specific cloud service provider and faces difficulty migrating its infrastructure to a different provider. Companies may choose to pursue a multi-cloud deployment to work around this issue. By distributing data and workloads across several cloud providers, a company can make sure it’s not tied to any single provider’s pricing, features, or limitations.
Choosing a cloud database can be challenging, as many options are available with different features and pricing structures. Here are some factors to consider when choosing a cloud database:
Cloud databases can offer many benefits to organizations, but it’s important to follow best practices to ensure that they are used effectively and securely. Here are some key practices to keep in mind:
By following these best practices, organizations can effectively develop, monitor, and manage their cloud database infrastructure. They can ensure that their data is secure, efficiently managed, and supports their business needs.
High availability, scalability, and low latency are now common features for most cloud databases. Increasingly, the mobile and cloud era demands unprecedented speed and scale to deploy sophisticated applications (such as automation, IoT, or machine learning), that only in-memory computing can provide.
Using in-memory data structures, like those provided by Redis, offers significant performance advantages in cloud environments, particularly for achieving millisecond-level latency.
Since data is stored in memory rather than on disk, access times are drastically reduced, enabling faster read and write operations. This speed is useful for real-time web applications such as caching, vector search, and more, where low-latency responses are critical. Additionally, Redis supports advanced data structures like hashes, sets, sorted sets, vectors, and JSON, allowing developers to efficiently manage and query data in ways that further optimize performance.
Companies striving to build a modern AI stack require even further speed and scale. That’s why Redis for AI is specially designed to help teams bring GenAI apps into production.
Redis in-memory computing is transforming industries. Here are a few real-world scenarios where Redis is helping developers make fast apps, fast.
Financial services firms are undergoing massive digital disruption and are modernizing their applications to provide a superior customer experience, better decision-making, proactive fraud detection, and improved resilience. Redis Enterprise provides the modern data platform required to successfully deliver real-time financial services and mobile banking and comply with open banking requirements while enabling organizations to remain secure and compliant.
Truly great player experiences need a data platform that’s responsive and scales globally to match user growth. Redis Enterprise’s NoSQL in-memory database gives gaming companies real-time performance to make games responsive and fun, improve game engagement and player retention, and scale across hybrid and multi-cloud environments.
It’s now more important than ever to have a digital health infrastructure with the performance and scale needed to support the widespread adoption of telemedicine, digital claims processing, and other software-driven approaches to healthcare. Redis helps power modern healthcare apps by providing real-time performance, modernizing claims processing simplifying cloud migration, and applying AI to patient data analytics.
Today, customers expect a responsive, seamless, and personalized experience across all channels. Retailers must fundamentally transform their data layer to deliver the shopping experience customers want. This includes providing an omnichannel experience — a unified shopping journey across mobile, web, and in-store interactions. Modern retailers rely on Redis to deploy apps that deliver real-time inventory, caching for recent views, a personalized customer journey, cart searches, and more.
With Redis Cloud, companies can automatically and infinitely scale Redis with auto-rebalancing. Redis Cloud allows you to scale apps linearly for users anywhere in the world, serving real-time experiences with sub-millisecond latency and 99.999% availability. Make the most of your data infrastructure with hyperscale architecture, auto-tiering, and built-in durability.
Data tiering is a strategy for managing and storing data by categorizing it into different “tiers” or levels based on factors like usage frequency, performance needs, and storage costs. This approach optimizes performance and reduces costs by ensuring that critical data is quickly accessible while less important data is stored more economically. Data tiering is commonly used in cloud environments to balance performance with cost efficiency.
Redis is taking data tiering one step further with auto-tiering. Using Redis Enterprise’s auto-tiering, developers can extend large-volume databases beyond the limits of the existing DRAM in the cluster by using solid-state disks (SSD) as part of available memory. Taking advantage of some clever programming on our part, Redis Enterprise identifies what data should be in memory and what data should stay on SSDs at any given moment, doubling the throughput and cutting latencies in half as it did with previous solutions.
With cloud databases and an internet connection, people can access data and work from anywhere, empowering unprecedented global collaboration and communication. Over the past several years, technology around performance, scalability, availability, security, and more has made the use of cloud databases more efficient and cost-effective for many organizations.
Now, in-memory computing can deliver the unprecedented speed and scale needed to drive further innovation in the emerging GenAI era.
Redis is the world’s fastest in-memory database. It’s poised to help developers build, scale, and deploy apps fast—whether a company uses Community Edition, our in-memory database for caching and streaming; Redis Cloud, our fully managed service; or in conjunction with other SQL cloud databases such as Postgres on RDS and MongoDB.
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