Learn how OpenPipeline, AI Observability, and other platform upgrades empower teams by simplifying cloud complexity.
Complexity is the challenge facing engineering teams building, running, and improving cloud-native applications. Hybrid architectures comprising containers, microservices, APIs, and infrastructure also incorporate data lakes, ML models, and generative AI.
While powerful, these technologies introduce fragility and lack of transparency, inhibiting developer productivity and autonomy. At Perform 2024, Dynatrace unveiled Observability 2.0 enhancements that specifically target simplifying this complexity to help engineers innovate faster.
Taming the Data Deluge
Modern enterprises generate massive volumes of monitoring, security, and business data. Constrained bandwidth and storage create headaches trying to wrangle this variety of data sources. Dynatrace OpenPipeline provides a high-speed route to funnel all this telemetry into value-driving analytics and automation platforms.
Its patented processing algorithms handle petabyte-scale volumes at 5-10x the speed of alternatives, unlocking real-time analytics use cases. It retains full context as data streams in, enabling precise troubleshooting. OpenPipeline also reduces duplicate data by 30%, minimizing wasted resources.
For infrastructure and data engineers, this simplicity empowers more experimentation. You no longer must piece together custom pipelines that inevitably limit capabilities.
Trusting Data, Models, and AI
But easily accessing data is only helpful if the quality is pristine. Dynatrace Data Observability automatically tracks key telemetry health signals flowing through OpenPipeline. Any anomalies that could decrease downstream analytics accuracy trigger alerts for rapid investigation.
Data engineers can also proactively optimize pipelines by assessing data relevance and usage. You can iterate models with complete confidence in data provenance, knowing Dynatrace has your back.
Similarly, Dynatrace AI Observability provides end-to-end monitoring for generative AI across the full stack. This observability produces explainability into the behavior and quality of models in development, pre-production, and the wild.
For ML engineers, guardrails enforce governance by tracking model drift, token consumption efficiency, and origin tracing for AI-generated content. Application teams can confidently tap into generative AI thanks to this simplified oversight.
Empowering Autonomous Developer Experience
However, Dynatrace Perform 2024 went beyond infrastructure monitoring to enhancements aimed directly at developer experience, like Dynatrace Smartscape dependency mapping. This automatically surfaces connections between services, eliminating tedious manual tracking. Developers gain instant context into how changes impact app topology.
Copilot guidance speeds environment onboarding and provides policy guardrails to prevent misconfigurations. Meanwhile, Davis AI continually profiles application performance to surface code inefficiencies as they emerge.
These capabilities represent a fundamental shift left for observability data, empowering developers with the answers needed to optimize code proactively. Pipelines stay green thanks to quickly picking up on quality defects and performance deviations rather than waiting for angry customers.
And with the ability to tie precise emissions analytics to individual applications, developers can now assess environmental impacts right from their IDE. In other words, you no longer need a separate team to gauge sustainability; Carbon Impact does the math automatically and suggests code optimizations for greener apps.
The Bottom Line
Across the Observability 2.0 announcements, the clear intention is simplifying complexity for engineers through end-to-end monitoring combined with advanced Davis AI. This empowers developer autonomy by surfacing insights into code efficiency, data pipeline health, model governance, and emissions impact in a single intuitive platform.
By eliminating tool sprawl complexity, engineers can focus on building resilient cloud-native applications matching the pace of digital innovation vs. struggling with integration headaches. As Dynatrace SVP of Product Management Steve Tack noted, “You can’t expect developers to worry about Kubernetes configuration. I want to remove things from the developers' care about so they can focus on being productive.”
Based on customer research, Dynatrace estimates teams can double time spent writing productive code from 40% to 80+% through AI automation. That simplicity promises faster cloud-native application delivery and a developer experience that empowers innovation.