Compare Litmus Edge vs. The Hark Platform

The HiveMQ Platform provides a scalable, reliable data backbone with an event-driven MQTT architecture. Here are a few highlights: 1. MQTT Broker: At the heart of the HiveMQ platform is a fully MQTT-compliant broker purpose-built for fast, reliable, bi-directional data movement between IoT devices and enterprise systems. 2. Edge Data Integration: HiveMQ Edge seamlessly integrates edge data by converting industrial protocols into standardized MQTT, enabling an interoperable IIoT infrastructure. 3. IoT Streaming Governance: Data Hub transforms data in flight, passing only the most relevant, contextualized data to cloud and enterprise systems. 4. UNS & IT/OT convergence Enabler: Commonly used as the backbone for Unified Namespace architectures and seamlessly connects OT devices with IT systems for full visibility and interoperability. 5. Distributed Data Intelligence: HiveMQ Pulse unifies and contextualizes data across the enterprise for smarter decisions exactly where they matter most. 6. Maximum Interoperability: Runs anywhere on-premises or in public or private clouds. Efficiently connects to streaming applications, databases and data lakes with a Java SDK to build your own 7. Scalability to Support Growth: Elastic scaling with automatic data balancing and smart message distribution. Proven benchmark of up to 200M active clients with 1.8B messages/hour 8. Business Critical Reliability: Zero message loss with persistence to disk and offline queuing. No single point of failure due to masterless cluster architecture and zero downtime upgrades

Epicor Connected Process Control (CPC) is a flexible no-code/low-code MES and manufacturing operations platform that helps manufacturers standardize production and assembly processes, improve quality, and increase visibility across the shop floor. Using digital work instructions with multimedia content, manufacturers can guide operators through critical tasks, enforce process control, and improve consistency across products, workstations, and production lines. CPC connects equipment and devices to collect production and quality data in real time, helping manufacturers identify issues faster, reduce waste, and support continuous improvement. CPC provides product traceability with a complete historical record of each product's build and inspection history. Manufacturers use CPC to support assembly verification, error proofing, quality inspections, operator guidance, and other connected manufacturing initiatives. For manufacturers with complex product variations, CPC can dynamically present the correct work instructions based on the product or configuration being built, helping ensure products are assembled accurately and consistently. Available on-premises or in the cloud, CPC scales from a single work cell to enterprise-wide deployments.

Tractian is the Industrial Copilot for maintenance and reliability, combining hardware and software solutions to monitor asset performance, manage industrial operations, and implement predictive maintenance strategies. Its AI-driven platform empowers businesses to prevent unplanned equipment downtime and boost production output. The company is headquartered in Atlanta, GA, and extends its presence globally with offices in Mexico City and Sao Paulo. Learn more at tractian.com.

Edgefinity IoT is a powerful RFID and IoT platform that gives organizations real-time visibility into inventory, assets, equipment, work-in-process, and personnel. By collecting data from RFID readers, sensors, and connected devices, the platform provides a clear view of what is happening across facilities and operations. Edgefinity IoT helps businesses automate tracking, locate critical assets, monitor movement, verify inventory and shipments, improve production visibility, and support safety applications such as personnel tracking and emergency mustering. Configurable rules and alerts can automatically trigger actions when predefined conditions are met, helping teams respond faster and reduce manual oversight. Built to work with a wide range of RFID hardware and IoT technologies, Edgefinity IoT can be deployed in the cloud or on-premise and scaled across applications, facilities, and enterprise operations.

Evocon is a cloud-based production monitoring solution that helps manufacturing companies manage and improve their production efficiency. Evocon automates and digitizes the collection of data from production machines. We then transfer the collected data to the cloud so that manufacturers can access it in real-time, either on-site or remotely. Finally, by visualizing the data in an intuitive way, we make it actionable. This enables manufacturers to track their downtime, reduce costs and optimize the use of their production equipment. It also gives them a platform for smart decision-making – where to invest and what areas need the most attention. By providing transparency, we help companies find a way forward based on what their data says. More than 10 years of experience in production monitoring Used in 50+ countries Covering 14 different industries Available in 18 languages

