Systems | Development | Analytics | API | Testing

Stories are how humans make sense of complexity. We remember cause and effect, not pie charts. We respond to tension, not tooltips. And yet, in most businesses, “data storytelling” still means downloading a CSV, sending screenshots in Slack, or fumbling through a slide deck minutes before the meeting. The promise was beautiful. Democratize data. Make every decision backed by fact, not gut. Put analytics in the hands of the many, not the few.

In the evolving landscape of AI applications, the Model Context Protocol (MCP) emerges as a pivotal standard, facilitating seamless integration between large language models (LLMs) and external tools, data sources, and services. By standardizing these interactions, MCP enables AI systems to perform complex tasks with enhanced context and precision. To harness the full potential of MCP, developers require robust tools that ensure reliability, scalability, and efficiency.

Manufacturers are drowning in data but struggling to use it effectively. Sensors on factory floors generate massive amounts of information - temperature, vibration, pressure - but much of it sits in isolated systems, creating "data silos." These silos prevent real-time decisions, predictive maintenance, and cost savings. The solution? Data AI Gateways. These gateways unify isolated sensors, process data locally with edge computing, and translate protocols to connect legacy equipment with modern systems.

Turn your warehouse into a growth engine — sync trusted data directly into Salesforce, Marketo, Zendesk, and more.

APIs have become the backbone of communication between applications in today’s software-oriented world. Digital systems are increasingly complex due to the proliferation of microservices and distributed systems, making API testing both efficient and crucial. A recent study projects the API testing industry to grow from USD 4.92 billion in 2025 to USD 27.38 billion by 2034, reflecting a compound annual growth rate (CAGR) of 20.99% during the 2025-2034 timeline. Source: Market Research Future.

Common Vulnerability and Exposures (CVE) collects known cybersecurity vulnerabilities and exposures to help you to better safeguard your embedded software. This framework is central to managing security threats effectively. Here, we explain what is CVE, unpack the role of CVE identifiers, examine the differences of CVE vs. CWE, expand on the CVE list, and outline how identifying vulnerabilities early in software development can be achieved with the right static analysis tools.

Modern data architectures rely on purpose-built analytics data platforms, stakeholder buy-in, and flexible, automated data integration.

It's not even 9 AM, and the day's already started. A customer can't reset their password. The product team just found a bug in the app. A partner bank needs data synced ASAP. Oh, and the CEO wants to know when the mobile app can do instant loans. Sounds about right? Yeah, for banking leaders, this is just another Tuesday. Customers want everything to be easy and digital. Stakeholders want things done well, safely, and by the book. And nobody has time for old-school stuff.

Marketing should be the hub of go-to-market strategy – not the internal agency that just says “yes” to everything. But what does it take to actually shift that perception and function?

Choosing the right IoT protocol for edge microservices can save energy, reduce latency, and improve efficiency. Here's a quick guide: MQTT: Lightweight and energy-efficient, ideal for resource-constrained devices. AMQP: Reliable and secure, great for mission-critical applications but resource-intensive. CoAP: Best for low-power, low-bandwidth devices with minimal energy needs. HTTP/REST: Widely compatible but consumes more energy and bandwidth.