InfluxData Affirms Leadership in Time Series Database Category

SAN FRANCISCO, Jan. 9, 2018 -- InfluxData, the modern Open Source Platform built specifically for metrics, events and other time series data that empowers developers to build next-generation monitoring, analytics and IoT applications, today announced its continued position as the leader among Time Series Database management systems, according to DB-Engines' latest results published this month.

According to DB-Engines' January results, InfluxDB continues its overwhelming lead, outpacing the competition with its popularity increasing 57 percent over the past 12 months, and 14 percent quarter over quarter. InfluxDB is InfluxData's Time Series Database used as a data store for any use case involving large amounts of timestamped data -- including DevOps monitoring, application metrics, IoT sensor data, and real-time analytics. The DB-Engines results included rankings of the leading vendors in the Time Series Database category, with InfluxDB's user popularity ranking nearly three times more than the nearest competitor.

DB-Engines is an initiative to collect and present information on database management systems (DBMS). DB-Engines also rated the overall Time Series Database Management System category, which continues to grow in popularity with a 59 percent year-over-year increase, stronger than the previous year's growth rate of 42 percent.

"As DB-Engines shows, InfluxData continues its strong leadership position as the most popular solution in the time series database market," said Mark Herring, InfluxData CMO. "This continued growth in the rankings offers proof that users are turning to time series databases to collect and store data that shows metrics and events for IoT sensors, DevOps monitoring, and real time analytics. Time series databases are more efficient and work well for time series workloads -- other databases require significant overhead to handle time series data. InfluxData is uniquely positioned as being purpose-built, and proving to be faster and more flexible and efficient in monitoring and reporting on time series workloads."

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