Databases have always been defined by a consistent and reliable structure, without frequent and radical shifts. However, the COVID-19 pandemic is playing a significant part in transforming how enterprises interact with their clients as well as their workforce. Part of this digital transformation involves increased adoption of cloud computing in the enterprise. Databases are evolving to handle more data and incorporate more intelligence. To better support this evolution and enjoy the economic benefits of the cloud, enterprises are increasingly adopting cloud databases.
In July of 2019, Gartner reported that by 2022, 75 percent of all databases will be deployed or migrated to the cloud to improve analytics and as a Database-as-a-Service (DBaaS) offering.
Today, the growth of the market is driven by enterprises migrating their sprawling database infrastructures to the cloud, where faster integration and configuration are proving more attractive than on-premise solutions. Improved security protocols and the availability of compliance tools to support the boom in remote work are also playing a significant part in the current growth of the market.
Also read: Mapping Out a Hybrid Multicloud Strategy
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The Benefits of Cloud Databases
Compared to an expansion of on-premise server capability, setting up cloud databases could be much cheaper. The upfront cost of on-site servers is significant, not factoring in maintenance and administrative costs. Enterprises benefit from a pay-as-you-go model that is characteristic of most cloud services, allowing for cost-effective deployments.
Enterprises that manage their own services know how demanding an undertaking it may be. To better the efficiency of enterprises, adopting cloud database solutions may unshackle enterprises managing their own services and maintaining costly hardware to anticipate periodic traffic spikes. Cloud databases are elastic and scalable as they inherently lack restrictions in their ability to expand.
Enterprises with mobile teams require infrastructure that allows the teams to access workloads with the highest degree of security and efficiency, regardless of geographical location. Cloud databases are beneficial to such enterprises since they can be remotely accessed from many devices without a dip in quality of service.
The value proposition of cloud technology to enterprises is heavily dependent on the guarantee of reliability, which includes built-in redundancy and can offer round-the-clock uptime.
Cloud Database Trends
Fully managed cloud databases
Self-managed databases are time-consuming and inefficient. Enterprises taking responsibility to maintain and scale databases themselves in a landscape of digital transformation are realizing that these time-intensive tasks can be offloaded to allow teams to work on delivering applications, products, and services faster.
Artificial intelligence enhances cloud databases to provide features like automated monitoring and anomaly detection, predictive analytics, and a more intuitive user experience among others. Fully managed databases aim to automate tasks like patching, tuning, and upgrades. Automated cloud databases allow enterprises to recover quickly from failures. Enterprises have automated backups and system restores. Furthermore, enterprises get to apply configuration standards and policies by offering standardized services and diverse compliance tools.
However, the challenge facing self-managed databases is the nuances between users that introduce complexity, making it more difficult to automate a database from end to end.
Increased streaming database demand
Billions of devices are generating data at any given moment. A great chunk of these is Internet of Things (IoT) devices producing weather, device health, soil quality, telematic, patient, and machine health data, to name a few. Such data is immutable as it cannot or should not be deleted or changed. Since all this data is stored as opposed to being updated in storage, enterprises need a database that allows data to stream in and be timestamped.
Time-series databases go beyond just timestamping. Some improve the speed of data querying by tracking and indexing data through dedicated functions and syntax. They should also be able to consume unstructured data with the aid of specialized protocols.
A key differentiator between time-series databases and traditional databases is their efficient ability to store and provide access to vast volumes of data. The exponential generation of streaming data reflects the growing prominence of the IoT and the rapid adoption of edge computing. Consequently, the demand for time-series databases is on the rise.
Greater graph database adoption
Enterprises increasingly need to manage connected data as a result of the ongoing data explosion. Graph databases are an ideal solution for data storage as well as for better establishing relationships between data versus traditional relational databases.
Compared to relational databases, graph databases offer superior performance when querying related data, big or small. They also offer constant performance with an increase in the size of data, making them an exceptional solution for real-time big data queries.
Another plus for graph databases is that some graph query languages are Turing complete. Algorithms can be written on them. And, they serve as good artificial infrastructure because of how well relational data between entities is structured.
Since graph databases do not need inflexible design and data structure protocols associated with relational databases, their popularity as a cloud database solution have increased as they offer efficient storage of complex sets of relationships.
Increased cloud deployments and migrations
More databases are being deployed or migrated to the cloud. These deployments and shifts are motivated by the ecosystems being developed around cloud service providers. Such ecosystems allow a number of services to be integrated within a cloud service provider. This provides a stark contrast with an on-premise deployment approach, where standalone products and services rarely have inherent capabilities to support integration with other products.
To better their dynamism, enterprises are increasingly seeking cloud services and ecosystems that will sustain their cloud-native architecture and applications. Furthermore, the absence of heavy hardware investment and the promise of ease, flexibility, and availability offered by cloud databases is added motivation for increased cloud migrations and deployments.
More multi-cloud clusters
Generating insights from heterogeneous data continues to be a challenge with the rapid advancement of technology. For enterprises to remain competitive, they need to derive actionable insights from emerging technologies. This means that databases need to be compatible with vast open-source systems as well as have the ability to connect to many analytics and computing engines. Enterprises also require protection from outages caused by failures of public cloud service providers.
Through multi-cloud deployments, enterprises are guaranteed availability, scalability, and better performance. Cloud databases are now leveraging this by offering users the chance to concurrently run applications on many cloud infrastructures. As a result, high availability of the databases is achieved.
The use of outdated security processes and reliance on native security practices are exposing just how much on-premise databases do not guarantee the security of data. Security is being built into cloud databases, with techniques such as Transparent Data Encryption offering database encryption at file level. The evolution of this technique, as well as techniques like flexible key management, is likely to ease the cloud security concerns of enterprises and motivate them to adopt cloud databases. Emerging technologies, such as the blockchain, are also evolving to offer potentially robust and enhanced security in cloud database environments.