Alternatives to Metaplane

Accelerate your data journey with AnalyticsCreator. Automate the design, development, and deployment of modern data architectures, including dimensional models, data marts, and data vaults or a combination of modeling techniques. Seamlessly integrate with leading platforms like Microsoft Fabric, Power BI, Snowflake, Tableau, and Azure Synapse and more. Experience streamlined development with automated documentation, lineage tracking, and schema evolution. Our intelligent metadata engine empowers rapid prototyping and deployment of analytics and data solutions. Reduce time-consuming manual tasks, allowing you to focus on data-driven insights and business outcomes. AnalyticsCreator supports agile methodologies and modern data engineering workflows, including CI/CD. Let AnalyticsCreator handle the complexities of data modeling and transformation, enabling you to unlock the full potential of your data

Anomalo helps you get ahead of data issues by automatically detecting them as soon as they appear and before anyone else is impacted. -Depth of Checks: Provides both foundational observability (automated checks for data freshness, volume, schema changes) and deep data quality monitoring (automated checks for data consistency and correctness). -Automation: Use unsupervised machine learning to automatically identify missing and anomalous data. -Easy for everyone, no-code UI: A user can generate a no-code check that calculates a metric, plots it over time, generates a time series model, sends intuitive alerts to tools like Slack, and returns a root cause analysis. -Intelligent Alerting: Incredibly powerful unsupervised machine learning intelligently readjusts time series models and uses automatic secondary checks to weed out false positives. -Time to Resolution: Automatically generates a root cause analysis that saves users time determining why an anomaly is occurring. Our triage feature orchestrates a resolution workflow and can integrate with many remediation steps, like ticketing systems. -In-VPC Development: Data never leaves the customer’s environment. Anomalo can be run entirely in-VPC for the utmost in privacy & security

Immuta's Data Access Platform is built to give data teams secure yet streamlined access to data. Every organization is grappling with complex data policies as rules and regulations around that data are ever-changing and increasing in number. Immuta empowers data teams by automating the discovery and classification of new and existing data to speed time to value; orchestrating the enforcement of data policies through Policy-as-code (PaC), data masking, and Privacy Enhancing Technologies (PETs) so that any technical or business owner can manage and keep it secure; and monitoring/auditing user and policy activity/history and how data is accessed through automation to ensure provable compliance. Immuta integrates with all of the leading cloud data platforms, including Snowflake, Databricks, Starburst, Trino, Amazon Redshift, Google BigQuery, and Azure Synapse. Our platform is able to transparently secure data access without impacting performance. With Immuta, data teams are able to speed up data access by 100x, decrease the number of policies required by 75x, and achieve provable compliance goals.

Examine the usage of your data assets, focusing on aspects like popularity, utilization, and schema coverage. Gain vital insights into your data assets, including their quality and usage metrics. You can easily locate and filter the necessary data by leveraging metadata tags and descriptions. Additionally, these insights will help you drive data governance and establish clear ownership within your organization. By implementing a streamlined lineage from data lakes to warehouses, you can enhance collaboration and accountability. An automatically generated field-level lineage map provides a comprehensive view of your entire data ecosystem. Moreover, anomaly detection systems adapt by learning from your data trends and seasonal variations, ensuring automatic backfilling with historical data. Thresholds driven by machine learning are specifically tailored for each data segment, relying on actual data rather than just metadata to ensure accuracy and relevance. This holistic approach empowers organizations to better manage their data landscape effectively.

Decube is a comprehensive data management platform designed to help organizations manage their data observability, data catalog, and data governance needs. Our platform is designed to provide accurate, reliable, and timely data, enabling organizations to make better-informed decisions. Our data observability tools provide end-to-end visibility into data, making it easier for organizations to track data origin and flow across different systems and departments. With our real-time monitoring capabilities, organizations can detect data incidents quickly and reduce their impact on business operations. The data catalog component of our platform provides a centralized repository for all data assets, making it easier for organizations to manage and govern data usage and access. With our data classification tools, organizations can identify and manage sensitive data more effectively, ensuring compliance with data privacy regulations and policies. The data governance component of our platform provides robust access controls, enabling organizations to manage data access and usage effectively. Our tools also allow organizations to generate audit reports, track user activity, and demonstrate compliance with regulatory requirements.

Detect and address code and optimization challenges in real-time, mitigate data incidents before deployment, and oversee data-affecting code modifications comprehensively—from the operational database to the user interface dashboard. With automated, column-level data lineage tracing the journey from the operational database to the reporting layer, every dependency is meticulously examined. Foundational automates the enforcement of data contracts by scrutinizing each repository in both upstream and downstream directions, directly from the source code. Leverage Foundational to proactively uncover code and data-related issues, prevent potential problems, and establish necessary controls and guardrails. Moreover, implementing Foundational can be achieved in mere minutes without necessitating any alterations to the existing codebase, making it an efficient solution for organizations. This streamlined setup promotes quicker response times to data governance challenges.

