Get Started Free
No time limit - totally free - just the way you like it.Sign Up Now
Companies and other organizations have been using metrics stored in time series databases (TSDBs) for critical functions such as monitoring, alerting and automating processes. However, they had a harder time deriving other insights and value from those databases due to limitations imposed by cardinality constraints and specialized query languages.
Now, the evolution of Apache Arrow — a popular open source multilanguage toolbox for accelerated data interchange and in-memory processing — creates new opportunities for improved real-time analytics and time series applications beyond traditional use cases such as climate modeling, finance and even AI.
Indeed, users of time series databases historically struggle with high-cardinality use cases, according to Rachel Stephens, an analyst for RedMonk. High-cardinality data sets are those that have a large and often unbounded set of unique possible values in a given field.
Read the full story here.