Dynatrace announced the launch of OpenPipeline®, a new core technology that provides customers with a single pipeline to manage petabyte-scale data ingestion into the Dynatrace® platform to fuel secure and cost-effective analytics, AI, and automation.
Dynatrace OpenPipeline empowers business, development, security, and operations teams with full visibility into and control of the data they are ingesting into the Dynatrace platform while preserving the context of the data and the cloud ecosystems where they originate.
Additionally, it evaluates data streams five to ten times faster than legacy technologies. As a result, organizations can better manage the ever-increasing volume and variety of data emanating from their hybrid and multicloud environments and empower more teams to access the Dynatrace platform’s AI-powered answers and automations without requiring additional tools.
Dynatrace OpenPipeline works with other core Dynatrace platform technologies, including the Grail™ data lakehouse, Smartscape® topology, and Davis® hypermodal AI, to deliver the following benefits:
- Petabyte scale data analytics: Leverages patent-pending stream processing algorithms to achieve significantly increased data throughputs at petabyte scale.
- Unified data ingest: Enables teams to ingest and route observability, security, and business events data–including dedicated Quality of Service (QoS) for business events–from any source and in any format, such as Dynatrace® OneAgent, Dynatrace APIs, and OpenTelemetry, with customizable retention times for individual use cases.
- Real-time data analytics on ingest: Allows teams to convert unstructured data into structured and usable formats at the point of ingest—for example, transforming raw data into time series or metrics data and creating business events from log lines.
- Full data context: Enriches and retains the context of heterogeneous data points—including metrics, traces, logs, user behavior, business events, vulnerabilities, threats, lifecycle events, and many others—reflecting the diverse parts of the cloud ecosystem where they originated.
- Controls for data privacy and security: Gives users control over which data they analyze, store, or exclude from analytics and includes fully customizable security and privacy controls, such as automatic and role-based PII masking, to help meet customers’ specific needs and regulatory requirements
- Cost-effective data management: Helps teams avoid ingesting duplicate data and reduces storage needs by transforming data into usable formats—for example, from XML to JSON—and enabling teams to remove unnecessary fields without losing any insights, context, or analytics flexibility.
“OpenPipeline is a powerful addition to the Dynatrace platform,” said Bernd Greifeneder, CTO at Dynatrace. “It enriches, converges, and contextualizes heterogeneous observability, security, and business data, providing unified analytics for these data and the services they represent. As with the Grail data lakehouse, we architected OpenPipeline for petabyte-scale analytics. It works with Dynatrace’s Davis hypermodal AI to extract meaningful insights from data, fueling robust analytics and trustworthy automation. Based on our internal testing, we believe OpenPipeline powered by Davis AI will allow our customers to evaluate data streams five to ten times faster than legacy technologies. We also believe that converging and contextualizing data within Dynatrace makes regulatory compliance and audits easier while empowering more teams within organizations to gain immediate visibility into the performance and security of their digital services.”
Dynatrace OpenPipeline is expected to be generally available for all Dynatrace SaaS customers within 90 days of this announcement, starting with support for logs, metrics, and business events.
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...