Alternatives to Monte Carlo

Auvik Network Management is a network management and monitoring software designed to empower IT professionals with deep visibility, automation, and control over their network infrastructure. This innovative platform is trusted by businesses of all sizes to streamline network operations, enhance security, and optimize performance. One of Auvik's standout features is its real-time network mapping and discovery capabilities. It automatically generates interactive, visual maps of your network topology, allowing you to easily identify devices, connections, and potential bottlenecks. This invaluable insight helps in planning and optimizing network architecture for maximum efficiency.

Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.

IT performance monitoring does not just focus on monitoring CPU, memory, and network resources. eG Enterprise makes the user experience the center of your IT management and monitoring strategy. eG Enterprise allows you to measure the digital experience of your users and get deep visibility into the performance of the entire application delivery chain -- from code to user experiences to data center to cloud -- all from a single pane. You can also correlate performance across domains to pinpoint the root cause of problems proactively. eG Enterprise's machine learning and analytics capabilities enable IT teams to make smart decisions about right-sizing and optimizing for future growth. The result is happier users, increased productivity, improved IT efficiency, and tangible business ROI. eG Enterprise can be installed on-premise or as a SaaS service. Get a free trial of eG Enterprise today.

Edge Delta is a new way to do observability. We are the only provider that processes your data as it's created and gives DevOps, platform engineers and SRE teams the freedom to route it anywhere. As a result, customers can make observability costs predictable, surface the most useful insights, and shape your data however they need. Our primary differentiator is our distributed architecture. We are the only observability provider that pushes data processing upstream to the infrastructure level, enabling users to process their logs and metrics as soon as they’re created at the source. Data processing includes: * Shaping, enriching, and filtering data * Creating log analytics * Distilling metrics libraries into the most useful data * Detecting anomalies and triggering alerts We combine our distributed approach with a column-oriented backend to help users store and analyze massive data volumes without impacting performance or cost. By using Edge Delta, customers can reduce observability costs without sacrificing visibility. Additionally, they can surface insights and trigger alerts before data leaves their environment.

Coralogix is the most popular stateful streaming platform, providing engineering teams with real-time insight and long-term trend analysis without relying on storage or indexing. To manage, monitor, alert, and manage your applications, you can import data from any source. Coralogix automatically narrows the data from millions of events to common patterns, allowing for faster troubleshooting and deeper insights. Machine learning algorithms constantly monitor data patterns and flows among system components and trigger dynamic alarms to let you know when a pattern is out of the norm without the need for static thresholds or pre-configurations. Connect any data in any format and view your insights anywhere, including our purpose-built UI and Kibana, Grafana as well as SQL clients and Tableau. You can also use our CLI and full API support. Coralogix has successfully completed the relevant privacy and security compliances by BDO, including SOC 2, PCI and GDPR.

Epsagon allows teams to instantly visualize, understand, and optimize their microservice architectures. With our unique lightweight auto-instrumentation, gaps in data and manual work associated with other APM solutions are eliminated, providing significant reductions in issue detection, root cause analysis and resolution times. Epsagon can increase development speed and reduce application downtime.

All data sources, including topology, monitoring, change, and observation tools, are aggregated. BigPanda's Open Box Machine Learning will combine the data into a limited number of actionable insights. This allows incidents to be detected as they occur, before they become outages. Automatically identifying the root cause of problems can speed up incident and outage resolution. BigPanda identifies both root cause changes and infrastructure-related root causes. Rapidly resolve outages and incidents. BigPanda automates the incident response process, including ticketing, notification, tickets, incident triage, and war room creation. Integrating BigPanda and enterprise runbook automation tools will accelerate remediation. Every company's lifeblood is its applications and cloud services. Everyone is affected when there is an outage. BigPanda consolidates AIOps market leadership with $190M in funding and a $1.2B valuation

Do you struggle to manage all those alerts and tickets that come in? Dell APEX AIOps can reduce noise, detect incidents sooner, and fix issues faster. Do not let a flood alerts slow you. We remove these annoying alerts automatically so that you can enjoy your day without distraction. Never look at a ticket again. We send you "Situations" instead of tickets so you can fix problems faster before your customers complain. Stop wasting your time switching between tools. We bring all the tools together in one place, so you can manage any incident regardless of its origin. Use AI and ML to identify patterns and prevent them from happening again. Continuous delivery means continuous changes. Dell APEX AIOps automates the incident management workflow to provide continuous improvement. This gives you more time for other important and enjoyable tasks.

