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).

Combine time series with document data: CrateDB is all you need.

Analyzing device readings with metadata integration

Time series: Analyzing weather data

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.

Advanced queries on time series data: CrateDB is all you need.

Analyzing weather data

Time series: Process financial data

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.

Custom ETL tasks: CrateDB is all you need.

Process financial data using CrateDB, Jupyter, and pandas