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Developers are at the forefront of creating the next wave of real-time, highly personalized customer experiences. They’re building adaptive applications that can adjust their behavior and features based on various factors like user preferences, environmental conditions, data inputs and changing circumstances. Imagine no longer getting irrelevant TV show or movie recommendations on streaming services.
These applications require real-time analytics and depend on fresh data to generate timely and accurate results. However, this real-time requirement poses a challenge for developers.
Relying on disparate transactional and analytical databases is creating a bottleneck in the application development process. Having to move data from system to system creates a delay, making it difficult for developers to keep up with the demand for real-time experiences and immediate service.
TLDR:
Highly contextualized applications that can adapt and react in real time to users’ needs are the next evolution of the user experience. How can this be made possible? Rapid sharing and data access can ensure applications perform without any delays, creating a smooth customer experience.
Companies that fail to deliver a seamless, intelligent and responsive user experience will fall behind. To keep pace, engineering teams are developing applications capable of ingesting and acting on data in milliseconds. This is enabled by real-time analytics, which provides enterprises with improved agility and performance through fast, schemaless ingestion of data.
Many developers today use multiple single-purpose data stores to juggle different tasks like transactions, operations and analytics. It’s like having to change an outfit every time you switch tasks throughout the day. Cooking breakfast? Put on a chef’s hat and apron. Answering emails? Change to office attire. Going to get the mail? Put on a post office uniform. This constant changing wastes time, creates a very chaotic closet and complicates simple tasks.
With contrasting landscapes, developers have to integrate and shuttle data across multiple systems, which is time-intensive and delays insights. Shuttling data is a tedious manual task that is essentially like using an ETL (extract, transform, load). It requires extracting data, transforming it and then loading it across various sources.
This back-and-forth data movement between systems — aka a write-back latency gap — delays application development. It’s no wonder analytics is almost never a “real-time” activity. According to Forrester Research, “Moving data from transactional systems to operational systems and then to analytical systems is a barrier to making better decisions faster. Disparate data stacks also compromise the delivery of timely data to various applications.”
For an application to be truly adaptive, it needs to be real time. That’s why developers are turning to solutions that can handle it all — aka a database that leverages columnar storage on the same platform as operational workloads to get value from data more quickly with real-time analytics.
This consolidation reduces disparate layers and leads to much less complex management, lower operating costs and improved efficiency due to fewer data sources. Now that sounds like an ideal situation for developers.
Columnar storage removes friction caused by multisource ingestion, enabling developers to consolidate and query data from various enterprise databases into a single location by integrating and organizing multiple data sources. With unified ingestion, developers have everything they need at their fingertips to address the real-time aggregation needed to deliver real-time, adaptive applications.
Additionally, columnar storage provides ease of use for developers. By working in a single platform, they can use one query language without having to go back and forth between different ones. The relational rigidity of traditional structures causes a language barrier and the need for translation, but columnar can automatically translate JSON structures into an analytics-ready columnar format so developers can seamlessly incorporate operational JSON data in analyses.
Converging operational and real-time analytic applications into one database platform gives developers the best of both worlds, and allows for workload isolation as applications scale, removing latency to deliver high performance.
This also leads to lower total cost of ownership because organizations aren’t absorbing the costs of separate platforms. Plus, with one platform, there is no need for a complex conversion process. This way, applications can give more immediate feedback, providing a real-time experience for end users, like live traffic updates during GPS navigation.
As user expectations for real-time experiences increase, multipurpose databases with columnar storage capabilities can be the Swiss Army knives of the data world, providing the scalable data infrastructure developers need to power the next generation of real-time apps.
Enterprises that consolidate fragmented data into an integrated columnar powerhouse will be at a competitive advantage with simplified architectures, empowering developers to build highly responsive, analytically driven app experiences that captivate the end user.
The result? Applications that adapt on the fly, giving users the freshest, most accurate information, while reducing latency, simplifying complexity and reducing cost. It’s time to make “real-time” truly feel like “right now.”
Want to learn more? See how you and your team can build AI-powered adaptive applications leveraging Couchbase vector search at the edge and real-time analytics with Couchbase Columnar.