LogicMonitor is the leading SaaS-based, fully-automated observability platform for enterprise IT and managed service providers. Cloud-first and hybrid ready. LogicMonitor helps enterprises and managed service providers gain IT insights through comprehensive visibility into networks, cloud, applications, servers, log data and more within one unified platform. Drive collaboration and efficiency across IT and DevOps teams, in a fully secure, intelligently automated platform. By providing end-to-end observability for enterprise businesses, LogicMonitor connects coders to consumers, customer experience to the cloud, infrastructure to applications and business insights into instant actions. Maximize uptime, optimize end-user experience, predict what comes next, and keep your business fearlessly moving forward.

Datadog is the cloud-age monitoring, security, and analytics platform for developers, IT operation teams, security engineers, and business users. Our SaaS platform integrates monitoring of infrastructure, application performance monitoring, and log management to provide unified and real-time monitoring of all our customers' technology stacks. Datadog is used by companies of all sizes and in many industries to enable digital transformation, cloud migration, collaboration among development, operations and security teams, accelerate time-to-market for applications, reduce the time it takes to solve problems, secure applications and infrastructure and understand user behavior to track key business metrics.

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.

ServiceNow Cloud Observability provides real-time visibility and monitoring of cloud infrastructure, applications and services. It allows organizations to identify and resolve performance problems by integrating data from different cloud environments into a single dashboard. ServiceNow Cloud Observability's advanced analytics and alerting features help IT and DevOps departments detect anomalies, troubleshoot issues, and ensure optimal performance. The platform supports AI-driven insights and automation, allowing teams the ability to respond quickly to incidents. Overall, the platform improves operational efficiency while ensuring a seamless user-experience across cloud environments.

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.

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.

Improving business outcomes involves making it easier to spot and address high-priority incidents. The WatchTower Platform serves as a comprehensive observability tool that streamlines incident resolution specifically within mainframe environments by effectively integrating and correlating events, data flows, and metrics across various IT silos. It provides a cohesive and intuitive interface for operations teams, allowing them to optimize their workflows. Leveraging established AIOps solutions, WatchTower is adept at detecting potential problems at an early stage, which aids in proactive mitigation. Additionally, it utilizes OpenTelemetry to transmit mainframe data and insights to observability tools, allowing enterprise SREs to pinpoint bottlenecks and improve operational effectiveness. By enhancing alerts with relevant context, WatchTower eliminates the necessity for logging into multiple tools to gather essential information. Its workflows expedite the processes of problem identification, investigation, and incident resolution, while also simplifying the handover and escalation of issues. With such capabilities, WatchTower not only enhances incident management but also empowers teams to proactively maintain high service availability.

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

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.

Navigating performance visibility in today’s intricate IT landscapes can be quite challenging, particularly with applications that traverse traditional data centers, SaaS, and IaaS cloud setups. A conventional, siloed management approach often leads to a disjointed and incomplete understanding of performance metrics. Consequently, IT teams find themselves dedicating significant time to data analysis, frequently arriving at divergent and sometimes contradictory insights regarding the root causes of performance issues. The Riverbed Portal addresses this issue by consolidating performance telemetry, resulting in a unified and dynamic overview of performance metrics. This comprehensive perspective provides IT operations teams with a reliable single source of truth, which streamlines troubleshooting processes and delivers valuable insights for stakeholders across the organization. Ultimately, this enables IT to effectively manage and enhance applications, data, and traffic within the entire hybrid network, allowing key resources to concentrate on high-priority strategic initiatives while reducing the likelihood of performance-related disputes. By fostering a clearer understanding of performance, teams can make informed decisions that drive overall efficiency and effectiveness within the organization.

