Best Anomaly Detection Software for Tableau
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Compare the Top Anomaly Detection Software that integrates with Tableau as of October 2025
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This a list of Anomaly Detection software that integrates with Tableau. Use the filters on the left to add additional filters for products that have integrations with Tableau. View the products that work with Tableau in the table below.
What is Anomaly Detection Software for Tableau?
Anomaly detection software identifies unusual patterns, behaviors, or outliers in datasets that deviate from expected norms. It uses statistical, machine learning, and AI techniques to automatically detect anomalies in real time or through batch analysis. This software is widely used in cybersecurity, fraud detection, predictive maintenance, and quality control. By flagging anomalies, it enables early intervention, reduces risks, and enhances operational efficiency. Advanced versions offer customizable thresholds, real-time alerts, and integration with analytics dashboards for deeper insights. Compare and read user reviews of the best Anomaly Detection software for Tableau currently available using the table below. This list is updated regularly.
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1
Splunk Enterprise
Cisco
Splunk Enterprise is a powerful platform that turns data into actionable insights across security, IT, and business operations. It enables organizations to search, analyze, and visualize data from virtually any source, providing a unified view across edge, cloud, and hybrid environments. With real-time monitoring, alerts, and dashboards, teams can detect issues quickly and act decisively. Splunk AI and machine learning features predict problems before they happen, improving resilience and decision-making. The platform scales to handle terabytes of data and integrates with thousands of apps, making it a flexible solution for enterprises of all sizes. Trusted by leading organizations worldwide, Splunk helps teams move from visibility to action. -
2
Dataiku
Dataiku
Dataiku is an advanced data science and machine learning platform designed to enable teams to build, deploy, and manage AI and analytics projects at scale. It empowers users, from data scientists to business analysts, to collaboratively create data pipelines, develop machine learning models, and prepare data using both visual and coding interfaces. Dataiku supports the entire AI lifecycle, offering tools for data preparation, model training, deployment, and monitoring. The platform also includes integrations for advanced capabilities like generative AI, helping organizations innovate and deploy AI solutions across industries. -
3
Metaplane
Metaplane
Monitor your entire warehouse in 30 minutes. Identify downstream impact with automated warehouse-to-BI lineage. Trust takes seconds to lose and months to regain. Gain peace of mind with observability built for the modern data era. Code-based tests take hours to write and maintain, so it's hard to achieve the coverage you need. In Metaplane, you can add hundreds of tests within minutes. We support foundational tests (e.g. row counts, freshness, and schema drift), more complex tests (distribution drift, nullness shifts, enum changes), custom SQL, and everything in between. Manual thresholds take a long time to set and quickly go stale as your data changes. Our anomaly detection models learn from historical metadata to automatically detect outliers. Monitor what matters, all while accounting for seasonality, trends, and feedback from your team to minimize alert fatigue. Of course, you can override with manual thresholds, too.Starting Price: $825 per month -
4
Acryl Data
Acryl Data
No more data catalog ghost towns. Acryl Cloud drives fast time-to-value via Shift Left practices for data producers and an intuitive UI for data consumers. Continuously detect data quality incidents in real-time, automate anomaly detection to prevent breakages, and drive fast resolution when they do occur. Acryl Cloud supports both push-based and pull-based metadata ingestion for easy maintenance, ensuring information is trustworthy, up-to-date, and definitive. Data should be operational. Go beyond simple visibility and use automated Metadata Tests to continuously expose data insights and surface new areas for improvement. Reduce confusion and accelerate resolution with clear asset ownership, automatic detection, streamlined alerts, and time-based lineage for tracing root causes.
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