The contemporary data workspace transforms the accessibility of your data assets, making everything from data tables to BI reports easily discoverable. With our robust search algorithms and user-friendly browsing experience, locating the right asset becomes effortless. Atlan simplifies the identification of poor-quality data through the automatic generation of data quality profiles. This includes features like variable type detection, frequency distribution analysis, missing value identification, and outlier detection, ensuring you have comprehensive support. By alleviating the challenges associated with governing and managing your data ecosystem, Atlan streamlines the entire process. Additionally, Atlan’s intelligent bots analyze SQL query history to automatically construct data lineage and identify PII data, enabling you to establish dynamic access policies and implement top-notch governance. Even those without technical expertise can easily perform queries across various data lakes, warehouses, and databases using our intuitive query builder that resembles Excel. Furthermore, seamless integrations with platforms such as Tableau and Jupyter enhance collaborative efforts around data, fostering a more connected analytical environment. Thus, Atlan not only simplifies data management but also empowers users to leverage data effectively in their decision-making processes.

Aggua serves as an augmented AI platform for data fabric that empowers both data and business teams to access their information, fostering trust while providing actionable data insights, ultimately leading to more comprehensive, data-driven decision-making. Rather than being left in the dark about the intricacies of your organization's data stack, you can quickly gain clarity with just a few clicks. This platform offers insights into data costs, lineage, and documentation without disrupting your data engineer’s busy schedule. Instead of investing excessive time on identifying how a change in data type might impact your data pipelines, tables, and overall infrastructure, automated lineage allows data architects and engineers to focus on implementing changes rather than sifting through logs and DAGs. As a result, teams can work more efficiently and effectively, leading to faster project completions and improved operational outcomes.

Assisting businesses in actively overseeing their comprehensive data quality lifecycle is achieved through the implementation of contextual data quality assessments, anomaly detection, and corrective measures. By revealing anomalies and relevant metadata, teams are empowered to implement necessary corrective actions effectively. Automated remediation workflows can be initiated to swiftly and efficiently address any errors that arise. This proactive approach helps ensure superior data quality, safeguarding against inaccuracies that could undermine business decision-making. Additionally, the SLA chart offers a detailed overview of service level agreements, showcasing the total number of monitoring activities conducted and any violations encountered. Such insights can significantly aid in pinpointing specific areas of your data that may necessitate further scrutiny or enhancement. Ultimately, maintaining robust data quality is essential for driving informed business strategies and fostering growth.

Digna is a solution powered by AI that addresses the challenges of data quality management in modern times. It is domain agnostic and can be used in a variety of sectors, including finance and healthcare. Digna prioritizes privacy and ensures compliance with stringent regulations. It's also built to scale and grow with your data infrastructure. Digna is flexible enough to be installed on-premises or in the cloud, and it aligns with your organization's needs and security policies. Digna is at the forefront of data quality solutions. Its user-friendly design, combined with powerful AI analytics, makes Digna an ideal solution for businesses looking to improve data quality. Digna's seamless integration, real time monitoring, and adaptability make it more than just a tool. It is a partner on your journey to impeccable data quality.

Experience the implications of data-related problems without the need to execute SQL queries. Our approach involves a thorough analysis of your logs and metadata to uncover issues such as freshness and volume discrepancies, changes in table schemas, and errors within pipelines, along with their potential impacts on your business operations. Masthead continuously monitors all tables, processes, scripts, and dashboards in your data warehouse and integrated BI tools, providing immediate alerts to data teams whenever failures arise. It reveals the sources and consequences of data anomalies and pipeline errors affecting consumers of the data. By mapping data problems onto lineage, Masthead enables you to resolve issues quickly, often within minutes rather than spending hours troubleshooting. The ability to gain a complete overview of all operations within GCP without granting access to sensitive data has proven transformative for us, ultimately leading to significant savings in both time and resources. Additionally, you can achieve insights into the expenses associated with each pipeline operating in your cloud environment, no matter the ETL method employed. Masthead is equipped with AI-driven recommendations designed to enhance the performance of your models and queries. Connecting Masthead to all components within your data warehouse takes just 15 minutes, making it a swift and efficient solution for any organization. This streamlined integration not only accelerates diagnostics but also empowers data teams to focus on more strategic initiatives.

Safeguard business service-level agreements by utilizing dashboards that enable monitoring of service health, troubleshooting alerts, and conducting root cause analyses. Enhance mean time to resolution (MTTR) through real-time event correlation, automated incident prioritization, and seamless integrations with IT service management (ITSM) and orchestration tools. Leverage advanced analytics, including anomaly detection, adaptive thresholding, and predictive health scoring, to keep an eye on key performance indicators (KPIs) and proactively avert potential issues up to 30 minutes ahead of time. Track performance in alignment with business operations through ready-made dashboards that not only display service health but also visually link services to their underlying infrastructure. Employ side-by-side comparisons of various services while correlating metrics over time to uncover root causes effectively. Utilize machine learning algorithms alongside historical service health scores to forecast future incidents accurately. Implement adaptive thresholding and anomaly detection techniques that automatically refine rules based on previously observed behaviors, ensuring that your alerts remain relevant and timely. This continuous monitoring and adjustment of thresholds can significantly enhance operational efficiency.

