Apache IoTDB

TLDR

The volume of time‑series data has exploded with the rise of IoT devices, demanding efficient edge and cloud management that supports high‑throughput ingestion, low‑latency queries, and advanced analysis. This demonstration presents Apache IoTDB as a time‑series database that enables new classes of IoT applications. IoTDB offers edge and cloud versions with an optimized columnar file format, high ingestion rates, low‑latency queries, and support for time‑series operations such as aggregations, down‑sampling, and sub‑sequence similarity search, and it demonstrates real‑time edge‑to‑cloud data management integrated with Hadoop, Spark, PLC4x, Calcite, and Grafana.

Abstract

The amount of time-series data that is generated has exploded due to the growing popularity of Internet of Things (IoT) devices and applications. These applications require efficient management of the time-series data on both the edge and cloud side that support high throughput ingestion, low latency query and advanced time series analysis. In this demonstration, we present Apache IoTDB managing time-series data to enable new classes of IoT applications. IoTDB has both edge and cloud versions, provides an optimized columnar file format for efficient time-series data storage, and time-series database with high ingestion rate, low latency queries and data analysis support. It is specially optimized for time-series oriented operations like aggregations query, down-sampling and sub-sequence similarity search. An edge-to-cloud time-series data management application is chosen to demonstrate how IoTDB handles time-series data in real-time and supports advanced analytics by integrating with Hadoop and Spark. An end-to-end IoT data management solution is shown by integrating IoTDB with PLC4x, Calcite, and Grafana.