Once largely reserved for the finance industry, times series databases are increasingly emerging as must-haves for many organizations. A classic SQL database,  for example, is not designed to process thousands or — in many cases — millions of data points per second that a time series database can monitor, track,  analyze and assimilate for forecasting applications for real-time analytics and business intelligence. These applications often include application, server, network  monitoring, for industrial or IoT data.

However, many organizations are faced with the conundrum of not being able to afford investing in the required hardware infrastructure for time series data analysis or most of their operations are cloud-based and they have faced a dearth of viable alternatives. As a solution, InfluxData, a leading data has launched InfluxDB Cloud 2.0, the first serverless time series platform as a service (PaaS).

In this The New Stack Makers podcast, Paul Dix, co-founder and CTO, InfluxData, discussed InfluxDB Cloud 2.0, and how the evolution of time series databases have evolved to create a need for a cloud-based alternative.