Effortlessly monitor thousands of tables through machine learning-driven anomaly detection alongside a suite of over 50 tailored metrics. Ensure comprehensive oversight of both data and metadata while meticulously mapping all asset dependencies from ingestion to business intelligence. This solution enhances productivity and fosters collaboration between data engineers and consumers. Sifflet integrates smoothly with your existing data sources and tools, functioning on platforms like AWS, Google Cloud Platform, and Microsoft Azure. Maintain vigilance over your data's health and promptly notify your team when quality standards are not satisfied. With just a few clicks, you can establish essential coverage for all your tables. Additionally, you can customize the frequency of checks, their importance, and specific notifications simultaneously. Utilize machine learning-driven protocols to identify any data anomalies with no initial setup required. Every rule is supported by a unique model that adapts based on historical data and user input. You can also enhance automated processes by utilizing a library of over 50 templates applicable to any asset, thereby streamlining your monitoring efforts even further. This approach not only simplifies data management but also empowers teams to respond proactively to potential issues.

Deep Text Inspection encompasses anomaly detection and clustering, utilizing advanced AI to analyze all log data while providing real-time insights and alerts. With machine learning clustering, it identifies emerging errors and unique risk KPIs, among other metrics, through effective pattern recognition and discovery techniques. This solution offers robust anomaly detection for data risk and content monitoring, seamlessly integrating with platforms like Logstash, ELK, and more. Deployable in mere minutes, AiOpsX enhances existing monitoring and log analysis tools by employing millions of intelligent observations. It addresses various concerns including security, performance, audits, errors, trends, and anomalies. Utilizing distinctive algorithms, the system uncovers patterns and evaluates risk levels, ensuring continuous monitoring of risk and performance data to pinpoint outliers. The AiOpsX engine adeptly recognizes new message types, shifts in log volume, and spikes in risk levels while generating timely reports and alerts for IT monitoring teams and application owners, ensuring they remain informed and proactive in managing system integrity. Furthermore, this comprehensive approach enables organizations to maintain a high level of operational efficiency and responsiveness to emerging threats.

VictoriaMetrics Anomaly Detection, a service which continuously scans data stored in VictoriaMetrics to detect unexpected changes in real-time, is a service for detecting anomalies in data patterns. It does this by using user-configurable models of machine learning. VictoriaMetrics Anomaly Detection is a key tool in the dynamic and complex world system monitoring. It is part of our Enterprise offering. It empowers SREs, DevOps and other teams by automating the complex task of identifying anomalous behavior in time series data. It goes beyond threshold-based alerting by utilizing machine learning to detect anomalies, minimize false positives and reduce alert fatigue. The use of unified anomaly scores and simplified alerting mechanisms allows teams to identify and address potential issues quicker, ensuring system reliability.

Keep a close eye on your data health and the performance of your pipelines. Achieve comprehensive oversight for pipelines utilizing cloud-native technologies such as Apache Airflow, Apache Spark, Snowflake, BigQuery, and Kubernetes. This observability platform is specifically designed for Data Engineers. As the challenges in data engineering continue to escalate due to increasing demands from business stakeholders, Databand offers a solution to help you keep pace. With the rise in the number of pipelines comes greater complexity. Data engineers are now handling more intricate infrastructures than they ever have before while also aiming for quicker release cycles. This environment makes it increasingly difficult to pinpoint the reasons behind process failures, delays, and the impact of modifications on data output quality. Consequently, data consumers often find themselves frustrated by inconsistent results, subpar model performance, and slow data delivery. A lack of clarity regarding the data being provided or the origins of failures fosters ongoing distrust. Furthermore, pipeline logs, errors, and data quality metrics are often gathered and stored in separate, isolated systems, complicating the troubleshooting process. To address these issues effectively, a unified observability approach is essential for enhancing trust and performance in data operations.

Harness the power of AI for anomaly detection and root cause analysis focused on the key metrics that impact your business. Avora employs machine learning to oversee your business metrics around the clock, promptly notifying you of critical incidents so you can respond within hours instead of waiting for days or weeks. By continuously examining millions of records every hour for any signs of unusual activity, it reveals both potential threats and new opportunities within your organization. The root cause analysis feature helps you identify the elements influencing your business metrics, empowering you to implement swift, informed changes. You can integrate Avora’s machine learning features and notifications into your applications through our comprehensive APIs. Receive alerts about anomalies, shifts in trends, and threshold breaches via email, Slack, Microsoft Teams, or any other platform through Webhooks. Additionally, you can easily share pertinent insights with your colleagues and invite them to monitor ongoing metrics, ensuring they receive real-time notifications and updates. This collaborative approach enhances decision-making across the board, fostering a proactive business environment.

