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.

Time Series: Device Readings with Metadata

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).

Analyzing Device Readings with Metadata Integration
Time Series: Advanced SQL

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.

Analyzing Weather Data

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.

Notebook on GitHub Notebook on Colab

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.

Notebook on GitHub Notebook on Colab

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.