Category: Amazon CloudWatch

In this post, we demonstrate how BigBasket implemented the lakehouse architecture on AWS, including their architecture decisions, implementation approach, and the measurable business results you can expect from a similar modernization. Whether you’re facing scalability challenges or planning your own lakehouse implementation, this blueprint provides actionable insights you can adapt for your organization.

In this post, we explore how MDAA transforms data architecture development from months of manual coding to production-ready deployment through configuration-driven infrastructure and embedded governance, examine a real customer transformation, and provide a clear implementation pathway for your own data modernization journey.

In this post, I show you how to implement effective monitoring for your Kafka clusters using Amazon MSK and Amazon CloudWatch. You’ll learn how to track critical metrics like broker health, resource utilization, and consumer lag, and set up automated alerts to prevent operational issues.

In this post, we guide you through the steps to determine if your OpenSearch Service domain is right-sized, using AWS tools and best practices to optimize your configuration for workloads like log analytics, search, vector search, or synthetic data testing.

This post provides an in-depth look at setting up Amazon CloudWatch alarms for OpenSearch Ingestion pipelines. It goes beyond our recommended alarms to help identify bottlenecks in the pipeline, whether that’s in the sink, the OpenSearch clusters data is being sent to, the processors, or the pipeline not pulling or accepting enough from the source. This post will help you proactively monitor and troubleshoot your OpenSearch Ingestion pipelines.

In this post, you learn how to build Log Lake, a customizable cross-company data lake for compliance-related use cases that combines AWS CloudTrail and Amazon CloudWatch logs. You’ll discover how to set up separate tables for writing and reading, implement event-driven partition management using AWS Lambda, and transform raw JSON files into read-optimized Apache ORC format using AWS Glue jobs. Additionally, you’ll see how to extend Log Lake by adding Amazon Bedrock model invocation logs to enable human review of agent actions with elevated permissions, and how to use an AI agent to query your log data without writing SQL.

In this post, we explore commonly used Amazon CloudWatch metrics and alarms for OpenSearch Serverless, walking through the process of selecting relevant metrics, setting appropriate thresholds, and configuring alerts. This guide will provide you with a comprehensive monitoring strategy that complements the serverless nature of your OpenSearch deployment while maintaining full operational visibility.

In this post, we discuss how to build a fully automated, scheduled Spark processing pipeline using Amazon EMR on EC2, orchestrated with Step Functions and triggered by EventBridge. We walk through how to deploy this solution using AWS CloudFormation, processes COVID-19 public dataset data in Amazon Simple Storage Service (Amazon S3), and store the aggregated results in Amazon S3.

We have launched job worker metrics in Amazon CloudWatch for EMR Serverless. This feature allows you to monitor vCPUs, memory, ephemeral storage, and disk I/O allocation and usage metrics at an aggregate worker level for your Spark and Hive jobs. This post is part of a series about EMR Serverless observability. In this post, we discuss how to use these CloudWatch metrics to monitor EMR Serverless workers in near real time.

As builders, sometimes you want to dissect a customer experience, find problems, and figure out ways to make it better. That means going a layer down to mix and match primitives together to get more comprehensive features and more customization, flexibility, and freedom. In this post, we introduce Log Lake, a do-it-yourself data lake based on logs from CloudWatch and AWS CloudTrail.