3 Ways FinTechs Can Improve Cloud Observability at Scale
Financial technology (FinTech) companies today are shaping how consumers will save, spend, invest, and borrow in the economy of the future. But with that innovation comes a critical need for scalable cloud observability solutions that can support FinTech application performance, security, and compliance objectives through periods of exponential customer growth.
In this blog, we explore why cloud observability is becoming increasingly vital for FinTech companies and three ways that FinTechs can improve cloud observability at scale.
Why FinTechs Need Cloud Observability at Scale
1. Security Operations and Advanced Persistent Threat Hunting
FinTech companies that hold customer funds or handle sensitive customer data are prime targets for cyber attacks by digital adversaries. Cloud observability solutions allow these companies the ability to monitor infrastructure and application logs to identify anomalous user behavior or other activities that could indicate a security breach.
2. Application Performance Optimization and Trend Analysis
Competition in the financial technology space is fierce and FinTech companies know that winning and retaining customers depends on delivering the best user experience. Cloud observability solutions give FinTechs the ability to monitor and track application performance metrics, identify bottlenecks and drop-off points, and fix issues that negatively impact the customer experience.
As FinTechs experience customer growth, retaining application logs at scale from a growing number of daily user sessions becomes increasingly costly. By investing in a high-scale cloud observability solution, high-growth companies can retain more application logs to support long-term use cases like analyzing application performance and usage trends over time.
3. Monitoring and Alerting
Cloud observability tools give FinTech companies the ability to monitor cloud-based applications and IT infrastructure at scale and alert SecOps or DevOps teams when a potential application or security issue is detected. This gives FinTechs the ability to respond quickly, initiate an investigation, and remediate the issue to minimize any negative impact on customer experience.
4. Troubleshooting and RCA
When an application or security issue is detected, FinTech security and DevOps teams need the ability to investigate the issue, understand what happened, and determine a root cause before the issue can be resolved. Cloud observability solutions allow FinTech companies to troubleshoot cloud services and conduct root cause analysis (RCA) by analyzing historical log data.
Deploying high-scale cloud observability solutions can retain logs for much longer at a lower cost. As a result, security and DevOps teams will have more access to historical data that can support application troubleshooting and root cause analysis use cases.
5. Compliance and Auditing
FinTech companies are subject to a variety of financial regulatory requirements around recordkeeping and sensitive data access. A cloud observability platform can support these compliance objectives by tracking data access, ensuring data integrity, and ensuring that critical data is retained for the periods required by law.
6. Capacity Planning and Cost Optimization
Demand for FinTech services fluctuates over time, firms will tend to see fluctuations in cloud-based workloads and related costs. A cloud observability platform helps these companies gain insights into cloud resource utilization so DevOps engineers can optimize infrastructure configurations, manage scaling, and allocate resources efficiently while maintaining performance levels.
3 Cloud Observability Challenges for FinTech Companies
Now let’s take a look at some of the major challenges for FinTech companies when it comes to deploying and managing cloud observability solutions.
1. Observability Tool Performance Degrades at Scale
FinTech companies use a variety of different cloud observability tools to analyze cloud infrastructure and application logs, but most current solutions weren’t built to handle the scale of data generated today.
A common solution for analyzing log data in the cloud today is the open-source ELK stack, which incorporates Logstash, Elasticsearch, and Kibana. ELK stack can work well at low scale, but Elasticsearch query performance tends to degrade as daily log ingest increases and indices become extremely large. FinTechs can improve that performance by adding additional nodes to the Elastic cluster, but this results in high infrastructure costs and management overhead.
2. Observability Stack Requires High Management Overhead
A second challenge associated with most current cloud observability solutions is the amount of management overhead it takes to operate them.
When it comes to running a solution like ELK stack, data engineers can spend hours every week configuring data pipelines in Logstash to pull data from applications into Elasticsearch. From there, additional resources must be allocated to configure and troubleshoot the Elasticsearch cluster, optimize indices for storage efficiency, implement back-up clusters, and build data visualizations or dashboards in Kibana. And as daily log ingest increases and the volume of data in Elasticsearch grows, troubleshooting the cluster to maintain up-time and preserve functional querying can also become a time-consuming activity.
All of these activities require time and expertise from high-skilled data engineers who should be spending their time on more valuable activities than solution maintenance.
3. High Observability Costs
Generally, today's cloud observability solutions were never designed to be cost-effective at the scale of data that FinTech companies are producing in 2023. While open-source solutions like ELK stack are sometimes perceived to be free to use, scaling your ELK stack to analyze log data at petabyte scale incurs huge cloud infrastructure and resource costs.
3 Ways FinTechs Can Improve Cloud Observability
1. Choose a Scalable Observability Solution
As FinTech companies experience growth in usership and increased daily log ingestion, their development and security teams need a cloud observability solution that can replace the ELK stack and perform high-scale queries with no performance degradation.
At ChaosSearch, we index cloud logs and telemetry data using our proprietary Chaos Index® technology to preserve a full representation of the data with up to 95% data compression. Our stable index supports SQL queries on log data, full-text search, and machine learning workloads with no performance degradation, even when you query huge volumes of log data.
2. Simplify the Observability Stack to Reduce Management Overhead
So much of the management overhead associated with cloud observability solutions comes from configuration and maintenance tasks that add minimal value to the end result. That includes building data pipelines, configuring log ingestion, implementing and managing back-up clusters, and more.
In addition to better scalability, financial services need cloud observability solutions that make it easier to ingest, transform, and analyze their data. With ChaosSearch, FinTech companies can ingest log and telemetry data directly into Amazon S3 or GCS cloud object storage with no complex data pipelines or ETL process.
From there, the data is automatically indexed and made available for analysis. Our Chaos Refinery® tool makes it easy for data analysts to apply transformations at query time, removing the need for time-consuming pipeline configuration and allowing analysts to explore data in innovative ways to extract insights or identify new analytics use cases.
3. Drive Down Observability and Data Retention Costs
As daily log ingestion grows, FinTech companies often notice that retaining historical data in their cloud observability solution becomes prohibitively expensive. To save costs, FinTechs often reduce the log retention window to as little as seven days. While this does help drive down observability costs, it also means that FinTechs are deleting critical data that could be used to support long-term analytics use cases.
ChaosSearch delivers low-cost data retention at scale by leveraging cost-effective public cloud storage in combination with our high-compression data indexing technology. As a result, ChaosSearch users can store, retain, and query much more of their data for a much lower cost compared to other solutions.
Optimize Your FinTech Observability Stack with ChaosSearch
ChaosSearch is the cloud observability platform that FinTech companies need to efficiently analyze log and telemetry data at scale while driving down management overhead and data retention costs.
By investing in cloud observability at scale, FinTechs can unlock long-term analytics use cases that help secure their systems against threats, enhance the user experience, and support regulatory compliance initiatives and optimized resource allocation.
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