Vishal Kumar P.’s Post

Byte 6: Popular Tools in Observability 1. Prometheus: For metric collection and alerting, widely used in cloud-native setups. 2. Grafana: Visualizes real-time metrics with customizable dashboards. 3. ELK Stack: Centralized log management and analysis. 4. Jaeger: Distributed tracing for microservices to detect bottlenecks. 5. Datadog: Full observability platform for logs, metrics, and traces.++ 6. AWS CloudWatch: Native AWS tool for monitoring logs and metrics.Provides amazing insights l. 7. New Relic: All-in-one monitoring with AI-driven insights.++ These tools help ensure reliable system performance and rapid troubleshooting. Did you use any of these tools ? How did you find them? #Observability #ContinuousLearning #ByteSizedLearning #TechGrowth #1PercentBetter #newrelic #datadog #aws

💡 Solving the Kafka Offset Mystery in AWS Lambda "Do I need to manage Kafka offsets manually in Lambda?" "What happens when my consumer group's offsets expire?" Learn how to build reliable event-driven architectures with Lambda and Kafka! (link in comments) #CloudComputing #AWS #ApacheKafka #ServerlessArchitecture #tech #Bigdata #aws #DataProcessing

Why OpenSearch is better than Elasticsearch: 1️⃣ Seamless AWS Integration 2️⃣ Open Source Flexibility and Cost Transparency 3️⃣ Enterprise-Grade Security 4️⃣ High Availability at Lower Cost 5️⃣ Performance Optimizations 6️⃣ Enhanced Ingestion, Monitoring, Security, Visualization, and AI Integrations 7️⃣ High Availability with Multi-AZ Architecture A weighted list of arguments in favor of OpenSearch by Alexey Vidanov. Link to the full article ⬇️

Day 16 of #120DaysOfDevOps: Delving into Azure Cosmos DB, unlocking a world of possibilities! Gained hands-on experience with this globally distributed, multi-model database service, designed for seamless scalability and low-latency performance. Explored its versatility in supporting diverse data models including document, key-value, graph, and column-family stores, catering to a wide range of application needs. Leveraged Azure Cosmos DB's automatic indexing, turnkey global distribution, and comprehensive SLAs to streamline data management and ensure high availability. From real-time analytics to mission-critical applications, Azure Cosmos DB offers a robust foundation for building scalable, globally distributed solutions. Excited to apply this newfound knowledge in our DevOps journey! #DevOps #Azure #CosmosDB #LearningInProgress #buildinpublic

🚀 Discover how to build scalable microservices architectures with #AzureCosmosDB! Join Teena Idnani for a deep dive into global distribution and partitioning strategies. Watch now: #https://lnkd.in/dvB_wUr5 💡 #Microservices

Tecnologias: Bedrock, Modelo claude 3 Haiku Anthropic, AWS Step functions, DSL

🚀 Just completed an exciting AWS lab on building serverless architectures! 🚀 In this lab, I built a proof-of-concept architecture using AWS services: SQS, Lambda, DynamoDB, SNS, and API Gateway. The goal was to create a serverless solution to capture, process, store, and notify based on incoming order data. Here’s what I accomplished and learned along the way: ✨ Highlights of the Lab: 1. IAM Roles & Security - Created IAM roles to ensure secure service interactions. Gained a strong grasp of permissions for secure resource access. 2. DynamoDB for Storage - Set up a DynamoDB table for flexible, scalable data storage without the hassle of database management. 3. SQS Queues - Configured an SQS queue to decouple components, making the system reliable and scalable by temporarily storing data. 4. Lambda & Event-Driven Processing - Used Lambda functions to automate data processing and notifications, without needing servers! 5. DynamoDB Streams - Enabled DynamoDB Streams to track data changes, triggering additional actions in real-time. 6. SNS Notifications - Created an SNS topic to send real-time notifications when new order data was added. 7. API Gateway - Built an API with API Gateway to allow external clients to securely submit order data into the system. 🧪 Testing the Architecture: Tested the whole flow from API Gateway to SNS, ensuring seamless integration between each component. This lab helped me understand how serverless technologies can streamline data processing without infrastructure overhead. 💡 Key Takeaway: AWS serverless services like Lambda, SQS, and DynamoDB make it possible to create scalable, efficient solutions that process data, trigger notifications, and manage storage without managing servers. #AWS #Serverless #CloudComputing #LearningInPublic #TechLab

🚀Developing telco SLMs: End-to-end MLOps for training and inference🚀 Discover how to build optimized telco Small Language Models (SLMs) with AWS. Join Subhash Talluri & Mecit Gungor from #AWS at #AWSreinvent to explore an end-to-end MLOps workflow for training and inference. Explore data curation best practices, custom tokenization, transformer architectures, training/inference frameworks, and domain adaptation techniques like finetuning and continued pre-training during this chalk talk. 🔗 Learn more: https://go.aws/3NmDO14

📢✨New Blog Alert! 🌐 78% of respondents in an IBM survey revealed that they are likely to increase the money, time, and effort invested in #microservices. Are microservices the future of #softwarearchitecture? Dive into our latest blog, "Understanding Microservices: A Comprehensive Guide for 2024," for a detailed read. https://lnkd.in/g5GGTnxH Microservices are the perfect solution for building today's modernized, #cloudnative infrastructure as they facilitate a faster #SDLC and an iterative “#failfast and #failforward” approach. Key Takeaways 1️⃣ Discover what spurred the shift to microservices and how to manage microservices on #AWS. 2️⃣ Do microservice benefits outweigh its challenges? 3️⃣ Are microservices better than #monolithic architecture? #Microservices #AWS #Ibexlabs #TechInnovation #SoftwareDevelopment #TechGuide #2024TechTrends #InnovationHub 🌐💻

#Fluentdpe #LogDataGenerated #RunningPods. #Kubernetes: #DaemonSetDeployment: In Kubernetes, Fluentd is often deployed as a DaemonSet, which ensures that an instance of Fluentd runs on each node in the cluster. This allows Fluentd to collect logs from all pods running on the node. #LogCollection: Fluentd is configured to collect log data from various sources within the Kubernetes environment, including Docker container logs, Kubernetes system logs, application logs, and any other log files that are mounted into containers as volumes. #LogParsingAndProcessing: Fluentd provides powerful parsing and processing capabilities, allowing it to parse log data into structured formats and enrich it with additional metadata. This enables users to extract relevant information from logs and perform actions such as filtering, transforming, or aggregating log data before forwarding it to downstream systems. #OutputPlugins: Fluentd supports a wide range of output plugins for forwarding log data to various destinations, such as Elasticsearch, Kafka, Amazon S3, Google Cloud Storage, or third-party logging services like Splunk or Datadog. Users can configure Fluentd to send log data to one or more destinations based on their requirements. Integration with Kubernetes Logging Stack: Fluentd integrates seamlessly with the Kubernetes logging stack, including the Kubernetes API server, kubelet, and container runtime. This allows Fluentd to collect logs from Kubernetes system components and containers running in pods without requiring additional configuration. #ScalabilityAndReliability: Fluentd is designed for scalability and reliability, making it suitable for collecting and processing large volumes of log data in production Kubernetes environments. It can handle high-throughput logging workloads and provide fault tolerance and buffering capabilities to ensure that log data is not lost during transmission. #CustomizationAndExtensibility: Fluentd is highly customizable and extensible, allowing users to configure it according to their specific requirements and integrate it with existing logging infrastructure. Users can develop custom plugins or extend Fluentd's functionality using its flexible configuration language.