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IoT Fleet Management Data Streaming

19 posts · Apr 2018 – May 2026

What this trail covers

Confluent Cloud continues to evolve its capabilities for integrating edge devices with data streaming platforms for real-time data ingestion and processing. This includes architectures for connecting IoT devices to cloud platforms like AWS IoT Core and Confluent Cloud, enabling contextualization of raw device data by combining it with business system information. The goal is to enrich data streams for improved decision-making, operational efficiency, and enhanced customer experiences. This post details the use of Confluent Cloud and Apache Flink for creating a Single Patient View (SPV) in healthcare, enabling real-time access to patient data across disparate systems for improved clinical outcomes and powering a real-time experimental treatment-matching service for cancer patients. The solution addresses challenges in interoperability, regulatory compliance (HIPAA), scalability, observability, and disaster recovery by leveraging a HIPAA-ready, fully managed, serverless data streaming platform.

All Confluent trails
  1. Agentic Fleet Management Architecture for Real-Time OperationsIntroduces agentic fleet management as a real-time, event-driven architecture where distributed AI agents continuously process streaming data for autonomous operational decisions and closed-loop execution. Details the architecture with components like edge ingestion, event streaming backbone (Kafka), stream processing (Flink), agent orchestration, AI/ML inference, command and control feedback, and monitoring. Highlights failure patterns of traditional systems and provides an end-to-end example of cold chain logistics. Outlines design principles for production-grade fleet intelligence including decoupled event streams, stateful processing, exactly-once guarantees, resilient failover, governance, and multi-region support. Contrasts real-time agentic systems with batch systems and outlines business impact and suitability criteria.
  2. Data Streaming in Healthcare: Achieving the Single Patient ViewThis post details the technical challenges and solutions for creating a Single Patient View (SPV) in healthcare using Confluent Cloud and Apache Flink. It highlights the need for interoperability with standardized formats like FHIR, regulatory compliance (HIPAA) with stringent access controls and encryption, and scalable, observable, disaster-recoverable data integration pipelines. The proposed solution involves ingesting bundled EMR files into Confluent Cloud, processing them with data from clinical trial and regulatory feeds via Apache Flink, and syncing data to MongoDB Atlas. The benefits include reduced manual intervention, elastic scaling, HIPAA readiness, and a simplified architecture with native observability and alerting.
  3. The Role of Data Streaming in Smart Cities | Confluent for IoTThis post details a specific implementation of an adaptive navigation system for emergency vehicles within a smart city context. It highlights the integration of various data sources (CCTV, incident reports, weather, vehicle position) using MQTT and Apache Flink on Confluent Cloud. The system calculates real-time congestion and optimizes routes. It emphasizes Confluent's broad integration capabilities with over 80 connectors, low-latency processing via the Kora engine for 'soft real-time' applications, and Kafka's inherent fault tolerance and scalability for handling fluctuating data volumes.
  4. The Power of MQTT and Confluent in Fleet ManagementThis post details the technical challenges of using MQTT for fleet management, specifically its limitations in stream processing and data reprocessing, leading to potential data loss or incompleteness. It proposes combining MQTT with Apache Kafka as a solution, leveraging Kafka Connect with Confluent's MQTT Source and Sink Connectors for bi-directional data flow. The architecture streams telemetry data in real-time, processes it with Confluent's stream processing capabilities, and syncs it to downstream systems like MongoDB Atlas. Key Confluent features highlighted include pre-built connectors, stream processing (ksqlDB), and Schema Registry for data consistency and validation.
  5. Achieving Same Day Delivery with Confluent Data StreamingThis post details a specific customer's migration from self-managed Kafka on Azure HDInsight to Confluent Cloud to enable same-day delivery. It highlights the challenges of managing Kafka in HDInsight, including lack of expertise, missed upgrades, frequent pipeline failures, limited connector availability, and no stream processing capabilities. The solution implemented uses Confluent Cloud's managed service, multi-AZ dedicated clusters with VNET peering, pre-built connectors (Debezium, JDBC Sink), and Schema Registry. The company also utilized the .NET client library. The outcome is improved reliability, reduced maintenance, and increased innovation capacity.
  7. Build a Real-Time IoT Application with Confluent and Apache KafkaThis post details the implementation of an end-to-end IoT motion detection and alerting system. It covers hardware selection (Raspberry Pi Zero W), software stack (Motion, cURL, librdkafka, Docker), integration with AWS S3 for video storage, and the use of Confluent Cloud with Kafka and ksqlDB for event ingestion and real-time processing. Specific technical details include using librdkafka for publishing events with a defined JSON schema, creating a ksqlDB stream for motion events, and implementing a React/Next.js UI with ksqlDB push queries (EMIT CHANGES) for real-time monitoring and pull queries for past event search, utilizing web workers for managing push query connections.
