Systems | Development | Analytics | API | Testing

How to Extend and Harden Legacy APIs Without Rewriting Them | DreamFactory

This guide explains how to add caching, rate limiting, role-based filtering, and clean separation of logic to legacy APIs without changing backend code. You will learn a practical abstraction-layer approach that lets teams govern access, enforce policy, and improve performance while keeping stored procedures and services intact.

What is Perforce P4 (Helix Core)?

What is Perforce P4 (formerly Helix Core)? And why is P4 considered the industry standard version control system? In this overview, our Sr. P4 User Advocate, Jase Lindgren breaks down what P4 is, how it works, and why it’s trusted for massive projects—from AAA games to blockbuster films to advanced hardware development.

Perfecto: Automate End-to-End RCS Validation

Perfecto enables automated, AI-powered end-to-end RCS, SMS, and MMS validation on real cellular networks, ensuring every message is delivered, rendered, and experienced precisely as your customers expect. Learn more in this in-depth demo. With Perfecto, you can: This capability is crucial for validating retail promotions, healthcare reminders, airline status updates, conversational business flows, and more.

The Tax Exchange Series

Tax is strategy, risk, technology, and real business impact all rolled into one. Our video series brings together practitioners sharing what’s actually working right now. From transfer pricing pressure and global regulatory shifts to AI, ERP realities, and elevating tax’s voice in business decisions, these conversations focus on practical insight you can actually use. Real perspectives. Straightforward conversations. No fluff.

What's New in Confluent Clients for Kafka: Python Async GA, Schema Registry Upgrades

Hey, fellow Apache Kafka developers! It’s time for another update on the Confluent client ecosystem. Following our recent architectural milestones, we’re excited to announce the release of librdkafka 2.13.0, which powers the latest versions of our Python, JavaScript, .NET, Go, and C/C++ clients. In this release, you’ll find numerous improvements to the Python experience as well as critical security and Schema Registry enhancements for everyone.

Evolve25: Event Intro & Today's Cloudera with our CEO Charles Sansbury

Cloudera CEO Charles Sansbury kicks off Evolve 25 New York by defining the "Era of Convergence" and the rise of Private AI. Discover how Cloudera is managing over 25 exabytes of data to help global leaders move from horizontal AI use cases to business-unit centric ROI. Charles details the strategic acquisitions of Octopai and Taikun, explaining how they bridge the gap between "Command & Control" and "Cloud Convenience." Learn how to operationalize high-fidelity data to drive "Everywhere AI" across hybrid environments without compromising security.

From Instinct to Operating System: How Wistia Turned Strategy Into a Scalable Machine

In the early days of a company, decisions move quickly because the founder carries most of the context. Priorities are clear. Communication is simple. The team is small enough that alignment happens without much effort. As a company grows, that stops working. More customers introduce new use cases. More products create more tradeoffs.

Automate Your Data Workflows: Connect Databox MCP to Make.com

In this video, we show you how to connect Databox to Make using the Model Context Protocol (MCP). Learn how to give your automated workflows and AI tools direct access to your live business metrics, empowering you to easily fetch context, analyze data, and build data-driven automations faster than ever. Links & Resources: About this series: This video is part of our "Chat with Your Data" series, where we explore the Databox MCP.

Demo days: Reliability Under Pressure: How to Build Self-recovering Data Pipelines

Modern data pipelines don’t fail loudly. A schema change slips through. A few bad records halt ingestion. Dashboards go stale. Engineers rerun backfills. Warehouse costs spike. Business teams begin to question the data. Pipeline instability and silent failures remain some of the biggest bottlenecks for analytics teams operating at scale.