CloudNito, an AI-powered SaaS solution, is designed to help companies of all sizes optimize AWS cloud costs. Our solution combines real time monitoring, advanced anomaly detecting, and automated cost saving actions to eliminate wasteful cloud spending and improve efficiency. The following are the key features: AI-driven cost anomaly identification Automated resource scaling, optimization and scaling Cost allocation and reporting in detail Cost forecasting - a predictive tool Customizable alerts, thresholds and alerts CloudNito allows companies to save on their AWS costs, ensuring that they get the most out of their cloud investment.

Eliminate data outages by proactively identifying and resolving data quality problems before they enter production. Achieve full test coverage of your data pipelines in just one day, going from 0 to 100%. With automatic regression testing across billions of rows, understand the impact of each code modification. Streamline change management processes, enhance data literacy, ensure compliance, and minimize the time taken to respond to incidents. Stay ahead of potential data issues by utilizing automated anomaly detection, ensuring you're always informed. Datafold’s flexible machine learning model adjusts to seasonal variations and trends in your data, allowing for the creation of dynamic thresholds. Save significant time spent analyzing data by utilizing the Data Catalog, which simplifies the process of locating relevant datasets and fields while providing easy exploration of distributions through an intuitive user interface. Enjoy features like interactive full-text search, data profiling, and a centralized repository for metadata, all designed to enhance your data management experience. By leveraging these tools, you can transform your data processes and improve overall efficiency.

Auger.AI delivers the most comprehensive solution for maintaining the accuracy of machine learning models. Our MLRAM tool (Machine Learning Review and Monitoring) guarantees that your models maintain their accuracy over time. It even assesses the return on investment for your predictive models! MLRAM is compatible with any machine learning technology stack. If your ML system lifecycle lacks ongoing measurement of model accuracy, you could be forfeiting profits due to erroneous predictions. Additionally, frequently retraining models can be costly and may not resolve issues caused by concept drift. MLRAM offers significant benefits for both data scientists and business professionals, featuring tools such as accuracy visualization graphs, performance and accuracy notifications, anomaly detection, and automated optimized retraining. Integrating your predictive model with MLRAM requires just a single line of code, making the process seamless. We also provide a complimentary one-month trial of MLRAM for eligible users. Ultimately, Auger.AI stands out as the most precise AutoML platform available, ensuring that your machine learning initiatives are both effective and efficient.

Experience a seamless zero-touch copilot designed to enhance data quality, spending efficiency, performance metrics, and overall usage. Your data team will be promptly informed about any analytics failures or operational bottlenecks, ensuring no critical issues go unnoticed. We swiftly identify anomalies and notify you instantly, allowing you to maintain high data quality and prevent downtime. As performance metrics shift negatively, you will receive immediate alerts, enabling proactive measures. Our solution bridges the gap between data utilization and resource distribution, helping you to minimize costs and allocate resources effectively. We provide a detailed breakdown of your spending across various dimensions such as warehouse, user, and query, ensuring transparency and control. If spending patterns begin to deviate unfavorably, you'll be notified right away. Gain valuable insights into underutilized data and its implications for your business's value. Revel in the benefits of Revefi, which vigilantly monitors for waste and highlights opportunities to optimize usage against resources. With automated monitoring integrated into your data warehouse, manual data checks become a thing of the past. This allows you to identify root causes and resolve issues within minutes, preventing any adverse effects on your downstream users, thus enhancing overall operational efficiency. In this way, you can maintain a competitive edge by ensuring that your data-driven decisions are based on accurate and timely information.

Uncover the hidden order within your intricate data landscape and consistently know where to seek solutions. Datakin seamlessly tracks data lineage, presenting your entire data ecosystem through an engaging visual graph. This visualization effectively highlights the upstream and downstream connections associated with each dataset. The Duration tab provides an overview of a job’s performance in a Gantt-style chart, complemented by its upstream dependencies, which simplifies the identification of potential bottlenecks. When it's essential to determine the precise moment a breaking change occurs, the Compare tab allows you to observe how your jobs and datasets have evolved between different runs. Occasionally, jobs that complete successfully may yield poor output. The Quality tab reveals crucial data quality metrics and their fluctuations over time, making anomalies starkly apparent. By facilitating the swift identification of root causes for issues, Datakin also plays a vital role in preventing future complications from arising. This proactive approach ensures that your data remains reliable and efficient in supporting your business needs.

