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.
Collaboration and communication are critical to the successful implementation of service level objectives. Development and operational teams need to evaluate the impact of their work against established service reliability targets in order to improve their end user experience. Datadog simplifies cross-team collaboration by enabling everyone in your organization to track, manage, and monitor the status of all of their SLOs and error budgets in one place.
In recent years, organizations have increasingly adopted service level objectives, or SLOs, as a fundamental part of their site reliability engineering (SRE) practice. Best practices around SLOs have been pioneered by Google—the Google SRE book and a webinar that we jointly hosted with Google both provide great introductions to this concept. In essence, SLOs are rooted in the idea that service reliability and user happiness go hand in hand.
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.
Logs provide a wealth of information that is invaluable for use cases like root cause analysis and audits. However, you typically don’t need to view the granular details of every log, particularly in dynamic environments that generate large volumes of them. Instead, it’s generally more useful to perform analytics on your logs in aggregate.
RethinkDB is a document-oriented database that enables clients to listen for updates in real time using streams called changefeeds. RethinkDB was built for easy sharding and replication, and its query language integrates with popular programming languages, with no need for clients to parse commands from strings. The open source project began in 2012, and joined the Linux Foundation in 2017.
