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Time Series

Time Series Data Lifecycle

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CrateDB handles the time series data lifecycle through efficient partitioning, tiering, retention, and snapshot strategies.

  • Partitioning & Data Retention: CrateDB's partitioning feature divides datasets into manageable portions and optimizes storage by automatically creating or removing partitions as needed. Data retention is managed using partitioned tables, and outdated data is removed using the DELETE FROM statement. Read more >

  • Data Tiering: CrateDB supports different storage types - hot for frequently accessed data and cold for less accessed data. Read more >

  • Snapshots: CrateDB's snapshots archive old table partitions at a specific moment, storing them in various backends. These are essential for data backup, restoration, and recovery from lost or corrupted data. Regular backup and restoration testing is crucial for disaster planning. Read more >

Guide to Sharding and Partitioning Best Practices in CrateDB

This tutorial offers an in-depth understanding of how partitioning and data lifecycle management work in CrateDB. It presents principles behind shard distribution and replication, and provides strategies to avoid common bottlenecks. 

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Guide to sharding and partitioning best practices in CrateDB
Whitepaper: "Guide for Time Series Projects: Data Modeling, Storage, and Lifecycle with CrateDB"

Guide for Time Series Data Projects

In this guide, you will dive deep into CrateDB's partitioning strategy, showcasing efficient data management for large datasets. It explores data tiering, demonstrating how to assign different storage types to cluster nodes. It also covers data retention, snapshot archiving, and recovery processes.

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