#timeseriesdata — Public Fediverse posts

Every #TimeSeriesDatabase is just a set of storage decisions:
➡️ Row layout
➡️ Compression timing
➡️ Partitioning strategy

These choices often impact cost and query performance more than the database you pick.

This #InfoQ article breaks down these fundamentals from first principles using #PostgreSQL & #ApacheParquet ⇨ https://bit.ly/4fkDHlV

#BigData #TimeSeriesData #Database

Every #TimeSeriesDatabase is just a set of storage decisions:
➡️ Row layout
➡️ Compression timing
➡️ Partitioning strategy

These choices often impact cost and query performance more than the database you pick.

This #InfoQ article breaks down these fundamentals from first principles using #PostgreSQL & #ApacheParquet ⇨ https://bit.ly/4fkDHlV

#BigData #TimeSeriesData #Database

Every #TimeSeriesDatabase is just a set of storage decisions:
➡️ Row layout
➡️ Compression timing
➡️ Partitioning strategy

These choices often impact cost and query performance more than the database you pick.

This #InfoQ article breaks down these fundamentals from first principles using #PostgreSQL & #ApacheParquet ⇨ https://bit.ly/4fkDHlV

#BigData #TimeSeriesData #Database

Every #TimeSeriesDatabase is just a set of storage decisions:
➡️ Row layout
➡️ Compression timing
➡️ Partitioning strategy

These choices often impact cost and query performance more than the database you pick.

This #InfoQ article breaks down these fundamentals from first principles using #PostgreSQL & #ApacheParquet ⇨ https://bit.ly/4fkDHlV

#BigData #TimeSeriesData #Database

Every #TimeSeriesDatabase is just a set of storage decisions:
➡️ Row layout
➡️ Compression timing
➡️ Partitioning strategy

These choices often impact cost and query performance more than the database you pick.

This #InfoQ article breaks down these fundamentals from first principles using #PostgreSQL & #ApacheParquet ⇨ https://bit.ly/4fkDHlV

#BigData #TimeSeriesData #Database

How #Netflix boosted #ApacheDruid performance: by implementing interval-aware caching, they now serve 84% of analytics results from cache and have reduced query load by 33%.

The secret? Decomposing rolling window queries into reusable time segments.
✅ Reduces scan volume
✅ Improves P90 latency
✅ Optimizes real-time analytics

Details on #InfoQ: https://bit.ly/4uHG4DE

#SoftwareArchitecture #DistributedSystems #DataAnalytics #TimeSeriesData #Caching #BigData #DataEngineering

CrateDB is designed to effortlessly manage your time-series data⌛️

Want to learn more? Head over to our solutions page and discover why CrateDB is a perfect fit for time series💡 https://hubs.ly/Q01_qtsS0

#db #data #database #CrateDB #TimeSeriesData #DataManagement #SQLDatabase

Did you miss last week's webinar?👀

Watch the recording now to start leveraging time-series data for your business success🚀👇

In this webinar, you’ll gain expert insights on time-series data analysis and learn the crucial data modeling decisions needed to implement time-series data in CrateDB 👩🏻‍💻
https://crate.io/resources/webinars/lp-wb-time-series-data

#crateio #cratedb #db #database #webinar #recording #livedemo #data #database #datascience #timeseries #timeseriesdata #timeseriesanalysis #dataanalysis

Working with time-series data in ClickHouse introduces date/time types, querying, counters & gauge metrics, codecs, materialised views, and scaling.

All the information, in one place. Why use a time-series DB when you have ClickHouse?

https://clickhouse.com/blog/working-with-time-series-data-and-functions-ClickHouse

#timeseriesdata #timeseriesdb #timeseriesdatabase #clickhouse #db