How low-code/no-code AutomationEngine advances automated workflows

Organizations can ingest, process, and analyze large volumes and varieties of data in one platform. They can now also use low-code/no-code automated workflows to reduce manual work—and they don’t need to be a developer to do so.

Cloud environments have become ever more complex, with an increasingly interconnected set of services. To tame this complexity and deliver differentiated digital experiences, IT, development, security, and business teams need automated workflows throughout these cloud ecosystems. But to be scalable, they also need low-code/no-code solutions that don’t require a lot of spin-up or engineering expertise.

IT leaders know that managing cloud environments through traditional manual monitoring practices will no longer suffice. According to recent Dynatrace data, 59% of CIOs say the increasing complexity of their technology stack could soon overload their teams without a more automated approach to IT operations and automated workflows.

Further, according to a Gartner® report, by 2025, 95% of decisions that currently use data will be at least partially automated.*

Development teams need automated workflows so they’re not stuck manually monitoring all stages of the software development lifecycle in their cloud environments. Similarly, security teams need to discover and automatically route security vulnerabilities to the right people to ensure prompt action. And operations teams need to forecast cloud infrastructure and compute resource requirements, then automatically provision resources to optimize digital customer experiences.

With the Dynatrace modern observability platform, teams can now use intuitive, low-code/no-code toolsets and causal AI to extend answer-driven automation for business, development and security workflows.

Low-code/no-code AutomationEngine fuels workflow automation

The Dynatrace platform brings modern observability and workflow automation to the fore.

With AutomationEngine, IT teams can now use their observability, security, and business data to automate workflows throughout hybrid and multicloud ecosystems.

For example, with AutomationEngine, teams can automate remediation and progressive delivery to continuously evaluate application performance against specific, measurable service-level objectives.

AutomationEngine also enables automated routing of security vulnerabilities to the proper teams while reducing false positives to ensure prompt action.

But automating workflows requires precise, accurate, and real-time answers from data that’s trustworthy.

This is the role of Dynatrace causal AI, fueled by Davis and topological mapping through Smartscape. With Davis and Smartscape, the Dynatrace platform provides teams with precise answers about the source of problems and incidents. Then, teams can use the AutomationEngine and its easy low-code/no-code interface to create automated workflows to enable various tasks that previously required manual work. An automated workflow, for example, might identify an application issue, send an alert to ServiceNow, generate a help desk ticket, and even trigger a remediation step.

All these tasks can take place seamlessly—ultimately resolving issues or identifying them proactively—and without needing to interrupt an engineer from a more strategic task.

Using Dynatrace AutomationEngine, teams can forecast future requirements and automate the provisioning of cloud infrastructure and compute resources to optimize the user experience. In addition, they can automatically route precise answers about performance and security anomalies to relevant teams to ensure action in a timely and efficient manner.

Bringing precise, answer-driven automation to observability, security, and business

As IT operations teams, security, DevOps, and others look to automate workflows, they need a solid platform foundation that enables data in context as well as provides precise and trustworthy answers.

To date, traditional observability tools ingest and process only partial data in silos which makes them ineffective to truly address application performance or security issues. Others lack causal AI to process data in full context that can actually pinpoint the root cause of problems.

With a data lakehouse, DevSecOps and business teams can aggregate, store, and centralize structured and unstructured data in one cost-efficient repository without predetermining which data is going to be necessary to generate insights in the future.

The Dynatrace Grail data lakehouse enables teams to ingest logs, metrics, traces, business events, and other data to get a full picture of their hybrid and multicloud environments. Because all the data is already contextualized and centralized, teams no longer have to manually sift through data and alerts to identify the team responsible for the problem. In turn, AutomationEngine uses this data in context to eliminate manual tasks and enable data-driven, automated workflows.

The low-code/no-code AutomationEngine brings several benefits to customers.

1. AutomationEngine enables user-friendly low-code/no-code automation

Even non-automation engineers can easily create ad-hoc automated workflows using a low-code/no-code interface. For example, teams can schedule a routine task or routing alerts with a visual drag-and-drop workflow creation experience. Operations teams can visualize their runbook automation as boxes and lines with flow logic for operational tasks. For example, using drag-and-drop on visual elements, teams can create an automation that queries a ticketing system like Jira or sends a Slack message to a specific person upon discovering an issue or vulnerability.

Cloud-native engineers benefit by creating GitOps-style automation-as-code and can evaluate Kubernetes events for faster issue remediation. And, ultimately, platform and site reliability engineers can provide answer-driven automation and higher-quality software with automated security and quality gates.

2. Tool consolidation reduces complexity

AutomationEngine reduces the number of home-grown tools teams need to rely on and improves the interoperability of ecosystem tools. As a result, it becomes easier to automate processes and reduce complexity. Further, AutomationEngine provides a centralized platform for managing and optimizing automation processes. This helps organizations chart a path to automation, improving efficiency and reducing the risk of errors or inconsistencies.

3. Compliance and data privacy are built into automated workflows

With AutomationEngine woven into the Dynatrace platform, it reduces the risk of sensitive data leaving the system. Further, all automated workflows are governed by an audit trail, access control, SSO, and security protection. This unburdens teams so they can concentrate on the actual automation task. Finally, a dedicated EdgeConnect component enables secure integrations for on-premises systems.

4. Answer-driven automation

When automation is based on false positives, it creates a situation of garbage in, garbage out. AutomationEngine is integrated within the Dynatrace platform, including Grail and Davis AI. Trustworthy, accurate answers and insights are a prerequisite for reliable security, business, and IT automation, especially when implementing feedback loop-based automation. This is true for workflow-based automation, as well as modern, event-driven cloud-native automation. With the ability to initiate and orchestrate automated actions throughout the ecosystem of collaboration—including ITSM, DevOps, and security tools–answer-driven automation delivers immediate and significant value to the organization.

In addition to the low-code/no-code AutomationEngine, Dynatrace announced a host of platform enhancements at Perform 2023 in Las Vegas. These enhancements further enable IT, DevOps, and security pros to move from data in context to insights on which they can execute with confidence, including the following:

• expanded Grail data lakehouse capabilities to enable new data types and graph analytics;
• a new user experience and interface and updated dashboards to more easily visualize trends;
• data Notebooks for petabyte-scale data exploration and analytics for real-time insights;
• AppEngine to create custom, compliant data-driven apps for answers and automation.

For more on AutomationEngine, visit our website.

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* ^{Gartner, “Emerging Tech: Venture Capital Growth Insights for Decision Intelligence Platforms,” Aakanksha Bansal, Alys Woodward, Akhil Singh, 10 February 2023.}

^{GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.}