Systems | Development | Analytics | API | Testing

dreamfactory

How to Integrate Monitoring Tools with Microservices

Jan 14, 2026 By Kevin Hood In DreamFactory
Monitoring microservices is challenging but essential for maintaining system performance and reliability. Unlike traditional applications, microservices require tracking individual services, their interactions, and the infrastructure they run on. Here's what you need to know: To succeed, instrument your services early, set clear Service Level Objectives (SLOs), and ensure your tools scale with your architecture.
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smartbear

How regulatory organizations can modernize API testing without compromising compliance

Jan 13, 2026 By Vyshnavi Dabbir In SmartBear
Picture this scenario: Your organization is three days away from a critical compliance audit. The auditors have requested comprehensive documentation of your API testing processes, including security testing results, change management logs, and validation records. As you and your QA team scramble to compile reports from multiple tools and spreadsheets, a sinking realization sets in.
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dreamfactory

Scaling REST APIs for High-Volume Databases | DreamFactory

Jan 13, 2026 By Terence Bennett In DreamFactory
Scaling REST APIs for databases with heavy traffic requires careful planning to ensure fast, reliable performance. Here’s the gist: -Stateless Design: APIs should process requests independently, enabling horizontal scaling and efficient load balancing. -Caching: Use client-side, server-side, and CDN caching to reduce database strain and improve response times. -Optimized Queries: Fine-tune database queries, use indexes, and implement pagination to handle large datasets effectively.
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dreamfactory

How to Turn SQL Server Stored Procedures into REST APIs: Complete Guide

Jan 12, 2026 By Kevin McGahey In DreamFactory
Enterprises have invested decades of development effort into SQL Server stored procedures. These procedures contain critical business logic—validation rules, complex calculations, transaction handling, reporting queries—that applications need to access. But stored procedures weren't designed for modern API-driven architectures.
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kong

AI Agent with Strands SDK, Kong AI/MCP Gateway & Amazon Bedrock

Jan 12, 2026 By Claudio Acquaviva In Kong
In one of our posts, Kong AI/MCP Gateway and Kong MCP Server technical breakdown, we described the new capabilities added to Kong AI Gateway to support MCP (Model Context Protocol). The post focused exclusively on consuming MCP server and MCP tools through Kong MCP Gateway. Now, it's time to check how an AI agent can leverage the AI and MCP infrastructure exposed and protected by Kong AI/MCP Gateway.
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keploy

Software Deployment In 2026: Checklist & Strategies That Work

Jan 12, 2026 By Keploy In Keploy
Software deployment looks simple on paper, but in real projects, it’s where most failures show up. Even stable code can break when deployment isn’t planned well. In 2026, software deployment is no longer just about pushing code – it’s about reliability, speed, and control. Let’s explore how modern teams can deploy smarter, faster, and safer in 2026.
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Supercharge your LLM Using Production Data Context

Jan 12, 2026 By Speedscale In Speedscale
Are your LLM coding agents (like Cursor or Claude Code) hallucinating fixes because they don't know what's actually happening in production? In this video, Matt from Speedscale shows you how to bridge the gap between your local IDE and live production traffic using the Model Context Protocol (MCP). Most observability tools just give you telemetry. Speedscale’s MCP server gives your agent the "inner workings" of actual API calls and payloads, so it can check its assumptions against reality. No more "vibe-coding" and hoping it works; let your agent find the 500 errors and rate limits for you.
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speedscale

Let Your LLM Debug Using Production Recordings

Jan 11, 2026 By Matthew LeRay In Speedscale
Modern LLM coding agents are great at reading code, but they still make assumptions. When something breaks in production, those assumptions can slow you down—especially when the real issue lives in live traffic, API responses, or database behavior. In this post, I’ll walk through how to connect an MCP server to your LLM coding assistant so it can pull real production data on demand, validate its assumptions, and help you debug faster.
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