Sample DQL query combining multiple data types. With DQL, you can easily combine different data types into a single query. There are now boundless possibilities, such as identifying users affected by a service outage in a red-alert scenario or doing forensic research on a recent data breach. With the Dynatrace Query Language (DQL), teams can perform these analyses by asking questions that weren’t possible in the past.
#Boundless game making a lake full#
Smartscape unifies the different data types ingested into Dynatrace and retains the full context of this data to enable holistic and precise data analytics.
These capabilities are powered by Smartscape for DQL, a directional graph representing the real-time topology and dependencies of a data architecture. Whether it’s metrics, logs, events, traces, or any other data type, Dynatrace not only retains the data context but also enables you to analyze data in its semantic context without boundaries. With Dynatrace and Smartscape for DQL, metrics are a completely different game. In a traditional monitoring environment, metrics are aggregated data points that lose their context and granularity when data sets are trimmed to make them more manageable. Whereas Bill Gates’ observation is still valid, for the DevSecOps industry today, a more accurate description is “ context is king.” However, data on its own, without context, doesn’t reveal all its insights. Observability and application security use cases rely on data. In DevSecOps, content includes applications and services-in addition to information about the environments where they run and the users who use them. Returns instant query results in real-time using indexless queriesīill Gates wrote an essay in 1996 entitled “Content is King” in which he described the future of the internet as a marketplace for content.
#Boundless game making a lake code#
Ensures that data retains its context by assembling trace spans into PurePath® distributed traces (including additional code and thread profiling data).Retains large data volumes for up to 15 months in a highly cost-efficient way.Handles data volumes of hundreds of terabytes a day.
With Grail, we address these customer challenges by offering the most powerful and future-proof trace analytics solution on the market, which: For more complex cloud-native architectures, adding more services and applications leads to a massive increase in the volume of collected traces.
Having access to traces that span the full hybrid and multicloud stack enables developers to debug their applications in production and understand dependencies in live environments. Let Grail do the work, and benefit from instant visualization, precise analytics in context, and spot-on predictive analytics.Ī distributed trace follows a transaction on its journey through every service, cloud platform, and host in your environment. You no longer need to split, distribute, or pre-aggregate your data. This is only possible because of our no-index approach and massive parallel processing capabilities, which enable Dynatrace to offer extra-long data retention (15+ months) at full granularity that is cost-efficient and fast. Grail solves this scalability issue! Metrics on Grail is architected to manage billions of metrics to cope with cardinalities and unique value combinations of 1 trillion potential permutations for timeframes beyond a year. The proliferation of metrics can quickly result in a high cardinality challenge, with each service, host, or Kubernetes pod adding its own unique values to the data set. Ensuring observability across these environments requires access to data at a massive scale. Introducing Metrics on Grailĭespite their many advantages, modern cloud-native architectures can result in scalability and fragmentation challenges. Grail is addressing a lot of shortcomings of common databases. Now we’re adding Smartscape to DQL and two new data sources to Grail: Metrics on Grail and Traces on Grail. Grail can already store and process log and business events. Grail – the foundation of exploratory analytics They also enable an entirely new way of interacting with data and performing any analysis without boundaries. These capabilities enable Davis®, the Dynatrace causal AI engine, to gather even more insights. From day one, Grail disrupted the log management and analytics market by unifying observability, security, and business data and providing instant answers thanks to its massively parallel processing (MPP) capabilities.įurther extending our platform's analytics capabilities, we're increasing Grail's capabilities by adding new data types and unlocking support for graph analytics. Last October, we introduced Dynatrace Grail™, our causational data lakehouse.
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