SigNoz serves as an open-source alternative to Datadog and New Relic, providing a comprehensive solution for all your observability requirements. This all-in-one platform encompasses APM, logs, metrics, exceptions, alerts, and customizable dashboards, all enhanced by an advanced query builder. With SigNoz, there's no need to juggle multiple tools for monitoring traces, metrics, and logs. It comes equipped with impressive pre-built charts and a robust query builder that allows you to explore your data in depth. By adopting an open-source standard, users can avoid vendor lock-in and enjoy greater flexibility. You can utilize OpenTelemetry's auto-instrumentation libraries, enabling you to begin with minimal to no coding changes. OpenTelemetry stands out as a comprehensive solution for all telemetry requirements, establishing a unified standard for telemetry signals that boosts productivity and ensures consistency among teams. Users can compose queries across all telemetry signals, perform aggregates, and implement filters and formulas to gain deeper insights from their information. SigNoz leverages ClickHouse, a high-performance open-source distributed columnar database, which ensures that data ingestion and aggregation processes are remarkably fast. This makes it an ideal choice for teams looking to enhance their observability practices without compromising on performance.

Logit.io are a centralized logging and metrics management platform that serves hundreds of customers around the world, solving complex problems for FTSE 100, Fortune 500 and fast-growing organizations alike. The Logit.io platform delivers you with a fully customized log and metrics solution based on ELK, Grafana & Open Distro that is scalable, secure and compliant. Using the Logit.io platform simplifies logging and metrics, so that your team gains the insights to deliver the best experience for your customers.

After a few late-night code fires, we set out to find application performance management tools that would help us stop them. We were able to identify what was wrong, but it didn't tell us why or how to prevent future failures. Retrace was created to do just that. We believe that when our 1300+ customers spend less of their time fighting technology, they spend more time releasing it. This makes the world a better place.

DX Unified Infrastructure Management stands out as the sole solution offering an open architecture, comprehensive observability across the entire stack, and a zero-touch configuration approach for effectively monitoring traditional data centers, public cloud platforms, and hybrid infrastructure setups. This solution is crafted to enhance the end-user experience and features a contemporary HTML5 operations console that enables IT teams to swiftly implement, utilize, and expand capabilities, resulting in accelerated time to value. Furthermore, DX Unified Infrastructure Management delivers actionable insights tailored for cloud platforms like AWS and Azure, along with modern architectures linked to cloud services, including Nutanix, Hadoop, MongoDB, and Apache, among others. By integrating extensive expertise across various hybrid cloud infrastructure components, it supports initiatives related to digital transformation, automation, and innovation. The system can automatically identify devices based on their attributes, setting specific policies for each type while deploying configurations and alarm protocols as necessary. This level of automation not only simplifies management but also enhances operational efficiency, allowing organizations to focus on strategic initiatives.

Introducing the ultimate self-repairing IT solution tailored for your enterprise. With its remarkable cognitive abilities, HEAL proactively averts IT system failures before they occur, allowing you to devote your attention to other vital areas of your business. In today’s fast-moving environment, merely identifying and reporting incidents post-factum is insufficient. HEAL stands out as a revolutionary IT tool that not only addresses issues but also anticipates and mitigates them through advanced AI algorithms and machine learning techniques, ensuring seamless operations for enterprises. Utilizing an innovative approach known as 'workload-behavior correlation,' HEAL thoroughly examines all elements essential for the efficient functioning of an IT system, including volume, composition, and payload. Whenever it detects any irregular behavior, it promptly initiates either a healing response or a scaling action based on the underlying cause, making it an indispensable asset for modern businesses striving for reliability and efficiency. This proactive strategy empowers organizations to maintain optimal performance and reduce downtime significantly.

OK, it’s not that big a secret. Companies that get ahead will remain focused on Continuous Experience Optimization. More than just a process, Continuous Experience Optimization becomes a deeply ingrained culture. A driving force built around constantly optimizing the digital experience. While other platforms alert you to user friction, only Blue Triangle helps you prioritize solutions with website monitoring software rooted in business outcomes.

