‘Observability is extremely crucial in cloud-native environments’ says Subbu Subramanian, Dynatrace
CXOToday spoke to Subbu Subramanian, Country Director – India, Dynatrace basis that he responded to the e-interaction
- Share a brief about Dynatrace
Response– Dynatrace is a unified platform for observability and security that enables organizations to automate processes, elevate software delivery, boost security, and enhance user experiences. Dynatrace solutions helps businesses to automate operations and enhance the performance, security, and reliability of modern applications and infrastructure in hybrid and multicloud environments. It combines observability, business, and security data with continuous runtime application security, AIOps, and automation to provide answers and intelligent automation from data.
Owing to its capabilities, Dynatrace serves a broad range of audiences, including operations (ITOps, cloud operations, site reliability engineering, SecOps), development (DevOps, developers, and architects), security (SecOps), and business (application owners, CXOs).
What sets Dynatrace apart is its unique approach and specialized technologies. OneAgent® services automatically discover, activate, and instrument applications, microservices, infrastructure, and dependencies in modern cloud environments for effective monitoring. PurePath® distributed tracing analyzes code-level context to pinpoint performance bottlenecks for seamless user experiences. Smartscape® topology dynamically maps relationships among components, enhancing architecture understanding. Grail™ data lakehouse, with Dynatrace Query Language (DQL), provides AI-powered analytics with context-rich information. The Davis® causal AI engine diligently analyzes intricate IT environments, delivering precise, business impact-focused answers and proactive alerts for swift resolution. Dynatrace streamlines enterprise cloud complexities, ensuring exceptional performance for your applications.
2. How is the company redefining multicloud operations for enterprises?
Response– Dynatrace firmly believes that legacy monitoring tools are not appropriate for modern cloud infrastructures. As a unified observability and security platform, it leverages a combination of advanced AI and ML to automate monitoring, detect performance issues, and forecast problems in real-time. Managing the complexity of a multi-cloud environment is an enormous challenge. Microservices, containers, and orchestration platforms like Kubernetes are used in cloud-native architectures, adding complexity to deployment, monitoring, and management.
Dynatrace full-stack observability offers deep and definite insights into applications and infrastructure across multi-cloud environments. With VMware, AWS EC2, Azure VMs, or GCP Compute Engines, among other enterprise-cloud technologies, Dynatrace cloud monitoring tools offer complete transparency while identifying and working on blind spots. It excels in proactive and predictive decision-making, resolving issues before they escalate. It is scalable, flexible, and integrates seamlessly with DevOps processes. By linking performance to business outcomes, Dynatrace enables prioritized actions for overall business success. With unified monitoring, AI-driven automation, and real-time insights, Dynatrace masterfully assists enterprises in optimizing multicloud environments for enhanced performance and user experiences.
3. What is the role of observability in cloud-native environments?
Response– Observability is extremely crucial in cloud-native environments, providing real-time insights and driving business performance. It grants organizations comprehensive visibility into their entire system, spanning multiple cloud platforms, monitoring applications, infrastructure, and services across various clouds. By analyzing extensive data, observability offers real-time insights into system performance, security and availability. This capability empowers organizations to swiftly detect and resolve issues, proactively optimizing their multi cloud environment for enhanced application performance and minimized downtime. With observability’s data-driven approach, organizations can make informed decisions and identify patterns, trends, and anomalies to optimize resources and improve performance.
Observability fosters effective collaboration and troubleshooting, providing a unified view of the system and facilitating seamless teamwork across different clouds. This leads to efficient problem-solving, faster incident resolution, and heightened operational efficiency. Hence, observability is indispensable in multi cloud environments, providing real-time insights, driving business performance, facilitating proactive issue resolution, enabling data-driven decision-making, and promoting effective collaboration. Leveraging observability allows organizations to optimize their multi cloud environment, drive business growth, and deliver exceptional user experiences.
4. How is AI playing a vital role in automating business processes? Also, elaborate on how secure and effective is it.
Response– Organizations are adopting AI extensively to streamline business processes and improve decision-making accuracy. Automation is one area significantly impacted by AI, as advanced ML algorithms enable the automation of repetitive and time-consuming tasks. Recently, Dynatrace introduced the first hypermodal artificial intelligence, expanding its Davis AI engine. The enhanced Dynatrace® Davis boosts productivity across various teams by providing generative AI recommendations supported by precise context from causal and predictive AI techniques tailored to each organization’s unique hybrid and multi cloud ecosystem.
Despite the benefits, data security concerns have hindered AI deployment. However, AI itself is part of the solution to address these worries. AI-powered security technologies can swiftly detect potential threats by analyzing vast data. AI-driven security solutions use anomaly detection to identify unusual application behavior patterns that may indicate vulnerabilities or breaches. Moreover, AI reinforces data security through fraud detection, data encryption, and access management. It aids in compliance by automating tasks such as risk assessment and data access monitoring while continuously scanning for security holes and vulnerabilities to maintain effective data protection procedures.
Effectiveness is influenced by variables like data reliability, model precision, and effective application. For automation to be successful, high-quality data and well constructed AI models must be acquired. To address potential biases and ethical concerns that AI systems may display, human monitoring is also essential. AI can help automate business operations and promote innovation when properly integrated and safeguarded.
