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#timeseriesdata — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #timeseriesdata, aggregated by home.social.

  1. 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 & #ApacheParquetbit.ly/4fkDHlV

    #BigData #TimeSeriesData #Database

  2. 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 & #ApacheParquetbit.ly/4fkDHlV

    #BigData #TimeSeriesData #Database

  3. 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 & #ApacheParquetbit.ly/4fkDHlV

    #BigData #TimeSeriesData #Database

  4. 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 & #ApacheParquetbit.ly/4fkDHlV

    #BigData #TimeSeriesData #Database

  5. Every 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 article breaks down these fundamentals from first principles using & bit.ly/4fkDHlV

  6. 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: bit.ly/4uHG4DE

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

  7. 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: bit.ly/4uHG4DE

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

  8. 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: bit.ly/4uHG4DE

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

  9. 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: bit.ly/4uHG4DE

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

  10. How boosted 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 : bit.ly/4uHG4DE

  11. Ever heard about SaQC, a software tool for reproducible #quality control of #timeseriesdata developed at @ufz? Here is what #wikidata knows about it: wikidata.org/wiki/Q128228853

  12. Ever heard about SaQC, a software tool for reproducible #quality control of #timeseriesdata developed at @ufz? Here is what #wikidata knows about it: wikidata.org/wiki/Q128228853

  13. Ever heard about SaQC, a software tool for reproducible #quality control of #timeseriesdata developed at @ufz? Here is what #wikidata knows about it: wikidata.org/wiki/Q128228853

  14. Ever heard about SaQC, a software tool for reproducible control of developed at @ufz? Here is what knows about it: wikidata.org/wiki/Q128228853

  15. Ever heard about SaQC, a software tool for reproducible #quality control of #timeseriesdata developed at @ufz? Here is what #wikidata knows about it: wikidata.org/wiki/Q128228853

  16. Grafana Mimir 3.0 is now live!

    This release introduces a new design that cleanly separates read & write operations, delivering significant gains in performance, reliability, and cost efficiency for organizations managing metrics at scale.

    Dive into the details and explore what’s new: bit.ly/4rqnD5G

    #InfoQ #DevOps #TimeSeriesData #Observability #Grafana

  17. Grafana Mimir 3.0 is now live!

    This release introduces a new design that cleanly separates read & write operations, delivering significant gains in performance, reliability, and cost efficiency for organizations managing metrics at scale.

    Dive into the details and explore what’s new: bit.ly/4rqnD5G

    #InfoQ #DevOps #TimeSeriesData #Observability #Grafana

  18. Grafana Mimir 3.0 is now live!

    This release introduces a new design that cleanly separates read & write operations, delivering significant gains in performance, reliability, and cost efficiency for organizations managing metrics at scale.

    Dive into the details and explore what’s new: bit.ly/4rqnD5G

    #InfoQ #DevOps #TimeSeriesData #Observability #Grafana

  19. Grafana Mimir 3.0 is now live!

    This release introduces a new design that cleanly separates read & write operations, delivering significant gains in performance, reliability, and cost efficiency for organizations managing metrics at scale.

    Dive into the details and explore what’s new: bit.ly/4rqnD5G

    #InfoQ #DevOps #TimeSeriesData #Observability #Grafana

  20. Grafana Mimir 3.0 is now live!

    This release introduces a new design that cleanly separates read & write operations, delivering significant gains in performance, reliability, and cost efficiency for organizations managing metrics at scale.

    Dive into the details and explore what’s new: bit.ly/4rqnD5G

  21. How time series data is fueling the final frontier

    When we think of space exploration, we picture towering rockets, satellites orbiting Earth and astronaut footprints on the…
    #NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Space #opinion #Science #SN #TimeSeriesData
    newsbeep.com/us/199061/

  22. How time series data is fueling the final frontier

    When we think of space exploration, we picture towering rockets, satellites orbiting Earth and astronaut footprints on the…
    #NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Space #opinion #Science #SN #TimeSeriesData
    newsbeep.com/us/199061/

  23. How time series data is fueling the final frontier

    When we think of space exploration, we picture towering rockets, satellites orbiting Earth and astronaut footprints on the…
    #NewsBeep #News #Space #CA #Canada #opinion #Science #SN #TimeSeriesData
    newsbeep.com/ca/186695/

  24. How time series data is fueling the final frontier

    When we think of space exploration, we picture towering rockets, satellites orbiting Earth and astronaut footprints on the…
    #NewsBeep #News #Space #AU #Australia #Opinion #Science #SN #TimeSeriesData
    newsbeep.com/au/184744/

  25. Deep dive into #Netflix’s Distributed Counter Abstraction - a scalable service that tracks user interactions, feature usage, and business performance metrics with low latency globally.

