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Querying PostgreSQL in plain English is no longer a futuristic idea; it’s something you can build today. With a Model Context Protocol (MCP) server, Claude Desktop connects securely to external systems, including databases, through a structured interface. Here, MCP acts as the bridge between Claude and PostgreSQL, allowing it to run queries, explore schemas, and return results instantly in Markdown. This guide walks you through building that bridge in C#.

We are thrilled to announce a significant update across our DAC (Data Access Components) product line. This release focuses on expanding compatibility with the latest development environments, extending support for modern databases, strengthening security, and adding powerful new functionality for AI-driven applications.

Inconsistent SQL formatting is a silent productivity killer. The database will execute it, but for developers, poorly structured queries lead to slower reviews, harder debugging, and errors that slip through unnoticed. Over time, this lack of consistency compounds into costly technical debt. This guide shows how to format SQL code so it remains clear, consistent, and easy to maintain.

ClickUp integration has become a key driver of operational efficiency for over 3 million teams worldwide. By connecting the ClickUp platform with business applications, organizations simplify operations, eliminate manual handoffs, and give leaders the visibility needed to act with confidence. But achieving these outcomes requires a stable integration approach because not all methods offer the same flexibility or access.

MariaDB has become a preferred backend for many enterprises, thanks to features like Galera clustering, JSON functions, and ColumnStore for analytics. But to extract value from MariaDB, organizations need reliable integration with BI tools, ETL pipelines, and custom apps, and this is where MariaDB ODBC drivers come in. These drivers bridge the gap between your database and external systems. But not all of them are production-ready.

Snowflake is the backbone of modern enterprise data architecture, powering over 10,600 organizations—including 800+ Fortune Global 2000 leaders. But thriving in this ecosystem takes more than just the platform. It requires a data architecture that can handle real-world complexity—and that’s where Snowflake connectors come in.

Zendesk is a goldmine of customer insights, but extracting that value from its usage is not simple. Teams trying to export data from Zendesk often run into paywall restrictions, API rate limits, and third-party tools that promise simplicity but falter at scale. For organizations integrating support data into BI platforms, migrating systems, or automating reporting pipelines, these challenges stall analytics and strategy.

Dynamic SQL in SQL Server is built for scenarios where queries can’t be fully defined in advance. It’s the method of choice when structure depends on user input, variable schemas, or runtime conditions, cases where static SQL falls short. However, without proper structure, this flexibility introduces security and maintenance challenges. To make it work at scale, you need a disciplined approach.

Database change management is the foundation for building a stable, secure, and high-performing application. In today’s fast-paced technological landscape, where agile and DevOps are the go-to for developing database application, rapid releases and continuous iteration are the norms. But with frequent deployments comes the risk of untracked database changes.

Vector databases are rapidly becoming the cornerstone of modern AI applications—and for good reasons. If you are very familiar with AI technologies like ChatGPT, you have already seen what vectors can do. When you ask ChatGPT a question, such as, “What’s the weather like today?” to provide an accurate answer, the AI would first convert your question into a vector, which implies a series of numbers that capture the intent and context of your sentence. The cool part?