MetricSign Alternatives

SCIKIQ Data Fabric makes enterprise data AI-ready, without rebuilding the data stack, In weeks and months or years SCIKIQ is an AI-native Data & Intelligence Platform that helps enterprises connect, contextualize, govern and activate their data for analytics, Generative AI and intelligent agents. Instead of adding another disconnected tool, SCIKIQ creates a unified intelligence layer across the technology you already use from SAP, Oracle and Salesforce to Snowflake, Databricks, cloud platforms, data lakes and enterprise applications. The result is trusted, contextualized and AI-ready enterprise data — in weeks, not years. What makes SCIKIQ Data Fbric different is its ability to bring the entire data-to-AI journey into one platform. Data integration, transformation, data quality, governance, catalog, lineage, semantic models, knowledge graphs, conversational analytics, AI/ML, data products and AI agents work together rather than as separate tools. At the heart of SCIKIQ is Contextual Intelligence. SCIKIQ connects technical metadata with business definitions, KPIs, ownership, relationships and rules so that people and AI understand what enterprise data actually means. This semantic foundation helps create more trusted analytics and better-grounded AI responses. Business users can simply ask questions of their enterprise data in natural language, explore KPIs and root causes, and receive contextual answers without depending on SQL or waiting for another report. For data and technology teams, SCIKIQ provides a governed foundation with 200+ connectors, active metadata, multi-hop lineage, data quality, role-based governance and multi-cloud support across AWS, Azure, GCP, hybrid and on-prem environments, and you don't have to rip and replace your existing investments. SCIKIQ works with your stack, not against it. SCIKIQ is already trusted in production by leading enterprises across the USA, India and UAE, including organizations such as American Express, London Stock Exchange Group, Landmark Group and EFS. Its solutions have also been delivered alongside global technology and consulting ecosystems including AWS, Microsoft Azure, Deloitte, EY, Infosys and Tech Mahindra. SCIKIQ has been recognized by Forrester, NASSCOM, YourStory, Inc42 and DataIQ, providing independent validation of its innovation in enterprise data and AI. If your enterprise already has data but is struggling to turn it into trusted AI, SCIKIQ is where that journey begins

Code-Cube.io is the full-stack data collection observability platform that protects your dataLayer, tags and conversion data. It detects tracking issues instantly and provides real-time alerts to prevent data loss and performance drops. The platform eliminates the need for manual QA by continuously auditing tracking implementations across websites and applications. Users gain full visibility into how tags and events behave across both client-side and server-side environments. Code-Cube.io ensures that marketing data remains accurate, enabling better decision-making, preventing wasted ad spend and maximizing campaign performance.

Edge Delta is a new way to do observability that helps developers and operations teams monitor datasets and create telemetry pipelines. We process your log data as it's created and give you the freedom to route it anywhere. 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. 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.

See the impact of data issues without running SQL. We analyze your logs and metadata to identify freshness and volume anomalies, schema changes in tables, pipeline errors, and their blast radius effects on your business. Masthead observes every table, process, script, and dashboard in the data warehouse and connected BI tools for anomalies, alerting data teams in real time if any data failures occur. Masthead shows the origin and implications of data anomalies and pipeline errors on data consumers. Masthead maps data issues on lineage, so you can troubleshoot within minutes, not hours. We get a comprehensive view of all processes in GCP without giving access to our data was a game-changer for us. It saved us both time and money. Gain visibility into the cost of each pipeline running in your cloud, regardless of ETL. Masthead also has AI-powered recommendations to help you optimize your models and queries. It takes 15 min to connect Masthead to all assets in your data warehouse.

Orchestra is a Unified Control Plane for Data and AI Operations, designed to help data teams build, deploy, and monitor workflows with ease. It offers a declarative framework that combines code and GUI, allowing users to implement workflows 10x faster and reduce maintenance time by 50%. With real-time metadata aggregation, Orchestra provides full-stack data observability, enabling proactive alerting and rapid recovery from pipeline failures. It integrates seamlessly with tools like dbt Core, dbt Cloud, Coalesce, Airbyte, Fivetran, Snowflake, BigQuery, Databricks, and more, ensuring compatibility with existing data stacks. Orchestra's modular architecture supports AWS, Azure, and GCP, making it a versatile solution for enterprises and scale-ups aiming to streamline their data operations and build trust in their AI initiatives.