  8. Real-Time Monitoring with Apache Kafka and Kafka ConnectIntroduces a proof-of-concept for wildlife monitoring using edge devices (Raspberry Pi) with TensorFlow Lite for real-time animal detection. Implemented a Kafka producer using confluent_kafka API to publish detection events to Confluent Cloud. Utilized ksqlDB to stream process raw detection data, extracting animal counts and applying a 30-second tumbling window with MAX aggregation for smoothing observations. Leveraged Kafka Connect Elasticsearch connector to ingest processed data for visualization in Kibana and implemented an alerting mechanism using the Kafka Connect HTTP Sink Connector to trigger a Telegram bot for rare animal sightings.
  9. Apache Kafka at Home: A Houseplant Alerting System with ksqlDB | Confluent Using Data Pipelines for a Real-Time Alerting SystemThis post details a personal project building a houseplant alerting system using a Raspberry Pi, capacitive soil moisture sensors, and Apache Kafka. It covers the hardware assembly, including wiring and I2C address management for multiple sensors. The data pipeline involves producing sensor readings and plant metadata to Kafka topics, then using ksqlDB for stream processing to determine watering needs and trigger alerts. The author chose Confluent Cloud for its ease of setup and Kafka for its stream processing capabilities, contrasting it with MQTT for IoT data.
  10. From the Cellar to the Cloud – How Aedifion is Driving Next-Generation Building Automation with Confluent | Confluent Smart Buildings with Real-Time IoT Analytics Using Confluent and AedifionThis post details how Aedifion uses Confluent Cloud for building automation and IoT analytics. It covers their requirements for scalability, security, and availability, their choice of Apache Kafka and Confluent Cloud, and the setup of their data pipeline. Key technical aspects include using Confluent REST Proxy for HTTP-only environments, a strict topic naming schema for access control and tenant separation, and self-built stateless connectors for ingesting data into SQL and NoSQL databases. The post highlights the advantages of Kafka as a buffer and its replay capabilities, and mentions a minor hurdle with MQTT-native hardware while awaiting Confluent's MQTT Proxy.
  11. How to Build an Intrusion Detection System Using ksqlDBThis post demonstrates how to build an Intrusion Detection System (IDS) using ksqlDB and Kafka. It details setting up a virtualized network environment with Vagrant, capturing network packets using TShark, and ingesting them into Kafka via the Spool Dir connector. The post then shows how to use ksqlDB to process these raw network packets, flatten the JSON data, and identify potential port scan and Slowloris attacks by creating separate ksqlDB streams for each attack type. It also provides example ksqlDB queries for data analysis and detection.
  12. Real-Time Asset Tracking with Apache Kafka and Kafka StreamsThis post details a specific implementation for real-time asset tracking using Apache Kafka and Kafka Streams. It describes a data pipeline that ingests geolocation data from an MQTT server via a Java client into a Kafka topic ('vehicle-positions'). Kafka Streams is then used to reformat and enrich this data, writing it to another topic ('vehicle-positions-enriched'). Finally, an Elasticsearch Sink Connector is configured to ingest the enriched data into Elasticsearch, which is then visualized in Kibana as a real-time map dashboard. The post provides sample configurations for the producer, Kafka Streams application, and Elasticsearch connector, along with example data formats and Kibana index templates.
  13. Confluent, MQTT, and Apache Kafka Power Real-Time IoT Use CasesThis post introduces Waterstream, a Kafka-native MQTT broker that runs as a Kafka Streams application, allowing direct integration of MQTT devices with Kafka. It details how Waterstream stores MQTT state (subscriptions, QoS status, retained messages) in Kafka and enables bidirectional communication. The post also provides a comprehensive overview of various MQTT integration options with Kafka, including Kafka Connect connectors, Confluent MQTT Proxy, vendor-specific integrations, and the Kafka-native approach, discussing their respective pros, cons, and considerations for large-scale use cases.
  14. Real-Time GPS & Fleet Tracking with Confluent Cloud & MongoDBThis post details the integration of Confluent Cloud with MongoDB Atlas for real-time GPS and fleet tracking. It showcases how to acquire telemetry data from fleets using Voluble, process events with ksqlDB, and store historical data and fleet management information in MongoDB Atlas. The architecture leverages Kafka Connect with MongoDB Source and Sink connectors to bridge Confluent Cloud and MongoDB.
  15. Stream Processing with IoT Data: Best Practices & TechniquesThis post details best practices and techniques for stream processing with IoT data, focusing on the challenges of variable connectivity, bursty data, mixed service levels, and diverse data formats. It highlights Apache Kafka as the core technology for durable storage, horizontal scalability, high throughput, and low latency. The post discusses designing a system around Kafka, including raw data ingest via intermediaries (REST/MQTT), handling large messages and backlogs, and implementing a partitioning strategy based on message reception epoch milliseconds for uniform distribution. It also touches upon the need for different mechanisms for storing and processing messages with varying durability and latency requirements.
