Systems | Development | Analytics | API | Testing

High-scale, high-frequency messaging and burst activity can lead to significant costs and performance challenges for realtime applications. To help with this, we’ve introduced server-side batching and conflated subscriptions—two powerful optimizations in Ably Pub/Sub that significantly reduce message count, lower bandwidth usage, and optimize processing overhead. These features allow you to process high-frequency updates more efficiently without sacrificing accuracy, reliability, or latency.

API audit logging is essential for tracking API interactions, securing sensitive data, and meeting compliance standards like GDPR, HIPAA, and PCI DSS. It records who accessed data, what actions were taken, when it happened, and more. Proper logging can reduce data breach risks by up to 70% and improve regulatory compliance by 90%.

So, you've built a Flask application — congratulations! You've crafted routes, connected databases, and perhaps even deployed it to a server. But have you tested it thoroughly? Testing isn't just a checkbox on a developer's to-do list: it's an essential part of building robust and reliable applications. So, in this article, we'll describe why testing is important for Flask applications and how you can effectively implement tests.

APIs are everywhere, quietly powering the apps and services we use daily. They allow seamless communication between software, forming the backbone of modern applications. However, if your APIs fail, your business eventually fails. Therefore, API testing becomes important as it checks for secure, high quality and integrated software.

The Head of Enterprise Data, Analytics & AI at IFF sat down with Fivetran to unpack how to successfully implement AI while balancing both innovation and governance concerns.

For QA professionals in financial services, the pressure is unlike any other industry. Whether you’re supporting a multinational bank or a regional credit union, one thing remains the same: even the smallest software error can have massive consequences. A glitch in an online payment system could affect thousands of customers. A regression bug in account processing might trigger a compliance breach.

The divide between operational and analytical systems has long resulted in data inconsistencies, unreliability, and redundancies. Without a single, unified source of truth, teams interpret information in their own ways—often after the fact. This can lead to downstream data discrepancies, issues, and distrust. Meanwhile, changes to upstream data structures create ripple effects, breaking downstream systems and requiring manual intervention to fix issues.

WSO2 APK (WSO2 API Platform for Kubernetes) provides a robust, Kubernetes-native platform for managing APIs. It’s purpose-built for cloud-native teams requiring fine-grained control over APIs in modern, distributed environments. With support for microservices architecture, secure ingress, and service discovery, APK solves the infrastructure side of the API equation.

In a world where data-driven decisions shape the future of every business, ETL (Extract, Transform, Load) processes are the backbone of operational intelligence. For organizations using Microsoft SQL Server, optimizing ETL pipelines isn't just a technical choice—it’s a strategic imperative. With over two decades in the ETL trenches, I’ve seen what works, what fails, and what silently erodes performance behind the scenes.

For many organizations across industries, the era of experimental AI has given way to the era of practical implementation. Even those companies still testing and evaluating AI solutions are shifting away from the art of the possible to focus more closely on what will soon produce measurable ROI. “It will no longer be enough for your organization to merely use AI to win the approval of company leadership,” says Samuel Lee, Product Marketing Director for Financial Services at Snowflake.