Bigeye is the data observability platform that helps teams measure, improve, and communicate data quality clearly at any scale. Every time a data quality issue causes an outage, the business loses trust in the data. Bigeye helps rebuild trust, starting with monitoring. Find missing and busted reporting data before executives see it in a dashboard. Get warned about issues in training data before models get retrained on it. Fix that uncomfortable feeling that most of the data is mostly right, most of the time. Pipeline job statuses don't tell the whole story. The best way to ensure data is fit for use, is to monitor the actual data. Tracking dataset-level freshness ensures pipelines are running on schedule, even when ETL orchestrators go down. Find out about changes to event names, region codes, product types, and other categorical data. Detect drops or spikes in row counts, nulls, and blank values to ensure everything is populating as expected.

Prevent data outages by identifying and fixing data quality issues before they get into production. Go from 0 to 100% test coverage of your data pipelines in a day. Know the impact of each code change with automatic regression testing across billions of rows. Automate change management, improve data literacy, achieve compliance, and reduce incident response time. Don’t let data incidents take you by surprise. Be the first one to know with automated anomaly detection. Datafold’s easily adjustable ML model adapts to seasonality and trend patterns in your data to construct dynamic thresholds. Save hours spent on trying to understand data. Use the Data Catalog to find relevant datasets, fields, and explore distributions easily with an intuitive UI. Get interactive full-text search, data profiling, and consolidation of metadata in one place.

Kensu monitors the end-to-end quality of data usage in real time so your team can easily prevent data incidents. It is more important to understand what you do with your data than the data itself. Analyze data quality and lineage through a single comprehensive view. Get real-time insights about data usage across all your systems, projects, and applications. Monitor data flow instead of the ever-increasing number of repositories. Share lineages, schemas and quality info with catalogs, glossaries, and incident management systems. At a glance, find the root causes of complex data issues to prevent any "datastrophes" from propagating. Generate notifications about specific data events and their context. Understand how data has been collected, copied and modified by any application. Detect anomalies based on historical data information. Leverage lineage and historical data information to find the initial cause.

Monitor and control everything your data pipelines do with zero code changes. Monitor data and pipelines in motion to proactively prevent downtime and quickly root cause issues. Optimize pipeline runs and job performance to save costs and keep SLAs. Accelerate code deployments and platform upgrades while maintaining reliability and performance. Data & performance checks in line with pipeline runs. Checks on input data, before pipelines even run. Automatic preemption of runs. definity takes away the effort to build deep end-to-end coverage, so you are protected at every step, across every dimension. definity shifts observability to post-production to achieve ubiquity, increase coverage, and reduce manual effort. definity agents automatically run with every pipeline, with zero footprints. Unified view of data, pipelines, infra, lineage, and code for every data asset. Detect in run-time and avoid async checks. Auto-preempt runs, even on inputs.

Actian Data Observability is an AI-powered platform designed to continuously monitor, validate, and manage the health, quality, and reliability of data across modern data environments. It uses automated Data Observability Agents that validate data as it arrives in data lakehouses or warehouses, detecting anomalies, explaining root causes, and coordinating resolution before issues impact dashboards, reports, or AI systems. It provides real-time visibility into data pipelines, ensuring that data remains accurate, complete, and trustworthy throughout its lifecycle. It eliminates blind spots by monitoring 100% of data rather than relying on sampling, allowing organizations to identify hidden errors that could otherwise corrupt analytics or machine learning outcomes. With built-in anomaly detection powered by AI and machine learning, it proactively identifies irregularities such as schema changes, missing data, or unexpected distributions, enabling faster diagnosis and resolution.

Organizations continuously strive to be more data-driven, building dashboards, analytics, and data pipelines across the modern data stack. Unfortunately, most organizations struggle with data reliability issues leading to poor business decisions and lack of trust in data as an organization, directly impacting their bottom line. Resolving complex data issues is a manual and time-consuming process involving multiple teams all relying on tribal knowledge to manually reverse engineer complex data pipelines across different platforms to identify root-cause and understand the impact. Pantomath is a data pipeline observability and traceability platform for automating data operations. It continuously monitors datasets and jobs across the enterprise data ecosystem providing context to complex data pipelines by creating automated cross-platform technical pipeline lineage.