Bid farewell to abandoned data catalogs. Acryl Cloud accelerates time-to-value by implementing Shift Left methodologies for data producers and providing an easy-to-navigate interface for data consumers. It enables the continuous monitoring of data quality incidents in real-time, automating anomaly detection to avert disruptions and facilitating swift resolutions when issues arise. With support for both push-based and pull-based metadata ingestion, Acryl Cloud simplifies maintenance, ensuring that information remains reliable, current, and authoritative. Data should be actionable and operational. Move past mere visibility and leverage automated Metadata Tests to consistently reveal data insights and identify new opportunities for enhancement. Additionally, enhance clarity and speed up resolutions with defined asset ownership, automatic detection, streamlined notifications, and temporal lineage for tracing the origins of issues while fostering a culture of proactive data management.

Infrascale offers a cloud backup solution for enterprises that includes Anomaly Detection. This alerts you when the number or change of files significantly exceeds established benchmark levels. The Anomaly Detection feature is an important early warning system that can quickly identify a ransomware infection. It will also allow you to recover critical data before the entire network is frozen.

Shield34 stands out as the sole web automation framework that ensures complete compatibility with Selenium, allowing users to seamlessly continue utilizing their existing Selenium scripts while also enabling the creation of new ones through the Selenium API. It effectively tackles the notorious issue of flaky tests by implementing self-healing technology, intelligent defenses, error recovery protocols, and dynamic element locators. Furthermore, it offers AI-driven anomaly detection and root cause analysis, which facilitates a swift examination of failed tests to identify what changed and triggered the failure. By eliminating flaky tests, which often present significant challenges, Shield34 incorporates sophisticated defense-and-recovery AI algorithms into each Selenium command, including dynamic element locators, thereby reducing false positives and promoting self-healing alongside maintenance-free testing. Additionally, with its real-time root cause analysis capabilities powered by AI, Shield34 can swiftly identify the underlying reasons for test failures, minimizing the burden of debugging and the effort required to replicate issues. Ultimately, users can relish a more intelligent version of Selenium, as it effortlessly integrates with your existing testing framework while enhancing overall efficiency.

Creating and overseeing a thoroughly documented data project requires significant time and extensive manual coding, but that is no longer the case. We are confident in our ability to help you improve data transformation efficiency, and we can back that promise with results. Our column-aware architecture facilitates the reuse of data patterns and efficient change management on a large scale. By enhancing visibility around change management and impact analysis, we ensure safer and more predictable data operations. Coalesce offers specially curated packages containing best-practice templates that can automatically generate native-SQL for Snowflake™. If you have specific requirements, rest assured that our templates are fully customizable to suit your needs. Navigating through your data pipeline is a breeze with Coalesce, as every screen and button has been thoughtfully designed for easy access to all necessary tools. With Coalesce, your data team gains enhanced control over projects, allowing for features like side-by-side code comparison and immediate visibility into project and audit histories. Additionally, we guarantee that table-level and column-level lineage information is continuously updated and readily available, ensuring that your data remains accurate and reliable. Ultimately, Coalesce empowers your team to optimize workflows and focus on delivering insights rather than getting bogged down in administrative tasks.

Realize your net zero goals through a unified platform. With IoT Gateways and Edge Gateways, you can gather real-time information on energy consumption, water usage, and emissions directly from sensors or automated systems. Experience streamlined workflows and efficient data scheduling for precise and rapid data collection. Capture data across your entire value chain seamlessly. Consolidate all your sustainability metrics into a single repository leveraging our robust third-party integrations and APIs. Maintain a comprehensive, auditable record of data generation, modification requests, and corrections. Implement automated data validation checks and anomaly detection to ensure your data remains pristine and suitable for disclosure. Additionally, establish permission settings, workflows, and validation protocols for both automated and manual data corrections to enhance your overall data governance. This comprehensive approach ensures that your sustainability efforts are both effective and transparent.

In a developer-friendly visual editor, you can design, debug, run, and troubleshoot data jobflows and data transformations. You can orchestrate data tasks that require a specific sequence and organize multiple systems using the transparency of visual workflows. Easy deployment of data workloads into an enterprise runtime environment. Cloud or on-premise. Data can be made available to applications, people, and storage through a single platform. You can manage all your data workloads and related processes from one platform. No task is too difficult. CloverDX was built on years of experience in large enterprise projects. Open architecture that is user-friendly and flexible allows you to package and hide complexity for developers. You can manage the entire lifecycle for a data pipeline, from design, deployment, evolution, and testing. Our in-house customer success teams will help you get things done quickly.

