Systems | Development | Analytics | API | Testing

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Best Practices for Monetizing AI Successfully

Artificial intelligence has become a driving force behind modern innovation, helping businesses across all industries optimize processes and generate income. But how do you monetize AI usage effectively? Whether you’re integrating AI features into an existing plan or launching entirely new AI products, choosing the right approach can unlock steady revenue growth and strengthen competitive advantage.

Achieving API Traceability with OpenTelemetry and Moesif

APIs power complex and modern applications and in doing so they’ve also become a challenge to observe, monitor, and analyze. Even the apps we rely on daily consists of numerous services, each glued together by dedicated APIs that interact with one another in intricate ways. In today’s market, you have to make sure that API complexity doesn’t hurt the visibility you need to possess into your product.

AI as External Imagination

AI isn’t replacing testers—it’s becoming an extension of how they think. Here’s how @Maaret Pyhäjärvi sees it: Applications make us more creative, acting as an “external imagination.” Testers do the same for developers—when devs anticipate tester feedback, their testing improves. AI, when used right, serves a similar role: it challenges us to refine and rethink, not just automate. The real power of AI in testing?Doing the work for usPushing us to think better.

Data Catalog- Streamlined Data Management for Data Analysts

How many times have you struggled to find the right dataset for an ETL job? Have you wasted hours verifying column definitions, data sources, or lineage before using the data? If so, you're not alone. For data analysts working with ETL pipelines and data integration, one of the biggest challenges is ensuring data discoverability, quality, and governance. A data catalog solves these challenges by providing a centralized repository of metadata, helping teams easily find, understand, and manage data assets.

Data Normalization for Data Quality and ETL Optimization

Have you ever struggled with duplicate records, inconsistent formats, or redundant data in your ETL workflows? If so, the root cause may be a lack of data normalization. Poorly structured data leads to data quality issues, inefficient storage, and slow query performance. In ETL processes, normalizing data ensures accuracy, consistency, and streamlined processing, making it easier to integrate and analyze.

Chrome vs Chromium: What's the Key Difference?

Most of us know and use Google Chrome every day. It’s fast, easy to use, and works well with Google services. But have you ever heard of Chromium? You might have come across it if you’re a developer or a tester. Did you know that Chrome is built on Chromium? While they look similar, they are not the same. Chrome is a ready-to-use browser for everyday users, while Chromium is more like a base platform used by developers and testing teams. Each has its strengths and weaknesses.

FinOps Best Practices: Balancing Performance and Cost for Snowflake

Join us for an innovative session in our Weekly Walkthrough series, "FinOps Metrics That Matter," where we explore cutting-edge strategies to optimize both performance and cost in your Snowflake environment. Striking the perfect balance between high performance and cost efficiency is crucial. Yet, 80% of data management experts struggle with accurate cost forecasting and management (Forrester). We'll show you how to overcome these challenges and lead the pack in Snowflake FinOps.

Four things we learned at WEST 2025

As global maritime challenges intensify, the pressing need for the U.S. Department of Defense to modernize its maritime technology has never been more critical. At WEST 2025 in San Diego, Navy, Marine Corps, and Coast Guard leaders called for cutting-edge technologies to improve readiness and new capabilities to support multi-domain operations. And while IT modernization topped the DoD’s priorities, a new conversation about testing emerged.