OpenTelemetry (OTel) is an open-source, vendor-neutral observability solution that provides a suite of components—including APIs, SDKs, and a data collector—that enable teams to collect and communicate telemetry data from cloud-native applications and services. OTel also defines the OpenTelemetry Protocol (OTLP), a standard for the encoding and transfer of telemetry data.
Today’s modern applications are made up of thousands of loosely connected private and publicly exposed APIs, each serving a specific function. This dynamic API landscape, in combination with the decentralized nature of microservice development, can be overwhelmingly challenging to manage—let alone govern or secure adequately. API sprawl is often created as a result, leading to fragmented or nonexistent internal API documentation, knowledge bases, and toolsets.
As your applications grow, your teams may be faced with managing a complex, expanding mesh of potentially thousands of loosely connected APIs—each one a new point of failure that can be difficult to track and patch. API sprawl comes naturally in rapidly expanding, distributed applications, and the difficulty of maintaining centralized knowledge and toolsets for your APIs creates friction when teams need to leverage APIs they don’t own.
In Part 1 of this series, we talked about some challenges with building sufficient coverage for detecting security threats. We also discussed how telemetry sources like logs are invaluable for detecting potential threats to your environment because they provide crucial details about who is accessing service resources, why they are accessing them, and whether any changes have been made.
Boomi is a cloud-based integration platform that helps customers connect their applications, data sources, and other endpoints. But monitoring and troubleshooting Boomi Atoms—the runtime engines for Boomi integration processes—and the applications connected to them can be a challenge. Boomi automatically purges logs after 30 days, and users must frequently correlate data from various disconnected sources for visibility into their Boomi processes.
Amazon SageMaker is a fully managed service that enables data scientists and engineers to easily build, train, and deploy machine learning (ML) models. Whether you are integrating a personalized recommendation system into your video streaming application, creating a customer service chatbot, or building a predictive business analytics model, Amazon SageMaker’s robust feature set can simplify your ML workflows.
At Datadog, we have always been deeply involved with open source software—producing it, using it, and contributing to it. Our Agent, tracers, SDKs, and libraries have been open source from the beginning, giving our customers the flexibility to extend our tools for their own needs. The transparency of our open source components also allows them to fully audit the Datadog software that is running on their systems. But our commitment to open source only starts there.
As your apps scale, testing can become repetitive, manual, and time-consuming, leading to slower release cycles and lower-quality code. Sofy is a SaaS platform that enables you to create and run automated tests on your mobile apps without writing any code. Sofy will automatically test your mobile apps on real iOS and Android devices, so you can optimize their performance and debug end-user experiences without setting up or maintaining your own test infrastructure.
Ansible is a configuration management tool that helps you automatically deploy, manage, and configure software on your hosts. By turning manual workflows into automated processes, you can quicken your deployment lifecycle and ensure that all hosts are equipped with the proper configurations and tools. The Datadog collection is now available in both Ansible Galaxy and Ansible Automation Hub.
When the services in your distributed application interact with a database, you need telemetry that gives you end-to-end visibility into query performance to troubleshoot application issues. But often there are obstacles: application developers don’t have visibility into the database or its infrastructure, and database administrators (DBAs) can’t attribute the database load to specific services.
