When you’re on call and get paged at an inconvenient time, you need to be able to quickly determine the seriousness of the issue and act decisively to reduce system downtime. But pager notifications often don’t give you the information you need to investigate an issue from your mobile device, meaning that access to a laptop at all times is a must.
Amazon Kinesis Data Firehose is a service for ingesting, processing, and loading data from large, distributed sources such as clickstreams into multiple consumers for storage and real-time analytics. AWS recently launched a new Kinesis feature that allows users to ingest AWS service logs from CloudWatch and stream them directly to a third-party service for further analysis.
In Part 1, we looked at some best practices for getting started with creating effective test suites for critical application workflows. In this post, we’ll walk through best practices for making test suites easier to maintain over time, including: We’ll also show how Datadog can help you easily adhere to these best practices to keep test suites maintainable while ensuring a smooth troubleshooting experience for your team.
Client libraries are collections of code that make it easier for developers to write flexible and efficient applications that interface with APIs. Datadog provides client libraries so you can programmatically interact with our API to customize dashboards, search metrics, create alerts, and perform other tasks. We’re pleased to announce that we’ve developed and open-sourced two new client libraries for Java and Go in addition to our existing Ruby and Python libraries.
Reliable systems are vital to meeting customer expectations. Downtime not only hurts a company’s bottom line but can be detrimental to reputation. Our goal at Gremlin is to help enterprises build more reliable systems using Chaos Engineering. Whether your infrastructure is deployed on bare metal in a corporate-owned data center or as Kubernetes-orchestrated microservices in a public cloud, chaos experiments can help you find system weaknesses early, before they affect customers.
From smart thermostats and grocery store checkouts to public utility infrastructures and industrial manufacturing lines, the Internet of Things (IoT) is all around us—and growing larger every day. But with this rapid growth comes a number of operational challenges: IoT devices collect a large amount of data, and are often distributed across harsh, ever-changing environments.
Out-of-memory (OOM) errors take place when the Linux kernel can’t provide enough memory to run all of its user-space processes, causing at least one process to exit without warning. Without a comprehensive monitoring solution, OOM errors can be tricky to diagnose. In this post, you will learn how to use Datadog to diagnose OOM errors on Linux systems.
Synthetic monitoring lets you improve end user experience by proactively verifying that they can complete important transactions and access key endpoints. But your applications serve many users, from customers to all the employees who run your business. This makes testing the performance of any internal-facing services within your private network just as critical as monitoring your external-facing applications.
Apache Ignite is a computing platform for storing and processing large datasets in memory. Ignite can leverage hardware RAM as both a caching and storage layer to serve as a distributed, in-memory database or data grid. This allows Ignite to ingest and process complex datasets—such as those from real-time machine learning and analytics systems—in parallel and at faster speeds than traditional databases supported by only disk storage.
As healthcare and life sciences organizations move to digitize their operations and automate their workflows, they are encountering new challenges related to HIPAA regulations, which dictate how patient information should be collected and stored.
