Time-series data:
what it is and why it matters

Time-series data collectively represents how a system, process or behavior changes over time.

Time isn't just a metric, but a primary axis.

Today's applications are capturing and analyzing more and more data in faster ways than before. Whether it be cars collecting data about the environment around them, or a building sensor collecting data about temperature & conditions, this data needs to be stored and analyzed based on how things change over time.

Optimize for time-series
storage and analysis

Capturing, storing and analyzing time-series data, regardless of their application, share common patterns. To do this well at scale, you want to optimize the system for these situations:

Scale and performance
Time-series data is generated at high-volume. This data must be stored efficiently to be inserted and retrieved quickly.
Time-series analytics
Special types of queries based on the temporal nature of data: time bucket, gap filling and approximation algorithms.
Time-oriented data management
Data retention policies, data reordering, data tiering, compression, all based with time as primary axis.