Systems | Development | Analytics | API | Testing

If you’re building a SaaS platform or data product, it’s important to consider what BI tools your customers are already using. They want to connect Tableau, Power BI, Logi Symphony, or their own analytics stack directly to your data. They want SQL access, and to query your platform the way they query everything else. But expectations don’t quite meet reality once as tickets start flooding in.

Most guidance on database replication focuses on what happens inside the pipeline: which replication strategy to use, how to handle consistency, how to monitor lag, and how to manage failover. That’s all worth understanding. But this isn’t where many replication problems start. They begin when a team realizes that one of their most important data sources can’t participate in the replication pipeline at all.

As enterprises race to deploy multicloud architectures and Agentic AI, they face a common bottleneck: "state." To govern AI token usage, manage agent-to-agent communication, or optimize performance via caching, API and AI gateways require a persistence layer to synchronize data. We’re excited to share the GA of Managed Redis cache for Kong Dedicated Cloud Gateways (DCGW).

This guide shows how to create a single API layer that joins data across MySQL, Postgres, and MongoDB using a federated query engine with an API gateway pattern. You will implement a hands-on build, see code samples, and review performance, security, and governance steps. DreamFactory is a secure, self-hosted enterprise data access platform that provides governed API access to any data source, connecting enterprise applications and on-prem LLMs with role-based access and identity passthrough.

Your database contains thousands of answers. The problem isn't the data—it's that exploring it requires either technical skills or waiting for someone who has them. But what if you could sit down with Claude and just... talk through it? Ask a question. Follow a thread. Go deeper when something looks interesting. This isn't about running queries—it's about having analytical conversations with your own business data, guided by an AI that excels at exactly that kind of thinking.

Whether you’re streaming change data capture (CDC) events from MongoDB to Apache Kafka or sinking high-velocity data from Kafka into MongoDB for analytics, the following best practices ensure a secure, performant, and resilient architecture. This technical deep dive covers implementing the MongoDB Atlas Source and Sink Connectors on Confluent Cloud.

Combining MCP Servers and LLMs with MS SQL Server simplifies database management with natural language queries, automation, and robust security. Here’s what you need to know.

Key Takeaways As companies continue to scale analytics beyond operational systems, transferring data from generic databases (like MongoDB) to cloud warehouses (like Snowflake) becomes critical.