The world has become fast-paced more than ever. The reason driving behind it is data.
You will be surprised to know almost 90% of the world’s data was produced in the last two years. Additionally, every two years, the volume of data created gets doubled.
In FinTech, data is like the motherboard of the CPU. Every transaction, fluctuation in operations and customer interaction generates tons of data. Hence, the potential to produce and analyze data becomes significant.
There is no question that Apache Kafka can be the backbone of FinTech operations. Apache FinTech developers give Kafka a lot of attention, which injects fresh air into digital financial services.
The financial services industry''s success hinges on our ability to effectively utilize cutting-edge technology to revolutionize conventional business procedures and establish novel principles.
Before we get much into detail, let''s first understand Apache Kafka.
What is Apache Kafka?
Apache Kafka is a distributed streaming platform that assists firms in ingesting, processing, and analyzing massive volumes of data in real-time.
It was initially developed at LinkedIn to solve the problem of ingesting high volumes of even data with low latency. Applications that feed data into Kafka are named producers, while those that consume data are called consumers.
Kafka’s primary strength lies in its ability to handle massive amounts of data, its flexibility to work with diverse applications and its fault tolerance. For businesses seeking to leverage Kafka alongside data warehouse consulting services, its capabilities offer a robust foundation for real-time data processing and analysis.
Use Cases of Kafka
Let’s discuss some of the most common and impactful use cases of Apache Kafka.
- Kafka serves as a highly reliable, scalable message queue. It decouples data producers from data consumers, which allows them to operate independently and efficiently at scale.
- A major use case is activity tracking. Kafka is ideal for ingesting and storing real-time events like clicks, views, and purchases from high-traffic websites and applications. Companies like Uber and Netflix use Kafka for real-time analytics of user activity.
- Kafka can disparate streams into unified real-time pipelines for analytics and storage.
- Kafka enables scalable stream processing of big data through a distributed architecture. For example, processing user click streams for product recommendations, detecting anomalies in IoT sensor data, or analyzing financial market data.
Use Case of Apache Kafka in the Financial Services Industry
How successfully financial institutions use technology to change "business as usual" and generate new value will determine their level of success in the next ten years.
By utilizing technological innovations, such as the rapid advancements in cloud computing, artificial intelligence, and data streaming, banks may achieve levels of client interaction and operational efficiency that were unthinkable just a few years ago. Certain technologies can be cleverly integrated into current systems and procedures to increase productivity, reduce operating expenses, and enhance customer satisfaction. Without a doubt, Apache Kafka is one of them.
Use case 1: Data flow for data analytics and downstream reporting.
Most of the end-of-day work for the primary banking system is creating data feeds for data analytics and downstream reporting. After the day''s online workload has decreased, this batch workload processing of the Extract Transform Load (ETL) type is usually completed at night.
Usually, it is done for two reasons: to combine all of the day''s transactions into one and to avoid causing the online load to be affected by these workloads.
These batches cause two main issues: first, they may interfere with concurrently executing core system batch activities (such as interest calculation and statement creation).
Operations can commence for the day once the feed-generation procedure is completed. Thus, great care has been taken in the design and sequencing of this procedure to prevent concurrency and data consistency problems.
Using Apache Kafka''s real-time streaming data architecture and analytics functionality will make handling this circumstance much more manageable. Using the Kafka Connect API, Apache Kafka makes it possible to create streaming data pipelines or the E and L in ETL.
This framework allows data to be moved from one system to Apache Kafka with a few straightforward configuration changes; it manages state durability, scaling, and distribution. This functionality allows data to be moved to Kafka from core banking systems. The Kafka Streams API can implement stream processing and transformations once the data is there; this puts the T in ETL.
Use case 2: Automated digital enrollment and customer communication.
In the world of banking, online account booking is starting to become widespread. As part of this, users select their preferred login information for online banking.
Though booking an account may seem straightforward, it involves several intricate steps, including identity verification through credit agencies, identity theft prevention checks, customer provisioning inside the security stack, and welcome email messages.
Processes like customer provisioning can be unloaded, even when some of them are completed sequentially in a workflow. Using an event-driven microservices architecture and Apache Kafka to stream data, customer provisioning can begin concurrently with account booking.
Separating the digital enrollment procedure into a separate step, streamlines and simplifies the entire process flow. An event producer can asynchronously communicate booking data to Apache Kafka to enable digital consumption and give the required digital banking credentials, which is one method of completing the parallel process.
In addition to enrollment, booking information can be sent to a centralized alerting platform via Apache Kafka, generating account booking notifications such as welcome, first funding, and exception alerts.
Use case 3: SWIFT message routing.
SWIFT message routing is a complex process in banking. Banks use the SWIFT network to communicate messages (including payments and securities).
Typically, a bank employs a centralized routing hub to route all inbound SWIFT messages to their appropriate back-office systems via complicated algorithms. Some require routing to numerous systems with varying frequencies and priorities.
Such sophisticated responsibilities can keep a SWIFT admin team on its toes because routing hubs built with traditional integration architecture, such as ESB or MQ, or a combination of the two, are not flexible enough to swiftly deploy new solutions.
Apache Kafka is developed to handle such integration complexity with ease. Its distributed publish-subscribe messaging solution dramatically decreases the multi-system integration overhead by categorizing SWIFT messages into topics and storing them in a fault-tolerant manner.
It is in sharp contrast to typical queueing systems, in which once one client consumes a message, it is no longer available to any other clients. Kafka''s long-lasting message retention feature even lets users work on their own time.
Conclusion
Apache Kafka opens new potential for rebuilding modern integrated banking applications. It also sheds new insight into the capabilities of the old integrated banking paradigm, which is under significant strain due to the growing workload.
Kafka-enabled solutions are altering the digital banking environment while providing an edge in consumer financial transactions and communication.
A new set of performance scaling capabilities for high-demand service would be necessary for success in a digital banking ecosystem, and Kafka delivers just that.
For these reasons, Apache Kafka is seen as the next major game changer in the FinTech software development Industry, capable of constructing complex transactional and messaging systems. It will help firms compete and remain ahead.