Time series fundamentals with CrateDB¶
Learn how to conduct fundamental data analysis on large time series datasets with CrateDB.
Metadata integration Advanced SQL for time series
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 advanced SQL 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.
Acquire and store historical data from S&P-500 companies into CrateDB using Python.
What’s Inside
Acquire historical stock ticker data from the Yahoo! Finance API.
Store data into CrateDB.
Query back data from CrateDB.
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
Display millions of data points using hvPlot, Datashader, and CrateDB
HoloViews and Datashader frameworks enable channeling millions of data points from your backend systems to the browser’s glass.
This notebook plots the venerable NYC Taxi dataset after importing it into a CrateDB Cloud database cluster.
Note: 🚧 This notebook is a work in progress. 🚧
Time series visualization
Python HoloViews hvPlot Datashader
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 often requires special feature support to enable fluent data workflows.
Time series analysis¶
Analyze time series data with statistical and machine learning techniques, for time series anomaly detection and forecasting.
Learn how to create a machine learning pipeline using R and CrateDB.
Use pandas and scikit-learn to run a regression analysis within a Jupyter Notebook.
Learn how to build a machine learning model that will predict whether a machine will fail within a specified time window in the future.
Learn how to conduct advanced data analysis on large time series datasets with CrateDB, MLflow, and PyCaret: Anomaly detection and forecasting, time series decomposition, Exploratory data analysis (EDA).
Big data operations¶
CrateDB clusters can elastically scale to store and query large time series data efficiently. CrateDB provides corresponding operational support.