AWS Big Data Blog
Category: Management Tools
Process millions of observability events with Apache Flink and write directly to Prometheus
In this post, we explain how the new connector works. We also show how you can manage your Prometheus metrics data cardinality by preprocessing raw data with Flink to build real-time observability with Amazon Managed Service for Prometheus and Amazon Managed Grafana.
Correlate telemetry data with Amazon OpenSearch Service and Amazon Managed Grafana
In this post, we show you how to use Amazon OpenSearch Service and Amazon Managed Grafana to correlate the various observability signals that improve root cause analysis, thereby resulting in reduced Mean Time to Resolution (MTTR). We also provide a reference solution that can be used at scale for proactive monitoring of enterprise applications to avoid a problem before they occur.
How MuleSoft achieved cloud excellence through an event-driven Amazon Redshift lakehouse architecture
In our previous thought leadership blog post Why a Cloud Operating Model we defined a COE Framework and showed why MuleSoft implemented it and the benefits they received from it. In this post, we’ll dive into the technical implementation describing how MuleSoft used Amazon EventBridge, Amazon Redshift, Amazon Redshift Spectrum, Amazon S3, & AWS Glue to implement it.
Amazon EMR Serverless observability, Part 1: Monitor Amazon EMR Serverless workers in near real time using Amazon CloudWatch
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.
Create a customizable cross-company log lake for compliance, Part I: Business Background
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.
Deliver Amazon CloudWatch logs to Amazon OpenSearch Serverless
In this blog post, we will show how to use Amazon OpenSearch Ingestion to deliver CloudWatch logs to OpenSearch Serverless in near real-time. We outline a mechanism to connect a Lambda subscription filter with OpenSearch Ingestion and deliver logs to OpenSearch Serverless without explicitly needing a separate subscription filter for it.
Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics, Part 2: Real-time monitoring using Grafana
Monitoring data pipelines in real time is critical for catching issues early and minimizing disruptions. AWS Glue has made this more straightforward with the launch of AWS Glue job observability metrics, which provide valuable insights into your data integration pipelines built on AWS Glue. However, you might need to track key performance indicators across multiple […]
Disaster recovery strategies for Amazon MWAA – Part 1
In the dynamic world of cloud computing, ensuring the resilience and availability of critical applications is paramount. Disaster recovery (DR) is the process by which an organization anticipates and addresses technology-related disasters. For organizations implementing critical workload orchestration using Amazon Managed Workflows for Apache Airflow (Amazon MWAA), it is crucial to have a DR plan […]
Enable metric-based and scheduled scaling for Amazon Managed Service for Apache Flink
Thousands of developers use Apache Flink to build streaming applications to transform and analyze data in real time. Apache Flink is an open source framework and engine for processing data streams. It’s highly available and scalable, delivering high throughput and low latency for the most demanding stream-processing applications. Monitoring and scaling your applications is critical […]
Monitor Apache Spark applications on Amazon EMR with Amazon Cloudwatch
To improve a Spark application’s efficiency, it’s essential to monitor its performance and behavior. In this post, we demonstrate how to publish detailed Spark metrics from Amazon EMR to Amazon CloudWatch. This will give you the ability to identify bottlenecks while optimizing resource utilization.