Enhance both your production and staging environments by integrating logs, metrics, and traces in real-time and on-demand directly from your IDE or command line interface. With Lightrun, you can significantly improve productivity and achieve complete code-level visibility. You can add logs and metrics instantly while services are operational, making it easier to debug complex architectures like monoliths, microservices, Kubernetes, Docker Swarm, ECS, and serverless applications. Quickly insert any missing log lines, instrument necessary metrics, or establish snapshots as needed without the hassle of recreating the production setup or redeploying. When you invoke instrumentation, the resulting data gets sent to your log analysis platform, IDE, or preferred APM tool. This allows for thorough analysis of code behavior to identify bottlenecks and errors without interrupting the running application. You can seamlessly incorporate extensive logs, snapshots, counters, timers, function durations, and much more without risking system stability. This streamlined approach lets you focus on coding rather than getting bogged down in debugging, eliminating the need for constant restarts or redeployments when troubleshooting. Ultimately, this results in a more efficient development workflow, allowing you to maintain momentum on your projects.

Leverage the most extensively utilized observability platform, founded on the reliable Elastic Stack (commonly referred to as the ELK Stack), to integrate disparate data sources, providing cohesive visibility and actionable insights. To truly monitor and extract insights from your distributed systems, it is essential to consolidate all your observability data within a single framework. Eliminate data silos by merging application, infrastructure, and user information into a holistic solution that facilitates comprehensive observability and alerting. By integrating limitless telemetry data collection with search-driven problem-solving capabilities, you can achieve superior operational and business outcomes. Unify your data silos by assimilating all telemetry data, including metrics, logs, and traces, from any source into a platform that is open, extensible, and scalable. Enhance the speed of problem resolution through automatic anomaly detection that leverages machine learning and sophisticated data analytics, ensuring you stay ahead in today's fast-paced environment. This integrated approach not only streamlines processes but also empowers teams to make informed decisions swiftly.

AI-powered cloud observation platform. Middleware platform helps you identify, understand and resolve issues across your cloud infrastructure. AI will detect and diagnose all issues infra, application and infrastructure and provide better recommendations for fixing them. Dashboard allows you to monitor metrics, logs and traces in real time. The best and fastest results with the least amount of resources. Bring all metrics, logs and traces together into a single timeline. A full-stack platform for observability will give you complete visibility into your cloud. Our AI-based algorithms analyze your data and make suggestions for what you should fix. Your data is yours. Control your data collection, and store it in your cloud to save up to 10x the cost. Connect the dots to determine where the problem began and where it ended. Fix problems before users report them. The users get a comprehensive solution for cloud observability at a single location. It's also too cost-effective.

Kensu provides real-time monitoring of the complete data usage quality, empowering your team to proactively avert data-related issues. Grasping the significance of data application is more crucial than merely focusing on the data itself. With a unified and comprehensive perspective, you can evaluate data quality and lineage effectively. Obtain immediate insights regarding data utilization across various systems, projects, and applications. Instead of getting lost in the growing number of repositories, concentrate on overseeing the data flow. Facilitate the sharing of lineages, schemas, and quality details with catalogs, glossaries, and incident management frameworks. Instantly identify the underlying causes of intricate data problems to stop any potential "datastrophes" from spreading. Set up alerts for specific data events along with their context to stay informed. Gain clarity on how data has been gathered, replicated, and altered by different applications. Identify anomalies by analyzing historical data patterns. Utilize lineage and past data insights to trace back to the original cause, ensuring a comprehensive understanding of your data landscape. This proactive approach not only preserves data integrity but also enhances overall operational efficiency.

Before transitioning to the public cloud, it's essential to utilize an AI-driven observability platform that enables you to manage costs, enhance performance, monitor your systems, and ensure uptime across various environments, including data centers and both private and public clouds. Enterprises often grapple with the critical question of which workloads to migrate and how to mitigate unforeseen expenses and performance drops after moving to the cloud. The Virtana unified observability platform offers a solution by facilitating migration and optimization across hybrid, public, and private cloud landscapes. This comprehensive platform gathers precise data and leverages AIOps technologies—such as machine learning and sophisticated data analytics—to deliver intelligent insights on individual workloads, empowering organizations to make informed decisions regarding their migration strategy. By harnessing this platform, businesses can effectively navigate the complexities of cloud migration while adhering to performance standards and optimizing their overall infrastructure.