Mona is a flexible and intelligent monitoring platform for AI / ML. Data science teams leverage Mona’s powerful analytical engine to gain granular insights about the behavior of their data and models, and detect issues within specific segments of data, in order to reduce business risk and pinpoint areas that need improvements. Mona enables tracking custom metrics for any AI use case within any industry and easily integrates with existing tech stacks. In 2018, we ventured on a mission to empower data teams to make AI more impactful and reliable, and to raise the collective confidence of business and technology leaders in their ability to make the most out of AI. We have built the leading intelligent monitoring platform to provide data and AI teams with continuous insights to help them reduce risks, optimize their operations, and ultimately build more valuable AI systems. Enterprises in a variety of industries leverage Mona for NLP/NLU, speech, computer vision, and machine learning use cases. Mona was founded by experienced product leaders from Google and McKinsey&Co, is backed by top VCs, and is HQ in Atlanta, Georgia. In 2021, Mona was recognized by Gartner as a Cool Vendor in AI Operationalization and Engineering.

All types of frauds can be addressed with one solution. Subex Fraud Management is a 25-year-old domain expertise that provides 360 degree fraud protection across digital service by leveraging advanced machine intelligence and signaling intelligence. This solution combines a traditional rule engine with advanced AI/machine learning capabilities to increase coverage across all services and minimize fraud run time in the network. It also includes real-time blocking capabilities. The Subex Fraud Management solution's core is a hybrid rule engine. It covers detection techniques such as expressions, thresholds, and trends. Rule engine comprises of a combination of threshold rules, geographic rules, pattern (sequential) rules, combinatorial rules, ratio/proportion-based rules, negative rules, hotlist based rules, etc. These rules allow you to monitor advanced threats in your network.

An open-source platform for monitoring machine learning models offers robust observability features. It allows users to evaluate, test, and oversee models throughout their journey from validation to deployment. Catering to a range of data types, from tabular formats to natural language processing and large language models, it is designed with both data scientists and ML engineers in mind. This tool provides everything necessary for the reliable operation of ML systems in a production environment. You can begin with straightforward ad hoc checks and progressively expand to a comprehensive monitoring solution. All functionalities are integrated into a single platform, featuring a uniform API and consistent metrics. The design prioritizes usability, aesthetics, and the ability to share insights easily. Users gain an in-depth perspective on data quality and model performance, facilitating exploration and troubleshooting. Setting up takes just a minute, allowing for immediate testing prior to deployment, validation in live environments, and checks during each model update. The platform also eliminates the hassle of manual configuration by automatically generating test scenarios based on a reference dataset. It enables users to keep an eye on every facet of their data, models, and testing outcomes. By proactively identifying and addressing issues with production models, it ensures sustained optimal performance and fosters ongoing enhancements. Additionally, the tool's versatility makes it suitable for teams of any size, enabling collaborative efforts in maintaining high-quality ML systems.

IBM Z Anomaly Analytics is a sophisticated software solution designed to detect and categorize anomalies, enabling organizations to proactively address operational challenges within their environments. By leveraging historical log and metric data from IBM Z, the software constructs a model that represents typical operational behavior. This model is then utilized to assess real-time data for any deviations that indicate unusual behavior. Following this, a correlation algorithm systematically organizes and evaluates these anomalies, offering timely alerts to operational teams regarding potential issues. In the fast-paced digital landscape today, maintaining the availability of essential services and applications is crucial. For businesses operating with hybrid applications, including those on IBM Z, identifying the root causes of issues has become increasingly challenging due to factors such as escalating costs, a shortage of skilled professionals, and shifts in user behavior. By detecting anomalies in both log and metric data, organizations can proactively uncover operational issues, thereby preventing expensive incidents and ensuring smoother operations. Ultimately, this advanced analytics capability not only enhances operational efficiency but also supports better decision-making processes within enterprises.

Anticipate issues before they arise by utilizing an Azure AI anomaly detection service. This service allows for the seamless integration of time-series anomaly detection features into applications, enabling users to quickly pinpoint problems. The AI Anomaly Detector processes various types of time-series data and intelligently chooses the most effective anomaly detection algorithm tailored to your specific dataset, ensuring superior accuracy. It can identify sudden spikes, drops, deviations from established patterns, and changes in trends using both univariate and multivariate APIs. Users can personalize the service to recognize different levels of anomalies based on their needs. The anomaly detection service can be deployed flexibly, whether in the cloud or at the intelligent edge. With a robust inference engine, the service evaluates your time-series dataset and automatically determines the ideal detection algorithm, enhancing accuracy for your unique context. This automatic detection process removes the necessity for labeled training data, enabling you to save valuable time and concentrate on addressing issues promptly as they arise. By leveraging advanced technology, organizations can enhance their operational efficiency and maintain a proactive approach to problem-solving.

Montara enables BI Teams and Data Analysts to model and transform data using SQL alone, easily and seamlessly, and enjoy benefits such a modular code, CI/CD and versioning, automated testing and documentation. With Montara, analysts are able to quickly understand the impact of changes in models on analysis, reports, and dashboards. Report-level lineage is supported, as well as support for 3rd-party visualization tools like Tableau and Looker. BI teams can also perform ad hoc analysis, create dashboards and reports directly on Montara.