  16. Stream, Analyze, and Visualize Wi-Fi Data with Kafka and FriendsThis post details the process of capturing Wi-Fi packet data (pcap) using a Raspberry Pi and streaming it to Apache Kafka. It demonstrates how to use Kafka Connect with Elasticsearch and Kibana for visual analysis of the data, including record counts, MAC address distributions, and packet type frequencies. It also showcases the use of ksqlDB for numerical analysis, including creating streams and tables to aggregate and query packet subtypes by day. The post further illustrates how to use ksqlDB to create a sink connector for PostgreSQL to persist these aggregates and how to use Kibana's graph capabilities to visualize relationships between devices and access points. Finally, it describes splitting the pcap data into separate Kafka topics using ksqlDB for more targeted analysis and schema management.
  17. Building a Telegram Bot Powered by Kafka and ksqlDBThis post details building a Telegram bot powered by Kafka and ksqlDB. It demonstrates how to ingest Wi-Fi packet data into Kafka, process it with ksqlDB to create stateful aggregates (tables), and then query these aggregates using pull queries to serve on-demand information to a Telegram bot. The technical depth includes ksqlDB schema declaration, table creation with aggregations (COUNT, MIN, MAX, COLLECT_SET), and the use of interactive queries (via REST API) to access the materialized state. It also touches upon the underlying storage mechanisms of ksqlDB (Kafka changelog, RocksDB) and the concept of events as the primary system of record.
  18. Scylla and Confluent Integration for IoT Deployments | ConfluentThis post details an end-to-end IoT architecture pattern integrating MQTT, Apache Kafka, and ScyllaDB. It explains how MQTT is used for device communication and lightweight messaging, while Kafka handles high-velocity data ingestion. ScyllaDB is used as a persistent, scalable NoSQL store for long-term data storage. A demo is provided showcasing the use of Confluent MQTT Proxy, Kafka Connect Cassandra connector, and Mosquitto for simulating sensor data and streaming it into ScyllaDB.
  19. Creating an IoT-Based, Data-Driven Food Value Chain with Confluent Cloud | ConfluentThis post details how BAADER is using Confluent Cloud to build an IoT-based, data-driven food value chain. It highlights the use of Kafka and Confluent Cloud as a core digital platform for capturing and processing IoT event data from farm to fork. Specific technical examples include using KSQL to monitor machine idle times in processing facilities and integrating GPS data with the Google Distance Matrix API for delivery truck arrival time predictions. The post also mentions the collection of weather data to analyze its impact on product quality and animal welfare. BAADER emphasizes the benefits of Confluent Cloud for its managed Kafka service, enabling them to focus on application development rather than infrastructure management. They also highlight the use of KSQL for stream processing and filtering, and the overall event-driven architecture as a key to their data strategy.
  20. Baader's IoT and Data Streaming Innovation in Food Value ChainThis post details BAADER's use of Confluent Cloud and Apache Kafka to build a data-driven food value chain, capturing data from farm to fork. It highlights the use of Confluent Cloud for its scalability, reliability, and connectors for integrating disparate data sources. The post also mentions Mojix's retail IoT platform using Confluent Cloud for real-time inventory management. Key technical aspects include using Kafka Connect, KSQL, and Kafka Streams for collecting, transporting, storing, and processing IoT events globally. It emphasizes Confluent Cloud's ability to handle massive data volumes, provide real-time analytics, and support hybrid/multi-cloud environments. BAADER specifically uses KSQL for stream processing and connectors to Elasticsearch and Kibana for visualization.
  21. Noise Mapping with KSQL, a Raspberry Pi and a Software-Defined Radio | ConfluentThis post details using a Raspberry Pi with a Software-Defined Radio (SDR) to capture ADS-B aircraft transmissions. The captured data is then sent to Kafka topics (location-topic and ident-topic) via the Confluent REST Proxy. KSQL is used to enrich this data by joining it with reference data (ICAO to aircraft mapping and callsign details) to create new streams (location_and_details_stream and ident_callsign_stream). These enriched streams are then pushed to Elasticsearch using Kafka Connect for visualization in Kibana dashboards. The post demonstrates a specific use case of identifying a low-flying aircraft that woke the author's cat by querying the enriched location data.
  22. We ❤️ syslogs: Real-time syslog Processing with Apache Kafka and KSQL—Part 2: Event-Driven Alerting with SlackThis post details how to use the Confluent Kafka Python client and KSQL to build an event-driven alerting system. It demonstrates filtering syslog data in real-time with KSQL to identify suspicious login attempts and then using a Python script to consume these filtered events from Kafka and send push notifications to Slack. The post covers installing the Slack and Confluent Kafka Python clients, consuming messages from Kafka, and integrating the two to send Slack notifications based on Kafka topic messages.