Identify and resolve performance issues and errors within your microservices architecture. Establish connections between root causes by analyzing traces, logs, and metrics. Reduce your costs associated with OpenTelemetry traces through Aspecto's integrated remote sampling feature. The way OTel data is visualized plays a crucial role in enhancing your troubleshooting efficiency. Transition seamlessly from a broad overview to intricate details using top-tier visualization tools. Link logs directly to their corresponding traces effortlessly, maintaining context to expedite issue resolution. Utilize filters, free-text searches, and grouping options to navigate your trace data swiftly and accurately locate the source of the problem. Optimize expenses by sampling only essential data, allowing for trace sampling based on programming languages, libraries, specific routes, and error occurrences. Implement data privacy measures to obscure sensitive information within traces, specific routes, or other critical areas. Moreover, integrate your everyday tools with your operational workflow, including logs, error monitoring, and external event APIs, to create a cohesive and efficient system for managing and troubleshooting issues. This holistic approach not only improves visibility but also empowers teams to tackle problems proactively.

When organizations choose to invest in a comprehensive observability platform that integrates data, insights, and actions throughout their IT landscape, they are able to address issues more swiftly while also removing data silos, reducing the need for resource-intensive war rooms, and alleviating alert fatigue. The Riverbed IQ unified observability solution empowers both business and IT to make quick and effective decisions by encapsulating expert troubleshooting knowledge, enabling less experienced staff to deliver more first-level resolutions, which in turn fosters digital innovation and enhances the overall digital experience for both customers and employees. By utilizing broad-based telemetry, organizations can attain a cohesive view of performance and insights, establishing a solid foundation of unified observability that supports the delivery of all other capabilities. Riverbed IQ’s methodology towards unified observability initiates with our full-fidelity telemetry, which spans across network and infrastructure components and incorporates metrics related to the end-user experience, ensuring a comprehensive understanding of system performance. This holistic approach not only streamlines troubleshooting but also positions organizations to respond adeptly to evolving digital demands.

ElastiFlow stands out as a comprehensive solution for network observability tailored for contemporary data platforms, delivering exceptional insights across various scales. This powerful tool enables organizations to attain remarkable levels of network performance, reliability, and security. ElastiFlow offers detailed analytics on network traffic flows, capturing critical data such as source and destination IP addresses, ports, protocols, and the volume of transmitted data. Such detailed information equips network administrators with the ability to thoroughly assess network performance and swiftly identify potential problems. The tool proves invaluable for diagnosing and resolving network challenges, including congestion, elevated latency, or packet loss. By scrutinizing network traffic patterns, administrators can accurately determine the root cause of issues and implement effective solutions. Utilizing ElastiFlow not only enhances an organization's security posture but also facilitates prompt detection and response to threats, ensuring adherence to regulatory standards. Consequently, organizations can achieve a more robust and responsive network environment, ultimately leading to improved operational efficiency and user satisfaction.

Enhance your observability framework to swiftly identify data and machine learning challenges, facilitate ongoing enhancements, and prevent expensive incidents. Begin with dependable data by consistently monitoring data-in-motion to catch any quality concerns. Accurately detect shifts in data and models while recognizing discrepancies between training and serving datasets, allowing for timely retraining. Continuously track essential performance metrics to uncover any decline in model accuracy. It's crucial to identify and mitigate risky behaviors in generative AI applications to prevent data leaks and protect these systems from malicious attacks. Foster improvements in AI applications through user feedback, diligent monitoring, and collaboration across teams. With purpose-built agents, you can integrate in just minutes, allowing for the analysis of raw data without the need for movement or duplication, thereby ensuring both privacy and security. Onboard the WhyLabs SaaS Platform for a variety of use cases, utilizing a proprietary privacy-preserving integration that is security-approved for both healthcare and banking sectors, making it a versatile solution for sensitive environments. Additionally, this approach not only streamlines workflows but also enhances overall 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.

