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Time Series Data Modeling

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CrateDB is not only a pure time series database; it also accommodates a variety of data types such as JSON, geospatial, vectors, full-text search, and binary large objects (BLOBs).

  • Relational and Time Series Data: CrateDB uses a simple relational representation of time series data, allowing easy querying with standard SQL, including window and aggregation functions.
    -- Read more on CrateDB as a relational database or time series database

  • Document/JSON Data: CrateDB supports JSON data, which is ideal for importing/exporting time series data. It allows direct insertion of JSON payloads with new attributes automatically indexed.
    -- Read more on CrateDB as a JSON database

  • Full-Text Search: CrateDB handles textual data effectively, offering robust full-text search capabilities for exact matches, typos, phrases, and synonyms.
    -- Read more on CrateDB as a search engine

  • Vector Search: CrateDB supports float vector data type for columns, accommodating up to 2048 dimensions. It uses a Hierarchical Navigable Small World graph for efficient indexing of vectors.
    -- Read more on CrateDB as a vector database

  • Geospatial Data: It handles geospatial data, enabling tracking of changes over time in a geographic area, vital for numerous applications.
    -- Read more on CrateDB as a geospatial database

  • Binaries (BLOBs): CrateDB manages binary large objects (BLOBs) efficiently, crucial for multimedia content or large log files in time series applications.
    -- Read more on CrateDB for BLOBs

Time Series Data Modeling

In this tutorial video, we explore time series data modeling in CrateDB. We will see that CrateDB is not just a time series database; it supports various different data types. Then, we will showcase the use of native SQL and how to query different data types.

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Modeling Data in CrateDB

The workshop introduces the fundamental concepts of CrateDB, and how it can be used to efficiently store, query, and analyze large amounts of data. You will learn how to efficiently model data in CrateDB.

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Guide for Time Series Data Projects

CrateDB simplifies SQL querying for timestamped, append-only records and supports JSON data for storing arbitrary contextual details. CrateDB also handles textual data, floating-point vectors, geospatial data, and binary large objects (BLOBs) efficiently. Its strong full-text search capabilities, spatial context provision through geospatial data, and efficient handling of BLOBs make it an excellent choice for managing complex time series data.

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