AWS Archives - Page 3 of 7 - DEVOPS DONE RIGHT.

EKS Cluster AutoScaler

The Amazon EKS AutoScaler automatically adjusts the number of worker nodes in an Amazon EKS cluster based on resource utilization and scaling demands.

Before we started using EKS Cluster Autoscaler, managing node scaling in our Kubernetes cluster was a constant struggle. Here’s what we were dealing with:

High Costs: We had to keep extra EC2 nodes running all the time to avoid pod scheduling issues — even when they weren’t being used.

Pod Failures: If we tried to reduce node count to save money, pods would often stay Pending due to lack of resources.

Manual Work: Scaling nodes up or down was a manual task—time–consuming and error–prone.

Slow Deployments: During deployments or traffic spikes, our workloads would get stuck because there weren’t enough resources.

Unpredictable Demand: Our workloads change frequently, but our cluster capacity doesn’t— leading to overuse or underuse of nodes.

These challenges pushed us to explore Cluster Autoscaler, and it turned out to be a game–changer

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Load Testing using AWS Distributed System – Part 1

In today’s fast-paced world, ensuring your application and business can handle growing users is very important. Whether you are building a Mobile application, Web application, or REST API it is important to understand how our system performs under pressure. Load testing is one of the ways we identify the bottlenecks, performance issues, and potential feature failures before the end user identifies it.

While consulting for an Edtech platform they highlighted an issue of having a dynamic and distributed load testing environment that can scale itself if we want to generate more load to the system and once the goal is achieved it should incur minimal cost to the organization since we are not leveraging a static provisioned environment.

AWS Distributed load testing system is a powerful automated setup provided by AWS that can be installed in our AWS cloud environment. This setup helps developers and testers generate real-world traffic at scale and test how well the system handles it. In this blog, we will talk about how the AWS Distributed Load Testing system works and how it can be leveraged to improve the reliability and performance of the system.

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End-to-End Data Pipeline for Real-Time Stock Market Data!

Transform your data landscape with powerful, flexible, and flexible data pipelines. Learn the data engineering strategies needed to effectively manage, process, and derive insights from comprehensive datasets.. Creating robust, scalable, and fault-tolerant data pipelines is a complex task that requires multiple tools and techniques.

Unlock the skills of building real-time stock market data pipelines using Apache Kafka. Follow a detailed step-by-step guide from setting up Kafka on AWS EC2 and learn how to connect it to AWS Glue and Athena for intuitive data processing and insightful analytics.
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End-to-End RAG Solution with AWS Bedrock and LangChain

Introduction

In this blog, we’ll explore the powerful concept of Retrieval-Augmented Generation (RAG) and how it enhances the capabilities of large language models by integrating real-time, external knowledge sources. You’ll also learn how to build an end-to-end application that leverages this approach for practical use.

We’ll begin by understanding what RAG is, how it works, and why it’s gaining popularity for building more accurate and context-aware AI solutions. RAG combines the strengths of information retrieval and text generation, enabling language models to reference external, up-to-date knowledge bases beyond their original training data, making outputs more reliable and factually accurate.

As a practical demonstration, we’ll walk through building a custom RAG application that can intelligently query information from your own PDF documents. To achieve this, we’ll use the AWS Bedrock Llama 3 8B Instruct model, along with the LangChain framework and Streamlit for a user-friendly interface.

Key Technologies For End-to-End RAG Solution

1. Streamlit:
a. Interactive front–end for the application.
b. Simple yet powerful framework for building Python web
apps.
2. LangChain:
a. Framework for creating LLM–powered workflows.
b. Provides seamless integration with AWS Bedrock.
3. AWS Bedrock:
a. State–of–the–art LLM platform.
b. Powered by the highly efficient Llama 3 8B Instruct model.

Let’s get started! Implementing this application involves three key components, each designed to streamline setup and ensure best practices. With the right AWS consulting service, you can efficiently plan, deploy, and optimize each component for a secure and scalable solution.”

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