LOGIQ.AI's LogFlow offers a unified management system for your observability data pipelines. As data streams are received, they are efficiently categorized and optimized to serve the needs of your business teams and knowledge workers. XOps teams can streamline their data flow management, enhancing data EPS control while also improving the quality and relevance of the data. LogFlow’s InstaStore, built on any object storage solution, provides limitless data retention and allows for on-demand data playback to any observability platform you prefer. This enables the analysis of operational metrics across various applications and infrastructure, yielding actionable insights that empower you to scale confidently while ensuring consistent high availability. By collecting, transforming, and analyzing behavioral data and usage trends from business systems, you can enhance business decisions and improve user experiences. Furthermore, in an ever-evolving threat landscape, it's essential to stay ahead; with LogFlow, you can identify and analyze threat patterns coming from diverse sources, automating both threat prevention and remediation processes effectively. This proactive approach not only strengthens security but also fosters a resilient operational environment.

Bigeye is a platform designed for data observability that empowers teams to effectively assess, enhance, and convey the quality of data at any scale. When data quality problems lead to outages, it can erode business confidence in the data. Bigeye aids in restoring that trust, beginning with comprehensive monitoring. It identifies missing or faulty reporting data before it reaches executives in their dashboards, preventing potential misinformed decisions. Additionally, it alerts users about issues with training data prior to model retraining, helping to mitigate the anxiety that stems from the uncertainty of data accuracy. The statuses of pipeline jobs often fail to provide a complete picture, highlighting the necessity of actively monitoring the data itself to ensure its suitability for use. By keeping track of dataset-level freshness, organizations can confirm pipelines are functioning correctly, even in the event of ETL orchestrator failures. Furthermore, the platform allows you to stay informed about modifications in event names, region codes, product types, and other categorical data, while also detecting any significant fluctuations in row counts, nulls, and blank values to make sure that the data is being populated as expected. Overall, Bigeye turns data quality management into a proactive process, ensuring reliability and trustworthiness in data handling.

You can innovate faster in the cloud, improve user experience and future-proof applications. Splunk is designed for cloud-native enterprises and helps you solve current problems. Splunk helps you detect any problem before it becomes a customer problem. Our AI-driven Directed Problemshooting reduces MTTR. Flexible, open-source instrumentation eliminates lock-in. Optimize performance by seeing all of your application and using AI-driven analytics. You must observe everything in order to deliver an excellent end-user experience. NoSample™, full-fidelity trace ingestion allows you to leverage all your trace data and identify any anomalies. Directed Troubleshooting reduces MTTR to quickly identify service dependencies, correlations with the underlying infrastructure, and root-cause errors mapping. You can break down and examine any transaction by any dimension or metric. You can quickly and easily see how your application behaves in different regions, hosts or versions.

Arize's machine-learning observability platform automatically detects and diagnoses problems and improves models. Machine learning systems are essential for businesses and customers, but often fail to perform in real life. Arize is an end to-end platform for observing and solving issues in your AI models. Seamlessly enable observation for any model, on any platform, in any environment. SDKs that are lightweight for sending production, validation, or training data. You can link real-time ground truth with predictions, or delay. You can gain confidence in your models' performance once they are deployed. Identify and prevent any performance or prediction drift issues, as well as quality issues, before they become serious. Even the most complex models can be reduced in time to resolution (MTTR). Flexible, easy-to use tools for root cause analysis are available.

Bindplane is an advanced telemetry pipeline solution based on OpenTelemetry, designed to streamline observability by centralizing the collection, processing, and routing of critical data. It supports a variety of environments such as Linux, Windows, and Kubernetes, making it easier for DevOps teams to manage telemetry at scale. Bindplane reduces log volume by 40%, enhancing cost efficiency and improving data quality. It also offers intelligent processing capabilities, data encryption, and compliance features, ensuring secure and efficient data management. With a no-code interface, the platform provides quick onboarding and intuitive controls for teams to leverage advanced observability tools.