VirtualMetric is a powerful telemetry pipeline solution designed to enhance data collection, processing, and security monitoring across enterprise environments. Its core offering, DataStream, automatically collects and transforms security logs from a wide range of systems such as Windows, Linux, MacOS, and Unix, enriching data for further analysis. By reducing data volume and filtering out non-meaningful logs, VirtualMetric helps businesses lower SIEM ingestion costs, increase operational efficiency, and improve threat detection accuracy. The platform’s scalable architecture, with features like zero data loss and long-term compliance storage, ensures that businesses can maintain high security standards while optimizing performance.

Automatically cover thousands of tables with ML-based anomaly detection and 50+ custom metrics. Comprehensive data and metadata monitoring. Exhaustive mapping of all dependencies between assets, from ingestion to BI. Enhanced productivity and collaboration between data engineers and data consumers. Sifflet seamlessly integrates into your data sources and preferred tools and can run on AWS, Google Cloud Platform, and Microsoft Azure. Keep an eye on the health of your data and alert the team when quality criteria aren’t met. Set up in a few clicks the fundamental coverage of all your tables. Configure the frequency of runs, their criticality, and even customized notifications at the same time. Leverage ML-based rules to detect any anomaly in your data. No need for an initial configuration. A unique model for each rule learns from historical data and from user feedback. Complement the automated rules with a library of 50+ templates that can be applied to any asset.

rhealth.dev is an uptime monitoring platform designed to reduce alert noise and speed up incident response. It monitors websites, APIs, cron jobs, TCP services, and internal systems from up to 40 global locations, confirming failures across regions before alerting. This eliminates false positives caused by temporary or local network issues. Unlike traditional monitoring tools that only say “down,” rhealth.dev sends actionable alerts with exact failure reasons such as timeouts, connection errors, slow responses, or content mismatches. Key capabilities 1. Website & API uptime monitoring 2. Response time and performance tracking 3. SSL certificate expiry alerts 4. Keyword and partial-failure detection 5. Cron job & background task monitoring 6. TCP and database port checks 7. Private network monitoring using Docker-based checkers Free plan includes 1. 20 monitors 2. Unlimited team members 3. Unlimited status pages 4. No credit card required

A low-code no-code approach to data quality. SaaS for flexibility, affordability, ease of integration, and efficient support. High standards of encryption, identity management, role-based access control, data governance, and compliance standards. Advanced ML models for detecting row-value data anomalies. Models will evolve and adapt to users' business and data needs. Add any number of data sources, records, and attributes. Well-equipped for unpredictable volume spikes. Support batch and streaming processing. Data is constantly monitored to provide real-time notifications, with zero impact on pipeline performance. Seamless boarding, integration, and investigation experience. Telmai is a platform for the Data Teams to proactively detect and investigate anomalies in real time. A no-code on-boarding. Connect to your data source and specify alerting channels. Telmai will automatically learn from data and alert you when there are unexpected drifts.

Monitor your entire warehouse in 30 minutes. Identify downstream impact with automated warehouse-to-BI lineage. Trust takes seconds to lose and months to regain. Gain peace of mind with observability built for the modern data era. Code-based tests take hours to write and maintain, so it's hard to achieve the coverage you need. In Metaplane, you can add hundreds of tests within minutes. We support foundational tests (e.g. row counts, freshness, and schema drift), more complex tests (distribution drift, nullness shifts, enum changes), custom SQL, and everything in between. Manual thresholds take a long time to set and quickly go stale as your data changes. Our anomaly detection models learn from historical metadata to automatically detect outliers. Monitor what matters, all while accounting for seasonality, trends, and feedback from your team to minimize alert fatigue. Of course, you can override with manual thresholds, too.

Sift is a unified observability platform purpose-built for modern, mission-critical hardware systems that provides engineers with infrastructure and tooling to ingest, store, normalize, and explore high-frequency, high-cardinality telemetry and event data from design, validation, manufacturing, and operations in a single source of truth rather than fragmented dashboards and scripts; it centralizes diverse data types, aligns signals across subsystems, and structures information for fast search, visual review, and traceability so teams can detect anomalies, perform root-cause analysis, automate verification and validation, and debug hardware with real-time precision. It supports automated data review, no-code visualization and querying of massive datasets, continuous anomaly detection, and integration with engineering workflows, including CI/CD pipelines and tooling, while enabling telemetry governance, collaboration, reporting, and knowledge capture across siloed teams.

