Operations | Monitoring | ITSM | DevOps | Cloud

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.

Hazelcast is a distributed, in-memory computing platform for processing large data sets with extremely low latency. Its in-memory data grid (IMDG) sits entirely in random access memory, which provides significantly faster access to data than disk-based databases. And with high availability and scalability, Hazelcast IMDG is ideal for use cases like fraud detection, payment processing, and IoT applications.

HiveMQ is an open source MQTT-compliant broker for enterprise-scale IoT environments that lets you reliably and securely transfer data between connected devices and downstream applications and services. With HiveMQ, you can provision horizontally scalable broker clusters in order to achieve maximum message throughput and prevent single points of failure.

Browser (or UI) tests are a key part of end-to-end (E2E) testing. They are critical for monitoring key application workflows—such as creating a new account or adding items to a cart—and ensuring that customers using your application don’t run into broken functionalities. But browser tests can be difficult to create and maintain. They take time to implement, and configurations for executing tests become more complex as your infrastructure grows.