Enhance your metrics and alerting capabilities using a top-tier open-source monitoring tool. Prometheus inherently organizes all data as time series, which consist of sequences of timestamped values associated with the same metric and a specific set of labeled dimensions. In addition to the stored time series, Prometheus has the capability to create temporary derived time series based on query outcomes. The tool features a powerful query language known as PromQL (Prometheus Query Language), allowing users to select and aggregate time series data in real time. The output from an expression can be displayed as a graph, viewed in tabular format through Prometheus’s expression browser, or accessed by external systems through the HTTP API. Configuration of Prometheus is achieved through a combination of command-line flags and a configuration file, where the flags are used to set immutable system parameters like storage locations and retention limits for both disk and memory. This dual method of configuration ensures a flexible and tailored monitoring setup that can adapt to various user needs. For those interested in exploring this robust tool, further details can be found at: https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fsourceforge.net%2Fprojects%2Fprometheus.mirror%2F

CtrlStack oversees a diverse array of operational functions and change sources to mitigate risks, assess the impact of changes, and swiftly identify the root causes of production problems. In observability, relationship mapping involves uncovering significant connections and interactions among various data types—such as metrics, events, logs, and traces. We employ a native graph database to efficiently encapsulate this “data between the data” at both speed and scale. Achieve comprehensive visibility of all changes related to commits, configuration files, and feature flags with a single click. Gather all pertinent information regarding an incident at the precise moment it arises, as well as throughout the process of diagnosis and resolution, to prevent the overwriting of one another's changes. Gain valuable insights into what alterations were made, when they occurred, who initiated them, and the subsequent effects on operations. Foster collaboration among teams by leveraging shared data knowledge through a DevOps graph, enhancing overall operational efficiency and communication. This approach not only improves incident response times but also strengthens the team's ability to work together effectively.

Gain a comprehensive understanding of your application's performance in a live environment by consistently utilizing continuous profiling techniques. By maintaining a low overhead for data collection, you ensure that you will always have access to crucial profiling information whenever needed. Many companies find that a significant portion of their resources, often around 20-30%, is squandered on poorly optimized code paths. The Parca Agent simplifies the profiling process by eliminating the need for instrumentation across your entire infrastructure; just deploy it and you're ready to go! Over time, the profiling data gathered by Parca allows for confident identification of hot paths that require optimization, while also enabling comparisons between different queries, such as software versions or other relevant factors. This valuable profiling data not only sheds light on the specific code executed by a process over time but also makes it easier to troubleshoot challenging issues, such as memory leaks or sudden spikes in CPU and I/O that lead to unexpected behaviors. With these insights, teams can effectively allocate resources and prioritize their optimization efforts for maximum impact.

Helios equips security teams with contextual and actionable insights during runtime, greatly alleviating alert fatigue by offering immediate visibility into application behavior. Our platform delivers detailed insights into the vulnerable software components currently in use and the data flows associated with them, providing a comprehensive evaluation of your risk profile. By focusing on your application's specific context, teams can effectively prioritize fixes, ensuring that valuable development time is used efficiently to address the most critical attack surfaces. With a clear understanding of the applicative context, security teams can accurately assess which vulnerabilities truly necessitate remediation. This clarity eliminates the need for persuading the development team about the legitimacy of a vulnerability, streamlining the response process and enhancing overall security. Moreover, this approach fosters collaboration between security and development teams, ultimately leading to a more robust security posture.

Our ready-to-use solution empowers you to keep an eye on your cluster's health and resolve problems more swiftly with intuitive dashboards that function seamlessly. Coupled with transparent and budget-friendly pricing, initiating your journey is a breeze. ContainIQ operates three agents within your cluster: one single replica deployment that gathers metrics and events from the Kubernetes API, along with two daemon sets—one dedicated to capturing latency data for every pod on the node and the other focused on logging for all pods and containers. You can monitor latency metrics by microservice and path, including p95, p99, average response times, and requests per second (RPS). The system works immediately without the need for additional application packages or middleware. Set alerts to notify you of significant changes and utilize search functionality to filter by date ranges while observing data trends over time. You can see all incoming and outgoing requests along with their associated metadata. Additionally, visualize P99, P95, average latency, and error rates over time for each specific URL path, and correlate logs for a particular trace, which is invaluable for troubleshooting when issues occur. This comprehensive approach ensures you have all the tools needed to maintain optimal performance and swiftly diagnose any challenges that arise.

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.