Dash0 is an OpenTelemetry-native observability platform that unifies metrics, logs, traces, and resources into one intuitive interface, enabling fast and context-rich monitoring without vendor lock-in. It centralizes Prometheus and OpenTelemetry metrics, supports powerful filtering of high-cardinality attributes, and provides heatmap drilldowns and detailed trace views to pinpoint errors and bottlenecks in real time. Users benefit from fully customizable dashboards built on Perses, with support for code-based configuration and Grafana import, plus seamless integration with predefined alerts, checks, and PromQL queries. Dash0's AI-enhanced tools, such as Log AI for automated severity inference and pattern extraction, enrich telemetry data without requiring users to even notice that AI is working behind the scenes. These AI capabilities power features like log classification, grouping, inferred severity tagging, and streamlined triage workflows through the SIFT framework.

CICube is an AI-powered platform designed to enhance your CI/CD team's efficiency by preventing failures and reducing pipeline costs through intelligent predictions. Its AI agents monitor GitHub Actions workflows, detect anomalies, and provide actionable fixes, saving hours of debugging time. A significant productivity killer in CI processes is context switching, where developers lose focus due to failed builds or CI notifications. CICube addresses this by identifying and fixing problematic builds and helping maintain developer flow. The platform offers AI-powered pipeline fixes, real-time monitoring, and actionable insights to improve CI pipeline performance and developer productivity. Features include automatic detection and resolution of CI pipeline failures, evaluation of CI lifecycle through key metrics like MTTR, success rate, throughput, and duration, and proactive monitoring of key metrics to identify and fix bottlenecks before they become critical.

Decube is a data management platform that helps organizations manage their data observability, data catalog, and data governance needs. It provides end-to-end visibility into data and ensures its accuracy, consistency, and trustworthiness. Decube's platform includes data observability, a data catalog, and data governance components that work together to provide a comprehensive solution. The data observability tools enable real-time monitoring and detection of data incidents, while the data catalog provides a centralized repository for data assets, making it easier to manage and govern data usage and access. The data governance tools provide robust access controls, audit reports, and data lineage tracking to demonstrate compliance with regulatory requirements. Decube's platform is customizable and scalable, making it easy for organizations to tailor it to meet their specific data management needs and manage data across different systems, data sources, and departments.

​Observo AI is an AI-native data pipeline platform designed to address the challenges of managing vast amounts of telemetry data in security and DevOps operations. By leveraging machine learning and agentic AI, Observo AI automates data optimization, enabling enterprises to process AI-generated data more efficiently, securely, and cost-effectively. It reduces data processing costs by over 50% and accelerates incident response times by more than 40%. Observo AI's features include intelligent data deduplication and compression, real-time anomaly detection, and dynamic data routing to appropriate storage or analysis tools. It also enriches data streams with contextual information to enhance threat detection accuracy while minimizing false positives. Observo AI offers a searchable cloud data lake for efficient data storage and retrieval.

Aggua is a data fabric augmented AI platform that enables data and business teams Access to their data, creating Trust and giving practical Data Insights, for a more holistic, data-centric decision-making. Instead of wondering what is going on underneath the hood of your organization's data stack, become immediately informed with a few clicks. Get access to data cost insights, data lineage and documentation without needing to take time out of your data engineer's workday. Instead of spending a lot of time tracing what a data type change will break in your data pipelines, tables and infrastructure, with automated lineage, your data architects and engineers can spend less time manually going through logs and DAGs and more time actually making the changes to infrastructure.

Unravel is an AI-native data observability platform designed to help modern enterprises detect, resolve, and prevent data issues at scale. It uses intelligent, automated agents that work alongside data teams to surface insights, guide decisions, and reduce operational toil. Unravel brings data observability and FinOps together, enabling organizations to improve performance, ensure reliability, and optimize cloud data spending. The platform provides end-to-end visibility across pipelines, workloads, and infrastructure. With agent-driven actionability™, Unravel can take action on behalf of teams, integrate directly with existing tools, or recommend next-best actions. It supports major data platforms including Databricks, Snowflake, and Google Cloud BigQuery. By combining automation with human control, Unravel transforms data observability into a collaborative, always-on partner.

Acceldata is an Agentic Data Management company helping enterprises manage complex data systems with AI-powered automation. Its unified platform brings together data quality, governance, lineage, and infrastructure monitoring to deliver trusted, actionable insights across the business. Acceldata’s Agentic Data Management platform uses intelligent AI agents to detect, understand, and resolve data issues in real time. Designed for modern data environments, it replaces fragmented tools with a self-learning system that ensures data is accurate, governed, and ready for AI and analytics.

