Best IoT Analytics Software

What is IoT Analytics Software?

IoT analytics software enables organizations to view, monitor, and analyze data collected from IoT devices and machines. IoT analytics software allow companies to understand what's happening with their IoT infrastructure by collecting IoT sensor data. Compare and read user reviews of the best IoT Analytics software currently available using the table below. This list is updated regularly.

  • 1
    Qrvey

    Qrvey

    Qrvey

    Qrvey is the only solution for embedded analytics with a built-in data lake. Qrvey saves engineering teams time and money with a turnkey solution connecting your data warehouse to your SaaS application. Qrvey’s full-stack solution includes the necessary components so that your engineering team can build less. Qrvey’s multi-tenant data lake includes: - Elasticsearch as the analytics engine - A unified data pipeline for ingestion and transformation - A complete semantic layer for simple user and data security integration Qrvey’s embedded visualizations support everything from: - standard dashboards and templates - self-service reporting - user-level personalization - individual dataset creation - data-driven workflow automation Qrvey delivers this as a self-hosted package for cloud environments. This offers the best security as your data never leaves your environment while offering a better analytics experience to users. Less time and money on analytics
  • 2
    Memfault

    Memfault

    Memfault

    Reduce risk, ship products faster, and resolve issues proactively by upgrading your Android and MCU-based devices with Memfault. By integrating Memfault into smart device infrastructure, developers and IoT device manufacturers can monitor and manage the entire device lifecycle, from development to feature updates, with ease and speed. Monitor hardware and firmware performance, remotely investigate issues, and incrementally rollout targeted updates to devices without disrupting customers. Go beyond application monitoring with device and fleet-level metrics, like battery health and connectivity with crash analytics for firmware. Resolve issues more efficiently with automatic detection, alerts, deduplication, and actionable insights sent via the cloud. Keep customers happy by fixing bugs quickly and shipping features more frequently with staged rollouts and specific device groups (cohorts).
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