Fintech is an industry that’s seen large growth in a span of just a few years From being a $1.8 billion industry in 2010, it has grown into an industry worth over $127 billion in 2018 with an expected annual growth rate of 24.8%. Fintech companies have a need for advanced computing solutions as the volumes of data they handle grow and transactions increase by the day. They need applications that can guarantee increased speed in processing data and constant high performance. Real-time insights are key in the fintech business, and the better and quicker companies process and analyze data, the better. Some of this data may fall under the remit of log data, for which Fintech companies would find use of a logging aggregator to their benefit for real-time trend analysis.
The fintech industry has experienced rapid growth in recent years, leading to a number of startups desiring to make a dent in the market. These startups need a viable data processing solution now more than ever due to the drop in funding caused by the COVID-19 pandemic. After years of record funding levels and rising valuations, venture capital deals decreased and funding dropped to $8.8 billion in the first half of 2020—a 20% year-on-year drop compared to the previous year. This scenario paints an uncertain future for many fintech companies and makes finding an ideal and cost-effective data infrastructure all the more important.
Pushing Boundaries, Scaling Performance
Processing hundreds or thousands of transactions a day means fintech companies require real-time reporting and high-speed data analytics. Bottlenecks in data processing can lead to loss of revenue and the inability to scale current business processes and implement new ones. The high scalability of an in-memory data grid will provide fintech companies more opportunities for growth and help them scale cost-effectively.
Data analytics is arguably one of the most vital features of a computing platform as it allows organizations to be predictive in its approach to business. Through analytics, businesses can transform raw data into actionable insights and help them determine current trends and answer critical questions about the business. An in-memory data grid pushes this to the next level through event-driven analytics, which is useful for instant notifications and alerts for vital business events like canceled payments and other transaction issues. Through event-driven analytics, a method or procedure is triggered whenever an event occurs or a set condition is met.
An in-memory data grid also provides context to streaming and transactional data by looking at an organization’s entire transaction history. Contextualizing data helps get real-time insights that lead to better—and quicker—business decisions. This is especially useful in assessing a business for potential risk that can affect regulatory compliance and customer behavior. Contextualized data also leads to real-time insights that help companies act accordingly, equipped with a better and deeper understanding of a risk’s impact and consequences. All this data is handled with high speed and low latency by an in-memory data grid, which can handle millions of events per second. Despite the lightning speed at which it processes data, an in-memory data grid effectively analyzes data to prevent undesired incidents like equipment breakdown, cyber attacks, customer churn, and more.
As fintech becomes a more competitive industry, the requirements for speed, availability, and scalability continue to grow. Fintech companies respond by leveraging the capabilities of the latest in-memory computing platforms. Below are the common in-memory components fintech companies use to meet their application demands.
- In-memory data grid
A memory cache inserted between application and database layers that uses RAM for maximum speed and to allow both the application and its data to collocate in a single memory space. It runs specialized software on each computer within a network to allow users to combine the computing power of all networked computers while keeping data synchronized and constantly available.
- In-memory compute grid
this uses distributed parallel processing to allow for the efficient acceleration of resource-intensive compute tasks.
- Distributed SQL
This provides support for traditional SQL queries that run across cluster nodes, including support for horizontal scalability, fault tolerance, and ANSI SQL-99 compliance.
- In-memory streaming and continuous event processing
This offers parallel indexing of distributed SQL queries to provide real-time analytics. It also allows for customizable workflow features that can process single or multiple continuous queries.
- Persistent store
This allows users to keep full dataset on disk and the most frequently used data in memory, providing the option to adjust the amount of RAM used by the system. It also optimizes data in such a way that the amount of stored data can exceed the amount of memory.
In-memory Computing for Growth
In-memory computing is, in no way, a new idea or technology; It has been around and used for years by forward-thinking companies looking to scale their business quickly and efficiently. A roadblock to its adoption was the cost of RAM, which has always been more expensive than disk. Recently, however, this cost has gone down to more reasonable levels and the technology has developed to a level that justifies the initial and ongoing costs. by allowing companies to perform parallel processing across a cluster of servers, they can determine logical next steps and plan for long-term growth.
As data becomes more a part of business and daily life, in-memory computing may be a fixture in the computing world and a critical component of every business system. With the flexibility to adapt to a variety of use-cases, including web-scale applications. and digital transformation, there may be an in-memory revolution across industries, including healthcare, eCommerce, telecommunications, and more.
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