Anomalo helps you get ahead of data issues by automatically detecting them as soon as they appear in your data and before anyone else is impacted. Detect, root-cause, and resolve issues quickly – allowing everyone to feel confident in the data driving your business. Connect Anomalo to your Enterprise Data Warehouse and begin monitoring the tables you care about within minutes. Our advanced machine learning will automatically learn the historical structure and patterns of your data, allowing us to alert you to many issues without the need to create rules or set thresholds.‍ You can also fine-tune and direct our monitoring in a couple of clicks via Anomalo’s No Code UI. Detecting an issue is not enough. Anomalo’s alerts offer rich visualizations and statistical summaries of what’s happening to allow you to quickly understand the magnitude and implications of the problem.‍

SYNQ is a data observability platform that helps modern data teams define, monitor, and manage their data products. It brings together ownership, testing, and incident workflows so teams can stay ahead of issues, reduce data downtime, and deliver trusted data faster. With SYNQ, every critical data product has clear ownership and real-time visibility into its health. When something breaks, the right people are alerted—with the context they need to understand and resolve the issue quickly. At the center of SYNQ is Scout, your autonomous, always-on data quality agent. Scout proactively monitors data products, recommends what and where to test, does root-cause analysis and fixes issues. It connects lineage, issue history, and contextual data to help teams fix problems faster. SYNQ integrates with the tools you already use and is trusted by leading scale-ups and enterprises such as VOI, Avios, Aiven and Ebury.

Alert Centric is a unified backup alert‑reporting solution for MSPs that consolidates alerts from any backup system into a single, centralized dashboard. It automatically monitors backups across diverse vendors and sites, detects failures, and delivers real‑time, customizable notifications to technicians, reducing human error and dramatically cutting the time spent on backup verification. It aggregates alerts via email or integration, prioritizes based on severity, correlates related events, and escalates issues as needed. Automated workflows can trigger actions like service restarts or IP blocks and generate reports and visual insights on backup status, trends, and resolution metrics. Designed for rapid deployment, Alert Centric integrates easily with PSA tools and supports all major backup vendors, offering MSPs full confidence in their clients’ backup health through proactive incident management and simplified oversight.

Matia is a unified DataOps platform designed to simplify modern data management by combining multiple core functions into a single, integrated system. It brings together ETL, reverse ETL, data observability, and a data catalog, eliminating the need for multiple disconnected tools and reducing the complexity of managing fragmented data stacks. It enables teams to move data quickly and reliably from various sources into data warehouses using advanced ingestion capabilities, including real-time updates and error handling, while also allowing them to push trusted data back into operational tools for business use. Matia emphasizes built-in observability at every stage of the data pipeline, providing monitoring, anomaly detection, and automated quality checks to ensure data accuracy and reliability before issues impact downstream systems.

StoreRadar is a business data product that packages the entire Shopify store database into a single CSV file. It covers 2.5M stores with 66+ columns per row: domain, verified email, phone number, country, category, theme, installed apps (250+ detected), full technology fingerprint, product count, first-seen and last-seen dates, and a lead score and SEO score from 0 to 100. The file works directly in Excel, Google Sheets, pandas, DuckDB, or Airtable, with no dashboard or login required. A live pipeline monitors Shopify infrastructure around the clock and refreshes the data daily. Pricing is a one-time $149 for the full export, with an optional verified-emails add-on and an annual plan for teams that need monthly refreshes.

See how your data assets are used: popularity, utilization, and schema coverage. Get important insights about your data assets such as popularity, utilization, quality, and schema coverage. Find and filter the data you need based on metadata tags and descriptions. Get important insights about your data assets such as popularity, utilization, quality, and schema coverage. Drive data governance and ownership across your organization. Stream-lake-warehouse lineage to facilitate data ownership and collaboration. Automatically generated field-level lineage map to understand the entire data ecosystem. Anomaly detection learns from your data and seasonality patterns, with automatic backfill from historical data. Machine learning-based thresholds are trained per data segment, trained on actual data instead of metadata only.

