Futureproofing applications with multi-model databases

With the complexity and the spread of apps’ data requirements only expanding, how can interactions with databases be kept efficient? 
9 March 2022

Public servants update Punjab’s government database of Covid-19 coronavirus test reports at the Red Cross Bhawan in Amritsar, India. (Photo by NARINDER NANU / AFP)

As digital experiences become increasingly complex, applications are facing unprecedented requirements for data processing. Increasing amounts of data are being collected, stored, and employed to enable comprehensive digital experiences, and this is only set to increase further. Gartner predicts that by 2025, the demand for memory efficiency will have driven the proportion of containerized enterprise applications to 15%, up from 5% in 2020. With the complexity of apps’ data requirements only expanding, how can interactions with databases be kept efficient?

The applications of the future

When it comes to digital experiences, the quality of customer, user, and developer experiences are paramount, and applications must be adapted to meet constantly evolving needs. Adding new functionalities to keep up with modern requirements is vital, but the updates and additional features required to stay ahead of the game often burden developers and servers with increasingly complex data challenges. Developers must strike a balance between structural consistency and agility.

As user requirements change, so does developer behavior. And one key hurdle that developers face is organizational reliance on traditional legacy databases. Not only are SQL databases unsuitable for many modern application requirements, they are also hindering progress.

In 2020, 61% of organizations that still relied on legacy databases reported that this was holding back their implementation of digital transformation projects. The push to develop cloud-based applications is driving developers away from these relational databases and towards serverless development strategies. Not only does this reduce the time-to-market for a product, but also reduces post-deployment operational costs as developers do not need to worry about underlying infrastructure when writing and deploying code.

To adapt to this new environment, developers are beginning to adopt cloud-native practices like continuous integration and delivery. Microservices are the perfect tool to allow continuous development with no impairment on continuity.

When each feature of an application has its own microservice, adjustments can be made to separate components without modifying the application as a whole — meaning no downtime is required to make modifications. But with agility comes risk.

Security is a significant concern for organizations looking to move towards cloud computing, with 62% of organizations citing security as a top-three concern when considering new cloud infrastructure. With this in mind, organizations must consider how new data and features can be added to applications safely, and whether developers have the tools and knowledge to achieve this.

Data sprawl leads to crawl

Microservices provide unparalleled agility from a development perspective, but they come with challenges of the data stack and data sprawl. As application development becomes more modular – and data requirements per application continue to grow – a single system could end up using thousands of databases. Eventually, servers cannot cope with data requirements, searches slow down, and user experience is impacted.

Data sprawl does not only impact search speed. Compromises in functionality and security are a common sight in applications with multiple databases, particularly as new features are added. As applications grow, data may become inconsistent, duplicated, and may no longer meet security requirements. This slows down development, causes issues with integration, and increases the complexity of administration – ultimately increasing cost.

Taking control of databases

Database management is an overarching problem in modern business. Development teams need to be efficient and bring applications to market fast. Learning and rewriting code so that new search capabilities can be added is inefficient, impacts performance, and slows down innovation. This is where multi-model database systems with full automation come in.

With their capacity to conduct multiple functions or services with a single database, flexible deployment, and cross datacentre replication, multi-model databases are a developer’s new best friend. And their potential functions are boundless.

As the name would suggest, multi-model databases support multiple data services in a single database platform. Not only do they help build microservices quickly, but they can support the consolidation of databases. This means sensitive data are better protected, automation simplifies administration, and both software and hardware costs are reduced by eliminating redundancy.

By taking advantage of the improved analytics provided by multi-model databases, businesses can improve how they use and collect data on customer profiles and behaviors. This allows them to design better customer experiences and targeted advertising – driving increases in sales through both loyalty due to positive experiences, and appropriate messaging. With multi-model databases, these analytics take place separately from data collection, keeping speeds high.

Other applications of multi-model databases include use of cross datacenter replication and memory-first architecture to reduce response times and implement multi-dimensional scaling of data services with no loss in latency. High speeds and responsiveness are key for growing businesses, especially those that require permanent uptime for security reasons.

As we develop applications of the future, their core building blocks – and data – must be managed effectively. Microservices continue to demonstrate their value, providing the agility, adaptability, and speed that all modern businesses crave. Mediating data sprawl before it gets out of hand will be a key challenge developers face, but with multi-model databases, customer services, latency, and high-scale processing do not have to be compromised as applications become more complex.




Article contributed by Anil Kumar, Director of Product Management at Couchbase