Systems | Development | Analytics | API | Testing

Open lakehouses powered by Apache Iceberg are redefining the landscape of data management. By bringing together the scalability of data lakes with the performance and reliability of data warehouses, lakehouses give you unprecedented flexibility and control over your data, while embracing open standards.

The future of data and analytics will be nothing like the experience we're used to today. We are at the beginning of a transformation that will fundamentally reshape how businesses use data, make decisions, and create value. At the center of this revolution is Agentic AI. Agentic AI fundamentally changes the way we work with data – moving from passive, reactive AI systems to autonomous, goal-oriented agents capable of reasoning, planning, and executing complex tasks across diverse data landscapes.

Qlik Connect this year started a little differently. Not with slides. Not with a video. But with a few minutes of unified harmony. On stage stood the Barcelona Guitar Quartet — except instead of four guitars, there was only one. And yet the sound that emerged was rich, layered, and astonishing. If you had closed your eyes, you’d have sworn it was a full band. It was a masterclass in collaboration.

MTConnect simplifies how manufacturing machines communicate, ensuring seamless data exchange across systems. It uses HTTP and XML to create a standard data model that works for both old and new equipment, making integration in factories straightforward.

Almost every web application is built for end-users to view and use. The application deployment process is an important operation in software development because it allows software to fulfill its purpose. This tutorial is primarily for software companies and developers to aid them in deploying and scaling web applications and services using a platform that automatically handles deployment with ease. Therefore, in this tutorial, we will be deploying a Django application to AWS.

Data is the new oil—but without a well-structured refinery, even the most abundant data becomes noise. Data warehouse modeling is that refinery: the critical blueprint that ensures organizations can store, retrieve, and analyze data with precision and efficiency. As of 2025, the landscape of data warehousing continues to evolve rapidly.

In 2025, the integration of Artificial Intelligence (AI) into Extract, Transform, Load (ETL) processes is transforming the data engineering landscape. Traditional ETL workflows are evolving from rigid, manually scripted pipelines into intelligent, adaptable systems powered by AI. These AI-driven ETL tools enable companies to handle increasing data complexity, schema drift, and real-time transformation demands without massive engineering overhead.

In an era where big data is often referred to as the “new oil,” extracting value from raw information is more critical than ever. However, this process is far from straightforward. Organizations today deal with data sprawled across SaaS platforms, on-prem systems, databases, CRMs, and countless APIs. Making sense of it requires powerful and reliable Extract, Transform, Load (ETL) capabilities — and that's where ETL consulting services become indispensable.

Automated testing is status quo for a majority of software delivery teams today, yet two-thirds of teams say they deploy code without completing all the necessary testing – and that it costs them anywhere between $500,000 and $5M USD annually. That’s according to a recent survey Tricentis commissioned with Censuswide.

The hype for Anthropic’s Model Context Protocol (MCP) has reached a boiling point. Everyone (including Kong) is releasing something around MCP to ensure they aren't seen as falling behind in the ever-changing AI landscape. However, in this mad dash, there remains confusion around MCP and what this standard actually enables. Some see MCP as a total game-changer, and some see it as little more than a thin and unnecessary wrapper. As usual, the truth lies somewhere in between.