GoalfyData is an AI data platform that helps teams turn business data and agent-generated results into reusable, governed datasets and data applications. It provides AI agents with persistent business context, including field definitions, table relationships, metric logic, processing rules, permissions, and usage guidance. Instead of repeatedly uploading the same files or explaining business definitions in every AI conversation, teams can maintain a shared source of truth that different agents and collaborators can reuse. GoalfyData supports structured dataset management, AI-powered data analysis, data governance, automated reporting, business intelligence, dashboard creation, and recurring data workflows. AI agents can query maintained datasets, generate reports, and build focused data apps while preserving the underlying schema, relationships, calculation rules, and access controls. Managed Refresh can rerun configured update workflows on a schedule, helping datasets, reports.

Visual Sentinel is a six-layer website monitoring platform for public websites, web applications, APIs, and client portfolios. It combines HTTP uptime, performance and Core Web Vitals, SSL certificate and domain expiry, DNS change detection, rendered browser screenshot comparisons, and critical content assertions. Visual Sentinel is designed to catch failures that ordinary ping monitors miss, including blank renders, missing CTAs, broken layouts, defacement, injected content, and unexpected page changes that still return 200 OK. Incident records keep the response, timeline, and visual evidence together. Teams can alert through email, Slack, Discord, Telegram, WhatsApp, Microsoft Teams, PagerDuty, Opsgenie, webhooks, and browser push according to plan. Additional features include server monitoring, API monitoring, status pages, client reports, Google and Cloudflare integrations, an Android companion, REST API, and MCP tools.

Synergy is an AI-native command center for enterprise IT operations that unifies siloed monitoring, ticketing, logging, and documentation into a single pane of glass. It continuously correlates signals across tools like Splunk, New Relic, Jira, ServiceNow, and Confluence to turn alert storms into clear, prioritized insights. Synergy’s Smart Incident Workflows automate routine tasks, suggest next steps, flag ownership gaps, and accelerate resolution to cut mean time to detection and repair. Its proactive monitoring detects risks before traditional alerts trigger, flags error spikes and missed escalations, recognizes emerging patterns, and answers investigative queries in natural language. Built-in root cause analysis traces incidents end-to-end across time, logs, metrics, tickets, and post-mortems, links to similar events for instant context, and generates concise summaries.

Power BI Connector for ServiceNow is a no-code integration that exports ServiceNow data into Microsoft Power BI for reporting and analytics. Pull incidents, requests, and other ServiceNow tables into Power BI, and use built-in dashboard templates to start fast. Select exactly the data you need, schedule automatic refreshes to keep reports current, and bring ServiceNow data into the same BI layer as the rest of your enterprise sources. Installed from the ServiceNow Store, it requires no coding or middleware and respects ServiceNow roles and permissions. The Power BI Connector for ServiceNow is part of Tempo's BI connector lineup within its Strategic Portfolio Management (SPM) suite, alongside connectors for Jira and monday.com.

Discover, predict, and resolve errors in real-time. Go beyond crash reporting, error tracking, logging and error monitoring. Get instant and accurate alerts — plus a real-time feed — of all errors, including unhandled exceptions. Our automation-grade grouping uses machine learning to reduce noise and gives you error signals you can trust.

Signals AI is an AI-native engineering-intelligence platform that continuously monitors development workflows and tool usage to surface real-time insights and risk signals. It combines traditional engineering metrics (e.g., deployment frequency, build and test outcomes, project activity) with AI-powered tracking of how teams use tools, enabling automatic detection of potential bottlenecks, delays, or emerging issues before they escalate. Signals AI integrates with systems like Jira and supports project-level visibility into execution, investments, deployments, and team performance metrics. It delivers real-time alerts for build failures, deployment risks, SLA breaches, and more, right where teams collaborate (e.g., in Microsoft Teams). Weekly digests summarize key operational metrics such as cycle-time, deployment frequency, and project hotspots. Its dashboards and AI-driven insights include root-cause analysis and recommended fixes, helping engineering leaders proactively.

Axoflow, the Security Data Layer is the foundation for your SIEM and analytics tools enabling the use of AI, up to 70% faster investigations, and more than 50% reduction in SIEM spend by feeding them with actionable data. Axoflow Platform is built up of the following parts: A pipeline acting as the transportation layer for your security data and also acting as an automated ‘translator’ between data schemas. AI - If you prefer to run your detection content locally - whether it’s an AI or ML model, a threat intel lookup, or another type of enrichment - we’ve got you covered. Storage solutions to facilitate the cost-effective storage of security data and also acting as local storage to run your decentralized detection. Orchestration to weave all of the parts together in an easy-to-use GUI that lets youmonitor and manage, and control and search your data.

