Systems | Development | Analytics | API | Testing

As analytics steps into the era of enterprise AI, customers’ requirements for a robust platform that is easy to use, connected and trusted for their current and future data needs remain unchanged. "Serverless computing" has enabled customers to use cloud capabilities without provisioning, deploying and managing either hardware or software resources.

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.

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.

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.

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.

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.

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.

The future of retail is now. Here is how key technology trends can help you build stronger customer relationships, boost revenue, and secure your operations.

In today’s data-driven world, the ability to turn raw data into actionable insights is no longer a nice to have—it’s a necessity to power exemplary citizen service. Singapore’s Smart Nation initiative is built on the idea that data, when utilized effectively, can transform public services and improve lives.

As real-time data processing becomes a cornerstone of modern applications, the ability to integrate machine learning model inference with Apache Flink offers developers a powerful tool for on-demand predictions in areas like fraud detection, customer personalization, predictive maintenance, and customer support. Flink enables developers to connect real-time data streams to external machine learning models through remote inference, where models are hosted on dedicated model servers and accessed via APIs.