Session Replay now includes Browser Dev Tools, a new feature that enables engineers to identify and debug the root causes of issues even faster by exposing key information about a playback session, such as network performance bottlenecks and any console log errors. This wealth of surrounding context will make it easier to trace frontend incidents throughout your application and remediate larger, ongoing issues.
Migrating your applications from on-prem infrastructure to the cloud comes with a number of benefits, including increased agility, resilience, and scalability, as well as potential cost and IT overhead reductions. But it can be complex, which is why organizations moving to Azure often use Microsoft’s Cloud Adoption Framework for Azure and its strategy for successful migrations.
Redis is an in-memory key-value data store that offers fast performance, flexible data structures, and multi-model databases, allowing it to handle a variety of use cases. Redis Enterprise enhances open source Redis with features designed to run distributed applications at scale, such as multi-tenancy, tiered data storage, active-active cluster replication, and support for up to five 9s of availability.
Modern DevOps teams that run dynamic, ephemeral environments (e.g., serverless) often struggle to keep up with the ever-increasing volume of logs, making it even more difficult to ensure that engineers can effectively troubleshoot incidents. During an incident, the trial-and-error process of finding and confirming which logs are relevant to your investigation can be time consuming and laborious. This results in employee frustration, degraded performance for customers, and lost revenue.
Datadog Events provides customers with a data feed about their infrastructure and applications, delivering an up-to-the-minute history of activity such as code deployments, configuration changes, and triggered alerts. Events collects data from Datadog products and over 100 third-party integrations—including Docker, Jenkins, Kubernetes, Sentry, AWS CloudWatch, and Azure Service Health.
gRPC is an open source Remote Procedure Call (RPC) framework developed by Google and released in 2016. Although gRPC is still relatively new, large organizations are adopting it in increasing numbers to build APIs to connect complex microservice meshes that use disparate languages and frameworks. gRPC-based APIs can process requests up to seven times faster than REST APIs, and they also allow customers to easily implement SSL authentication, load balancing, and tracing via plug-in libraries.
Shoreline is an incident response automation service that enables DevOps engineers and site reliability engineers (SREs) to quickly debug and remediate issues at scale and develop automated routines for incident management. Using Shoreline’s proprietary Op language, customers can run debug commands across all their hosts simultaneously and then deploy custom scripts via Actions to trigger automated remediations.
Adding automated testing to your CI/CD pipelines can help you ensure that you deploy changes safely. But as you continue to shift left, the number and complexity of tests are likely to increase, making them slower to run and harder to troubleshoot. Datadog CI Visibility can help you track the performance of your CI/CD pipelines and tests—and now you can also use Real User Monitoring (RUM) to monitor end-to-end (E2E) Cypress tests.
Hybrid mobile applications allow you to incorporate web-based content into your mobile offerings. By embedding webviews inside your iOS or Android app, you can repurpose existing code to build key mobile functionality, such as authentication processing or ad rendering. While hybrid apps can help streamline your development process, they can also make monitoring your system more complex.
AWS Lambda is AWS’s solution for highly portable, serverless computing. With Lambda functions, you can deploy and run business logic code without managing the underlying servers. Today, AWS announced that Lambda customers can now provision up to 10 GB of ephemeral storage for each of their functions, making them well-suited for new, data-intensive workloads—including machine learning inference, large media file processing, financial analysis, and more.
