Time Series Fundamentals with CrateDB¶
Getting Started¶
After evaluating connectivity options, you would like to get hands-on with CrateDB. We prepared a few introductory tutorials, some of them in executable forms, to demonstrate CrateDB’s features to work with time series data on the spot. You may want to use them as starting points for your own explorations.
CrateDB supports effective time series analysis with enhanced features for fast aggregations.
What’s Inside
Rich data types for storing structured nested data (OBJECT) alongside time series data.
A rich set of built-in functions for aggregations.
Relational JOIN operations.
Common table expressions (CTEs).
CrateDB provides enhanced features for querying time series data.
What’s Inside
Run aggregations with gap filling / interpolation, using common table expressions (CTEs) and LAG / LEAD window functions.
Find maximum values using the MAX_BY aggregate function, returning the value from one column based on the maximum or minimum value of another column within a group.
CrateDB for Time Series Modeling, Exploration, and Visualization
Access time series data from CrateDB via SQL, load it into pandas DataFrames, and visualize it using Plotly.
About advanced time series operations in SQL, like aggregations, window functions, interpolation of missing data, common table expressions, moving averages, relational JOINs, and the handling of JSON data.
Time series visualization
Python pandas Plotly Dash
Notebook: How to Build Time Series Applications with CrateDB
This notebook illustrates how to import and work with time series data using CrateDB and Dask DataFrames. Dask is a framework to parallelize operations on pandas data frames.
Data I/O
Python Dask SQL
Special Features¶
Working with time series data needs special feature support to enable fluent data workflows.
Downsampling and Interpolation
Operations¶
CrateDB provides operational support to store and query large time series data efficiently.
Tip
For more in-depth information, please visit the documentation pages about Advanced Time Series Analysis. Alternatively, you may prefer the Video Tutorials.