Databases

Domain Driven Design in Vector Databases

In-Short

CaveatWisdom

Caveat: We’ve all been swept up by the magic of Generative AI. Add a Retrieval-Augmented Generation (RAG) pipeline with Vector Databases on top, and your AI system suddenly sounds smarter—context-aware, grounded, and business-specific. But here’s the trap: Just bolting on a vector database doesn’t mean your AI is contextually intelligent. In fact, if your RAG database is a chaotic soup of embeddings from every business unit imaginable, you may have just created a slightly faster way to serve wrong answers—with more confidence.

Interweaving Purpose-Build Databases in the Microservices Architecture

It is best practice to have a separate database for each microservice based on its purpose. In this post we will understand how to analyse the purpose based on a scenario and choose the right database.

In-Short

CaveatWisdom

Caveat: We can easily run into cost overruns if we do not choose the right database and design it properly based on the purpose of our application.

Wisdom:

1. Understand the access patterns (Queries) which you make on our database.
2. Understand how your database storage scales, will it be in terra bytes or petabytes.
3. Analyse what is most important for your application among Consistency, Availability and Partition Tolerance.
4. Choose Purpose-Built databases on AWS cloud based on Application Purpose.

In-Detail

Query Lambda for RDS MySQL Private Database

Github link https://github.com/getramki/QueryLambda.git

It is important to create a database in private subnets in a VPC and not to expose it to internet, however it is challenging to connect to a private database instance and create the initial Schema and seed the database. This Query Lambda addresses this consern. This repo contains code for a Lambda function written in NodeJS and a SAM template to deploy it.

The Lambda function makes use of best practices of getting the secrets from Secrets Manager and using Layers for MySQL Package.