Building An Enterprise-Grade Database Architecture For Real-Time Applications

Big Data convergence, social media, mobile computing and the cloud have spawned a new generation of highly interactive, dynamic, consumer-oriented software systems. The so-called ‘systems of engagement’ often overlay older systems of record for connecting clients, employees, businesses and partners in real time.
The need for speed and huge sale which characterize systems of engagement has exposed legacy database technologies gaps which pose considerable challenges for deployment teams. For instance, it is very hard to ensure consistency of data among nodes in clusters and, when cache is added to the speed performance between persistent storage and cache.
The concern is compounded when establishing separate databases for reads and writes becomes necessary to boost performance. An incremental patchwork approach taken typically to address legacy database scalability and performance limitations naturally creates an ever-increasing web of complexity.
Due to the shortcomings, NoSQL databases are quickly emerging as the preferred option to enable real-time apps, offering significant advantages, such as scalability, flexibility, availability, productivity and agility. Enterprise infrastructure is influenced heavily and driven by the nature and choice of apps. The whole app stack is undergoing disruptive change from being relational, structured and schema-driven to become high-volume, real-time and schema-less apps. Databases are vital tools for apps and a lot of effort has been spent on the management and protecting them in accordance to the data’s lifecycle.
One of the largest challenges in product development is to ensure that what one actually deliver matches the most current requirements. Whether developing simple manufactured items, software systems or products or more complex ones with firmware, hardware and software components, making sure that the final design matches the finished product needs a process that is well-defined. When the development of software is linked to firmware an hardware development it is particularly challenging due to the huge range of requirements as well as numerous dependencies present for relatively simple apps. It’s for this reason that a Requirements Traceability process that is well-designed is vital to deliver a quality product.
Rapidly emerging workloads no longer served by any of the traditional options cause the new HTAP-optimized architectures offer a solution that is highly desirable. Minimizing both latency variable with one solution allows new apps and real-time data pipelines across industries. Almost ubiquitous web connectivity now drives modern workloads and a corresponding unique requirements set. Database systems should have characteristics such as data ingesting and processing in real time. In a lot of organizations, traditionally it has taken a day to understanding and analyzing data from when data is born to when it’s usable to analysts. These days, organizations want to do this in real time.
The generally accepted standard at present is that after data collection during the day and not necessarily using it, a four-to-six-hour process starts to produce an OLAP cube or materialized reports which facilitate quicker access for analysts. Nowadays, businesses expect queries to run on changing sets of data with results that are accurate to the last transaction. HTAP-Capable systems could run analytic over data change, meeting the needs of the emerging contemporary workloads. With minimized latency of data as well as reduced query latency, the systems offer predictable performance and also horizontal scalability. In-memory databases deliver lower latencies and more transactions for predictable SLAs or service level agreement. Systems that are disk-based simply could not achieve the same predictability level. For instance, if a disk-based storage system gets overwhelmed, performance could stop, creating havoc on app workloads. In-memory databases further deliver analytics as data is essentially written through a bathed extract, transform, load or ETL process. As analytics develop across historical and real-time data, in-memory databases could extend to columnar formats which run on top of higher disks capacity or flash SSDs to retain bigger datasets
Agile business has to implement stringent operational feedback loop so that decision makers could refine strategies fast. Analysts appreciate the ability of getting immediate access with visualization tools and preferred analysis. Users today expect custom experiences and publishers, retailers and advertisers could drive management through targeting recommendations that are based on the history and demographic information of a user. Personalization shapes the modern web experience. Creating apps to deliver the experiences needs real-time database to do segmentation as well as attribution at scale.
In-memory architectures scale can support big audiences, converge a record or system with a system of insight for tighter feedback loops and eradicating expensive pre-computation with the ability of capturing and analyzing data in real-time. Financial assets and their value shift in real time and the tools and reporting dashboards should keep up in the same way In-memory and HTAP systems converge analytical and transactional processing, thus portfolio value computations are accurate to the last trade. Today, users could update reports more often to recognize as well as capitalize on short-term trends, provide real-time serving layer to hundreds of analysts as well as view real-time and historical data via one interface.