Unify Your Data Stack: Experience the first no-code data pipeline platform and power enlightened decision making. Integrate.io is the only complete set of data solutions & connectors for easy building and managing of clean, secure data pipelines. Increase your data team's output with all of the simple, powerful tools & connectors you’ll ever need in one no-code data integration platform. Empower any size team to consistently deliver projects on-time & under budget. We ensure your success by partnering with you to truly understand your needs & desired outcomes. Our only goal is to help you overachieve yours. Integrate.io's Platform includes: -No-Code ETL & Reverse ETL: Drag & drop no-code data pipelines with 220+ out-of-the-box data transformations -Easy ELT & CDC :The Fastest Data Replication On The Market -Automated API Generation: Build Automated, Secure APIs in Minutes - Data Warehouse Monitoring: Finally Understand Your Warehouse Spend - FREE Data Observability: Custom

Power BI Connector for monday.com is a no-code integration that exports monday.com data into Microsoft Power BI for reporting and analysis. Choose which boards, groups, columns, and item types to export, and the connector builds Power BI data-table relationships automatically – no manual modeling required. Install and configure it entirely through the monday.com interface, with no coding or external tools. Scheduled refreshes keep your Power BI reports in sync with monday.com without manual exports. Built with security in mind: requests are encrypted, signed, and authorized under monday.com permissions, and no customer data is stored. Power BI Connector for monday.com is part of Tempo's BI connector lineup within its Strategic Portfolio Management (SPM) suite, alongside connectors for Jira, ServiceNow, and more.

SMART Training Management Software System manages training requirements for multiple job classifications, automates the registration process, and provides alerts when training or certifications are past due. Without training management software to streamline the process, managing employee training for a mid-to-large sized organization with multiple work groups, each with their own specific training and licensing requirements is time-consuming and difficult. In addition, tracking and managing refresher training on an ongoing basis is a difficult challenge. Failure of the Health and Safety and Training Manager to ensure all employees assigned to jobs or projects have received current and up-to-date training can result in improperly performed work, increased risk, and the potential for OSHA fines and citations.

Cekura is an AI-powered platform designed to test, monitor, and ensure the quality of voice AI agents. It enables users to simulate thousands of real-world conversational scenarios using AI-generated and custom datasets to evaluate agent performance quickly. With parallel calling and real-time alerting, Cekura provides actionable insights and instant notifications about errors, failures, or performance drops. The platform features an intuitive dashboard that visualizes performance metrics, helping teams continuously improve their AI agents. Trusted by over 50 conversational AI companies, Cekura supports various industries including customer support, sales, recruitment, and healthcare. It is SOC2 Type 2 and HIPAA compliant, providing reliable security and privacy standards.

In the process industry where everything is connected to everything, and there are very few historical events, you never know where the next failure will show up. We monitor everything. Our software detects quality issues, failures in rotating and static equipment, deviations in operation modes, and process behavior. Don’t waste time on hundreds of false alarms. Instead of hundreds of anomalies a day, we produce up to five actionable alerts a day. Get notified of a failure a week to 24 hours in advance. No more surprises in the middle of the night. Planned, proactive maintenance saves money. Industrial assets require a different approach. We automatically combine your experts’ knowledge with machine learning to achieve a unique predictive monitoring solution. Your sensors and data already exist. It takes only two weeks for your process engineer to specify the plant’s structure and behavior, and the software is up and running!

Soda drives your data operations by identifying data issues, alerting the right people, and helping teams diagnose and resolve root causes. With automated and self-serve data monitoring capabilities, no data—or people—are ever left in the dark. Get ahead of data issues quickly by delivering full observability through easy instrumentation across your data workloads. Empower data teams to discover data issues that automation will miss. Self-service capabilities deliver the broad coverage that data monitoring needs. Alert the right people at the right time to help teams across the business diagnose, prioritize, and fix data issues. With Soda, your data never leaves your private cloud. Soda monitors data at the source and only stores metadata in your cloud.

