Learn Azure AI Search with C# — index data, run vector and hybrid queries, and add RAG to your .NET app. Start building intelligent search today. If your application's search box still runs a LIKE '%term%' query against SQL Server, your users are quietly suffering. They type "cheap laptop for uni" and get zero results because your catalogue says "affordable notebook for students." Azure AI Search with C# fixes exactly this problem: it combines classic keyword search, vector embeddings, and semantic reranking into a single managed service that you can drive from .NET with a few dozen lines of code. In this tutorial you'll build a working search index from scratch, run keyword, vector, and hybrid queries, and finish with a Retrieval Augmented Generation (RAG) pattern that grounds an LLM in your own data. This guide targets .NET 9 and the Azure.Search.Documents v11 SDK. Every snippet is runnable. We'll explain why each design choice matter...
Learn AWS DynamoDB with C# and .NET: setup, CRUD, the Object Persistence Model, queries, and serverless Lambda best practices. Start building today. If you are building serverless .NET applications on AWS, sooner or later you will need a database that scales as effortlessly as AWS Lambda does. That database is almost always Amazon DynamoDB . In this DynamoDB C# tutorial, you will learn how to connect a .NET application to DynamoDB using the AWS SDK for .NET, perform CRUD operations with both the low-level and high-level APIs, run efficient queries, and avoid the pitfalls that catch most developers on their first serverless project. DynamoDB is a fully managed NoSQL key-value and document database. There are no servers to patch, no connection pools to tune, and it delivers single-digit millisecond latency at any scale. For Lambda functions written in C#, it is the natural fit: both services are pay-per-use, both scale horizontally, and neither requires you to manage infrastructur...