What are the emerging trends involving databases, AI, chaos engineering and other data/analytics technologies? Understanding these now can assist businesses as they enter 2022.
To gain an insight into current trends and developments, Digital Journal caught up with Siddon Tang, Chief Architect, and Shen Li, Head of Global Business, PingCAP.
Chaos-as-a-service brings Chaos Engineering to masses
‘Chaos Engineering’ is a relatively new term of the IT environment. It refers to the process of stressing an application in testing or production environments by creating disruptive events.
To put this into context, Tang states: “In the early 2010s, Netflix pioneered the practice of Chaos Engineering, a process for testing a highly distributed computing platform’s ability to withstand random disruptions and ultimately improve its reliability and resilience. Since then, other major web companies have adopted Chaos Engineering, but it hasn’t caught on with organizations running sub-hyperscale deployments, who lack the resources to leverage it.”
Tang adds: “However, in 2022, a new concept will begin to gain steam that will help bring chaos engineering to orgs of all sizes: Chaos-as-a-Service. CaaS will eventually enable orgs that aren’t running at the scale of Netflix of Facebook to leverage Chaos Engineering and boost their infrastructure’s resiliency.”
The rise of cloud-native databases
Cloud-native databases are simply databases that are primarily cloud-based. For the deployment and delivery, the process is conducted via the cloud. Such databases are designed to make the most effective use of cloud computing technology.
Li explains how these types of databases are helping to transform business practices; “As the pandemic drove increasing use of online services, traditional database systems struggled to keep up with all the requests and new data that flooded in. In 2022, more organizations will remedy this by transitioning to cloud-native databases.”
He adds that: “Cloud-native databases provide improved agility, scalability, reliability and availability compared to traditional databases. Adoption of cloud-native databases will pick up particularly among enterprises in the e-commerce and finance sectors, which must support a massive number of customer transactions and rapidly expanding data volumes while having to create new apps in order to deliver new services. ”
Smarter, scalable data extraction
Scalability is a key feature for big data analysis and machine learning frameworks. Li sees this as essential for business development, noting: “While accelerating digital transformation continues to be top of mind, many organizations are still facing extensive big data challenges including the need to extract real-time, relevant data at scale and at a faster pace. In 2022, organizations will increasingly turn to cloud-native databases for enhanced in-cloud data movement efficiency, intelligent data retrieval and real-time insights, which will help provide greater data agility, reliability, scalability and availability in comparison to traditional databases. More leaders will be betting on cloud-native databases to mitigate complexities introduced to their environment by conducting data integration themselves or through third-party data management tools. The business results will shine through as they will be able to extract data much quicker and lower their operational costs, while taking further steps toward their digital transformation goals and business growth initiatives
Orgs dive into self-service intelligence
Li picks on another important change afoot for next year, stating: “In 2022, organizations will equip their line of business users and CMOs with self-service intelligence. This intelligence will provide these users with a single, real-time view of data to drive immediate decisions on new services, while further enabling them to see the impact of tweaks they make to these new services and how it impacts their ROI bottom line. To achieve this, orgs will embrace a nimble data-on-demand model that can accommodate “what if” scenarios on the fly. That model must be underpinned by a database that is able to scale quickly, can easily integrate natively historical/transactional data with real-time analytics and is highly available to address the always-on needs of online businesses.”
