Choosing an IoT Big Data Platform: Hadoop vs TDengine and Other Time‑Series Databases

The presentation introduces the rapid growth of IoT data, distinguishing static and dynamic (time‑series) data, and explains why traditional real‑time databases struggle with horizontal scaling, outdated architectures, weak analytics, and lack of cloud support.

It outlines the four‑step IoT/Industry 4.0 data pipeline—data collection, edge computing, storage/query/compute, and application delivery—highlighting the importance of edge processing and cloud data engines.

Traditional real‑time databases (e.g., OSIsoft PI) are described, followed by their limitations: no horizontal scaling, costly hardware, limited analytics, and no PaaS capability.

The article then discusses generic big‑data solutions using Hadoop, Kafka, Spark/Flink, and Redis, noting their suitability for massive batch processing but high cost and complexity for smaller deployments.

Several real‑world case studies are presented:

Smart‑park power monitoring system using an outdated real‑time database and Oracle for history, suffering from limited historical analysis and upgrade difficulty.

Vehicle telematics data warehouse built on Hadoop, facing high hardware and maintenance costs, poor real‑time query performance, and scaling challenges.

Industrial equipment management system combining Kafka, Redis, relational DB, and Cassandra, with fast writes but slow queries.

High‑frequency factory data acquisition requiring 20 ms sampling and massive throughput, suggesting TDengine or Prometheus.

Electric‑vehicle real‑time detection system that outgrew MySQL and switched to TDengine for better write performance and scalability.

Key selection criteria for time‑series databases are summarized: high‑performance real‑time writes and queries, columnar storage for structured data, unique per‑sensor data streams, minimal updates/deletes, efficient expiration handling, write‑heavy workloads, stable traffic patterns, and support for massive data volumes.

The article concludes with a checklist covering performance gains, business value, total cost of ownership (hardware, operations, development), and emphasizes testing specific pain points to choose the optimal IoT big‑data platform.