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This Month in Datadog: DASH 2023, In-App WAF and User Protection, Cloudcraft for Azure, and more!

Datadog is constantly elevating the approach to cloud monitoring and security. This Month in Datadog updates you on our newest product features, announcements, resources, and events. This month, we put the Spotlight on DASH 2023..

Cloud Cost Management Demo

Growing cloud costs are a new constraint and challenge for many DevOps, FinOps, and Cloud Platform teams. Cloud Cost Management delivers granular cost data, scoped to the services developers own, so that engineers can take action on cost data. By unifying cost and observability data, engineering teams can quickly understand the root cause of cost changes, identify wasteful spend in their environment, and empower everyone across their organization to become a cost owner.

Datadog On Caching

Caching (and cache invalidation!) is often mentioned as one of the hardest problems in computer science. While caching can bring substantial performance improvements, reasoning about cached data can be extremely difficult as caching fundamentally means that you are no longer reading from your source of truth. With that in mind, many teams at Datadog needed to build distributed caches to scale their services and keep latency low.

Datadog Universal Service Monitoring Demo

See how you can get instant visibility into the health of your entire fleet of services—without requiring you to change a single line of code. By automatically discovering, mapping, and monitoring every service and dependency, Universal Service Monitoring allows you to detect issues faster, monitor service performance and SLOs across entire environments, and centralize all the knowledge about your services in a single place.

Datadog on Data Engineering Pipelines: Apache Spark at Scale

Datadog is an observability and security platform that ingests and processes tens of trillions of data points per day, coming from more than 22,000 customers. Processing that amount of data in a reasonable time stretches the limits of well known data engines like Apache Spark. In addition to scale, Datadog infrastructure is multi-cloud on Kubernetes and the data engineering platform is used by different engineering teams, so having a good set of abstractions to make running Spark jobs easier is critical.