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.
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