BigQuery Connector for Jira is a no-code integration that loads Jira data into Google BigQuery for large-scale analysis and reporting. Export any Jira data – standard fields, custom fields, history, and agile metrics – plus data from across the Tempo suite (Timesheets, Capacity Planner, Financial Manager) and other Marketplace apps. Calculated fields like Time in Status and Time at Assignee come built in. Filter with basic options or JQL, schedule automatic refreshes, and keep your BigQuery datasets consistently up to date – no coding required. Granular permissions and sharing settings keep data access aligned to user roles. Built for enterprise, government, and education teams running analytics on a cloud data warehouse. BigQuery Connector for Jira is part of Tempo's Strategic Portfolio Management (SPM) suite, alongside Tempo Structure PPM, Timesheets, Capacity Planner, and Financial Manager.

HookWatch is an automated monitoring platform designed to track webhooks, cron jobs, and AI agent tool calls from a single dashboard. It provides real-time visibility into events, success rates, failures, and latency metrics to prevent silent infrastructure issues. Developers can inspect full payloads, debug delivery errors, and replay missed webhook events with one click. The platform includes cron monitoring with human-readable schedules, execution logs, and automatic retry mechanisms. Its MCP Proxy feature enables full request and response logging for AI agent tool calls without requiring code changes. HookWatch offers smart alerts through email, Slack, Discord, and PagerDuty to keep teams informed. Built for indie hackers and small teams, it delivers unified observability with a developer-friendly CLI and cloud-optional setup.

WebWatch provides vigilant, periodic checks for updates to your favorite websites right on your device. What makes it even more unique is its ability to operate seamlessly, even when the application is in the background. When a change on a monitored website is detected, you will receive a prompt notification. The brilliance of WebWatch is its ability to differentiate between trivial and significant updates. This means you can customize your preferences to receive notifications only when specific, relevant parts of the website undergo changes. WebWatch makes efficient use of iOS's background app refresh functionality, handing off the task of periodic checking to the system. Due to this intelligent integration, the frequency of refresh intervals may vary based on your device and its settings.

Adps AI is an autonomous AI-SRE platform that transforms how companies run, troubleshoot, and secure their cloud infrastructure. Instead of relying on slow, manual, human-driven incident workflows, Adps AI continuously monitors signals across logs, metrics, traces, deployments, Kubernetes, CI/CD pipelines, and cloud services—instantly detecting anomalies, diagnosing root cause, and generating precise recovery actions in seconds. By reducing MTTR by up to 99% and delivering 99.99%+ reliability, Adps AI eliminates on-call fatigue, prevents outages, and ensures uninterrupted operations across any cloud environment.

DQOps is an open-source data quality platform designed for data quality and data engineering teams that makes data quality visible to business sponsors. The platform provides an efficient user interface to quickly add data sources, configure data quality checks, and manage issues. DQOps comes with over 150 built-in data quality checks, but you can also design custom checks to detect any business-relevant data quality issues. The platform supports incremental data quality monitoring to support analyzing data quality of very big tables. Track data quality KPI scores using our built-in or custom dashboards to show progress in improving data quality to business sponsors. DQOps is DevOps-friendly, allowing you to define data quality definitions in YAML files stored in Git, run data quality checks directly from your data pipelines, or automate any action with a Python Client. DQOps works locally or as a SaaS platform.

DataTrust is built to accelerate test cycles and reduce the cost of delivery by enabling continuous integration and continuous deployment (CI/CD) of data. It’s everything you need for data observability, data validation, and data reconciliation at a massive scale, code-free, and easy to use. Perform comparisons, and validations, and do reconciliation with re-usable scenarios. Automate the testing process and get alerted when issues arise. Interactive executive reports with quality dimension insights. Personalized drill-down reports with filters. Compare row counts at the schema level for multiple tables. Perform checksum data comparisons for multiple tables. Rapid generation of business rules using ML. Flexibility to accept, modify, or discard rules as needed. Reconciling data across multiple sources. DataTrust solutions offers the full set of applications to analyze source and target datasets.

Tracebit generates and maintains tailored canary resources in your cloud environments, closing gaps in stock protection without time and cost intensive detection engineering. Tracebit generates and maintains dynamic cloud canaries. Tracebit alerts have inherent context that can be understood and actioned by the whole team. We cover a full and growing range of cloud resources, and continue to evolve and refresh your canaries instep with your environment to constantly keep your adversaries guessing. Leverage our infrastructure as code integration and automated canary recommendations to scale our cloud canaries across your estate rapidly.