Google Cloud's Dataplex serves as an advanced data fabric that empowers organizations to efficiently discover, manage, monitor, and govern their data across various platforms, including data lakes, warehouses, and marts, while maintaining uniform controls that ensure access to reliable data and facilitate large-scale analytics and AI initiatives. By offering a cohesive interface for data management, Dataplex streamlines processes like data discovery, classification, and metadata enhancement for diverse data types, whether structured, semi-structured, or unstructured, both within Google Cloud and external environments. It organizes data logically into business-relevant domains through lakes and data zones, making data curation, tiering, and archiving more straightforward. With its centralized security and governance features, Dataplex supports effective policy management, robust monitoring, and thorough auditing across fragmented data silos, thereby promoting distributed data ownership while ensuring global oversight. Furthermore, the platform includes automated data quality assessments and lineage tracking, which enhance the reliability and traceability of data, ensuring organizations can trust their data-driven decisions. By integrating these functionalities, Dataplex not only simplifies data management but also enhances collaboration within teams focused on analytics and AI.

Intelligent Threat Detection. Faster response. Active Directory is the root cause of 98% of all security threats. Nearly all of these threats involve data compromises on enterprise data storage. Our unique combination of detailed auditing and anomaly detection, real-time alerting, real-time data discovery and classification makes it easy to quickly identify, prioritize, and investigate threats. Protect sensitive data from rogue users and compromised user accounts. Our technology allows you to detect and investigate data threats to your most sensitive data like no other vendor. Data classification and data discovery are combined with threat detection to allow you to examine all events, changes and actions with context. You have complete visibility of Active Directory, Group Policy and File Servers, Office 365. NetApp, SharePoint. Box, Dropbox, Office 365. Security threats 10x faster detected and responded to Active Directory allows you to track movements and investigate threats as soon as they arise.

The Real-time Anomaly Detection solution enables the identification of unusual user behaviors or specific actions that deviate from established patterns within a dataset. These expected patterns can be derived from historical data or customized datasets tailored to your needs, reflecting our strong emphasis on personalization at Tatvic. With this solution, you can discern whether a sudden increase in traffic to your website or application is caused by bots and spam or if it is influenced by other external elements. Additionally, the Real-time Anomaly Detection solution highlights issues on your site, such as a disrupted user experience resulting from a recent change or update. For more intricate websites, this tool is invaluable for monitoring the overall performance and operational status of your website and application, ensuring they function seamlessly. By implementing this solution, businesses can proactively address potential issues before they escalate, enhancing user satisfaction and retention.

In just 15 minutes, you can set up your automated data catalogue and receive column-level lines, Entity Relationship diagrams, and auto-populated documentation in 24 hours. You can easily tag, find, and add documentation to data so everyone can find the right one for them. Select Star automatically detects your column-level data lineage and displays it. Now you can trust the data by knowing where it came. Select Star automatically displays how your company uses data. This allows you to identify relevant data fields without having to ask anyone else. Select Star ensures that your data is protected with AICPA SOC2 Security, Confidentiality and Availability standards.

SmartFlow is an advanced IT cybersecurity monitoring solution that employs Anomaly Detection to identify elusive security risks. It serves as an enhancement to traditional signature-based monitoring systems. By scrutinizing network flow traffic, SmartFlow is adept at uncovering zero-day attacks. Designed specifically for medium to large enterprises, this appliance-based tool leverages patented anomaly detection methods and network behavior analysis to spot potential threats within a network. Utilizing Solana algorithms, it processes flow data like Netflow to identify various threats, including address scans, DDoS attacks, botnets, port scans, and malware. Unlike signature-based systems, which may overlook zero-day threats and encrypted malicious traffic, SmartFlow ensures comprehensive detection of these risks. It effectively transforms network traffic and flow data into over 20 distinct statistical metrics, which are then continuously monitored to provide early alerts regarding cyber threats. In doing so, SmartFlow not only enhances security but also offers peace of mind for organizations seeking to safeguard their digital assets.

NEMESIS is an advanced AI-driven technology for anomaly detection that specializes in identifying fraud and inefficiencies. This next-generation platform not only highlights opportunities for improved efficiency within your business management systems, but it also serves as a customizable enterprise solution that allows business analysts to quickly convert data into meaningful insights. By harnessing the capabilities of artificial intelligence, NEMESIS addresses challenges including overstaffing, medical inaccuracies, care quality, and fraudulent claims. With its continuous process monitoring, NEMESIS reveals various risks, from anticipating quality concerns to uncovering waste and misuse. Utilizing machine learning and AI, it effectively identifies fraudulent activities and schemes before they can impact your financial resources. Furthermore, NEMESIS enhances your ability to manage expenses and track budget discrepancies, ensuring ongoing transparency regarding waste and abuse. This comprehensive approach not only improves operational efficiency but also fosters a more accountable financial environment within your organization.