The Asset Guardian (TAG) Mobi: Tackle Downtime with TAG Mobi TAG Mobi is a fully embedded preventive maintenance and asset management (EAM) solution within Microsoft Dynamics 365 Business Central. Designed for modern manufacturing and infrastructure operations, TAG Mobi helps reduce risk, minimize downtime, and streamline maintenance workflows—all from within your existing Business Central environment. From proactive asset health monitoring and predictive maintenance to real-time mobility and AI-powered adoption tools, TAG Mobi equips maintenance teams with everything they need to boost performance and take control of asset operations. Key Features: • Fully embedded in Microsoft Dynamics 365 Business Central • Real-time mobile access for on-the-go asset tracking • Predictive maintenance to reduce unplanned downtime • AI-assisted onboarding for faster adoption • Advanced APM tools to monitor asset health and anticipate failures No silos. No extra software. Just a seamless, native experience that empowers maintenance teams and provides managers with the insights they need—right inside Business Central.

RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times. RaimaDB is a high-performance, cross-platform embedded database optimized for mission-critical applications in industries such as IoT and edge computing. Its lightweight design makes it ideal for resource-constrained environments, supporting both in-memory and persistent storage options. RaimaDB offers flexible data modeling, including traditional relational models and direct relationships through network model sets. With ACID-compliant transactions and advanced indexing methods like B+Tree, Hash Table, R-Tree, and AVL-Tree, it ensures data reliability and efficiency. Built for real-time processing, it incorporates multi-version concurrency control (MVCC) and snapshot isolation, making it a robust solution for applications demanding speed and reliability.

High-Performance Data Engineering. Total Sovereignty. TIMi delivers the power of a complete cloud data stack—on-premises, fully sovereign, and ridiculously fast. We reject artificial vendor lock-in and hidden costs. Instead, we offer absolute peace of mind through engineering excellence, giving your team the freedom to experiment, innovate, and solve complex AI, analytics, and automation challenges in record time. Why Top Enterprises Choose TIMi? Enterprise Integration & *No-Code* ETL/Data preparation: Automate complex workflows and seamlessly link your entire stack: SAP, Salesforce, SharePoint, S3, Azure Storage, PowerBI, Tableau, etc. Unmatched Infrastructure Efficiency: Our competitors such as Databricks, Dataiku, and MS Fabric all rely on Spark—and that makes them inherently inefficient since a single €2k TIMi server outperforms a 267-node Spark cluster. TIMi process billions of rows in seconds and manage petabyte-scale data lakes at a fraction of the cost. Proven AI Leadership: Harness pioneering machine learning from the creators of the first Auto-ML engine (est. 2007). Whether deployed on-premises or via our EU-Hosted Sovereign Cloud, TIMi empowers leaders in Banking, Telecoms, Manufacturing, Retail, Defense and Government.

AnalyticsCreator is a metadata-driven design application for data warehouse automation and data product engineering across the Microsoft data stack. Its Governed Control Model connects business meaning, data structures, transformation rules, dependencies, lineage and technical implementation in one controlled project model. Data teams design the required architecture in AnalyticsCreator, then generate native Microsoft assets from that design. Generated outputs can include SQL Server objects, SSIS packages, Azure Data Factory pipelines, supported Microsoft Fabric components, deployment artefacts and Power BI semantic models. AnalyticsCreator supports dimensional, 3NF and hybrid modelling approaches together with ingestion, transformations, delta loading, historisation, Slowly Changing Dimensions, snapshots and repeatable data-processing patterns. Because generated outputs are native Microsoft technology, no AnalyticsCreator runtime is required in production. Organisations retain ownership of the resulting implementation and can integrate generated assets into Git, Azure DevOps and CI/CD workflows. Lineage, documentation and dependency information remain connected to the design, helping teams understand change impact before regenerating affected assets. Design Intelligence extends this governed project context into AI-assisted data engineering by providing authorised AI tools and agents with structured access to metadata, lineage, dependencies and design rules. Typical use cases include enterprise data warehouse development, Microsoft Fabric adoption, SQL Server and SSIS modernisation, governed Power BI delivery and repeatable data product engineering.

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