Supervizor's continuous quality assurance, featuring unmatched anomaly detection, is designed to eliminate errors in accounting and mitigate fraud risks. Our goal is to empower companies to generate trustworthy financial information. With distinctive anomaly detection features, Supervizor enables organizations to pinpoint various types of mistakes, including those related to accounting, as well as potential fraud attempts. As errors are systematically created by processes and personnel, companies are increasingly facing sophisticated fraud schemes. By connecting your ERP system, Supervizor can aggregate journal entries utilizing a comprehensive library filled with millions of accounting patterns. You can run ready-to-use checks continuously across diverse areas, fostering collaboration among teams to ensure the quality of financial data across different subsidiaries, systems, departments, and regions. The platform also automates the extraction and preparation of your data, saving you from the tedious tasks of manual gathering, scrubbing, and formatting. Additionally, it smartly identifies and ranks your most critical findings for investigation, effectively reducing the likelihood of false positives while enhancing overall accuracy. Through these capabilities, Supervizor not only enhances financial integrity but also streamlines the auditing process for organizations.

A low-code, no-code strategy enhances data quality management. This software-as-a-service (SaaS) model offers flexibility, cost-effectiveness, seamless integration, and robust support options. It maintains rigorous standards for encryption, identity management, role-based access control, data governance, and compliance. Utilizing advanced machine learning algorithms, it identifies anomalies in row-value data, with the capability to evolve alongside the unique requirements of users' businesses and datasets. Users can incorporate numerous data sources, records, and attributes effortlessly, making the platform resilient to unexpected increases in data volume. It accommodates both batch and streaming processing, ensuring that data is consistently monitored to provide real-time alerts without affecting pipeline performance. The platform offers a smooth onboarding, integration, and investigation process, making it accessible to data teams aiming to proactively spot and analyze anomalies as they arise. With a no-code onboarding process, users can simply connect to their data sources and set their alerting preferences. Telmai intelligently adapts to data patterns, notifying users of any significant changes, ensuring that they remain informed and prepared for any data fluctuations.

Our Managed Detection and Response (MDR) service is specifically crafted for superior threat detection, proactive threat hunting, anomaly identification, and offering responsive guidance through a comprehensive defense-in-depth strategy that continuously observes and integrates data from network activities, endpoints, logs, and various other sources. In contrast to a conventional Managed Security Service Provider (MSSP), our approach emphasizes proactive threat prevention rather than merely reactive measures. To achieve this, we employ cutting-edge technologies in cloud computing and big data analytics, alongside advanced machine learning algorithms, all supported by the foremost incident response team in the cybersecurity field to effectively pinpoint risks to your systems. Our methodology harnesses a blend of top-tier commercial solutions, open-source resources, and proprietary tools to ensure the highest level of monitoring accuracy. Additionally, we have formed a partnership with Cylance to deliver unparalleled endpoint threat detection and prevention through their innovative solution, CylancePROTECT(™), ensuring that our clients have access to the most effective protection available today. This commitment to leveraging the latest technology and expert collaboration positions us as leaders in proactive cybersecurity solutions.

Automate the detection of anomalies to comply with service level agreements while overseeing the entire network landscape. Enhance digital experiences by modernizing security protocols, utilizing an agentless, AI-driven strategy that takes advantage of your current infrastructure. Implement a least privilege access policy and establish identity-based protections that encompass applications, devices, and the overall infrastructure. Receive immediate alerts for any escalations and review comprehensive session activities and details through integrated logging systems. Facilitate device fingerprinting to achieve valuable insights into network topology, while ensuring smooth integration with existing infrastructure. Streamline connectivity and governance from campus environments to cloud services. With Lumeus, organizations can harness AI to monitor and identify escalations, segment traffic to thwart lateral movement, and enhance user access security through the implementation of multi-factor authentication and zero trust principles, all managed from a single, coherent platform. Additionally, Lumeus provides a cloud management portal that connects seamlessly to your infrastructure via API, allowing for enhanced oversight and control. This holistic approach enables organizations to respond proactively to threats and optimize their security posture effectively.

Amazon GuardDuty serves as a proactive threat detection solution that consistently observes for harmful activities and unauthorized actions to safeguard your AWS accounts, workloads, and data housed in Amazon S3. While the cloud facilitates the effortless collection and aggregation of both account and network activities, security teams often find it labor-intensive to continuously sift through event log data in search of potential threats. GuardDuty offers a smart and budget-friendly alternative for ongoing threat detection within the AWS environment. Utilizing machine learning, anomaly detection, and built-in threat intelligence, this service effectively identifies and ranks potential threats. It scrutinizes tens of billions of events across various AWS data sources, including AWS CloudTrail event logs, Amazon VPC Flow Logs, and DNS logs. Enabling GuardDuty requires just a few clicks in the AWS Management Console, and there is no need to deploy or manage any software or hardware. This streamlined process allows organizations to focus more on their core activities, knowing that their cloud infrastructure is being continuously monitored for security risks.