    “At Netflix, our counting use cases include tracking millions of user interactions, monitoring how often specific features or experiences are shown to users, and counting multiple facets of data during A/B test experiments, among others.”

    Learn more: bit.ly/49tO41Z

    #InfoQ #CaseStudy #DistributedSystems #EventsDrivenArchitecture #TimeSeriesData

  26. Deep dive into #Netflix’s Distributed Counter Abstraction - a scalable service that tracks user interactions, feature usage, and business performance metrics with low latency globally.

    “At Netflix, our counting use cases include tracking millions of user interactions, monitoring how often specific features or experiences are shown to users, and counting multiple facets of data during A/B test experiments, among others.”

    Learn more: bit.ly/49tO41Z

    #InfoQ #CaseStudy #DistributedSystems #EventsDrivenArchitecture #TimeSeriesData

  27. Deep dive into #Netflix’s Distributed Counter Abstraction - a scalable service that tracks user interactions, feature usage, and business performance metrics with low latency globally.

    “At Netflix, our counting use cases include tracking millions of user interactions, monitoring how often specific features or experiences are shown to users, and counting multiple facets of data during A/B test experiments, among others.”

    Learn more: bit.ly/49tO41Z

    #InfoQ #CaseStudy #DistributedSystems #EventsDrivenArchitecture #TimeSeriesData

  28. Deep dive into #Netflix’s Distributed Counter Abstraction - a scalable service that tracks user interactions, feature usage, and business performance metrics with low latency globally.

    “At Netflix, our counting use cases include tracking millions of user interactions, monitoring how often specific features or experiences are shown to users, and counting multiple facets of data during A/B test experiments, among others.”

    Learn more: bit.ly/49tO41Z

    #InfoQ #CaseStudy #DistributedSystems #EventsDrivenArchitecture #TimeSeriesData

  29. Deep dive into ’s Distributed Counter Abstraction - a scalable service that tracks user interactions, feature usage, and business performance metrics with low latency globally.

    “At Netflix, our counting use cases include tracking millions of user interactions, monitoring how often specific features or experiences are shown to users, and counting multiple facets of data during A/B test experiments, among others.”

    Learn more: bit.ly/49tO41Z

  30. 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💡 hubs.ly/Q01_qtsS0

  31. 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💡 hubs.ly/Q01_qtsS0

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

  32. 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💡 hubs.ly/Q01_qtsS0

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

  33. 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💡 hubs.ly/Q01_qtsS0

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

  34. Very excited to share that I'll be speaking at :postgresql: in less than a week! I'll be announcing the new for time series and of internal .

    Bonus talk: "Don't Do This" - PostgreSQL bad practices and pitfalls!
    👇
    2023.pgdaychicago.org/

  35. Very excited to share that I'll be speaking at #PGDay #Chicago :postgresql: in less than a week! I'll be announcing the new #PostgreSQL #extension #pg_statviz for time series #analysis and #visualization of #Postgres internal #statistics.

    Bonus talk: "Don't Do This" - PostgreSQL bad practices and pitfalls!
    👇
    2023.pgdaychicago.org/

    #timeseries #timeseriesdata #opensource #database #databases #stats #performance #event #events

  36. Very excited to share that I'll be speaking at #PGDay #Chicago :postgresql: in less than a week! I'll be announcing the new #PostgreSQL #extension #pg_statviz for time series #analysis and #visualization of #Postgres internal #statistics.

    Bonus talk: "Don't Do This" - PostgreSQL bad practices and pitfalls!
    👇
    2023.pgdaychicago.org/

    #timeseries #timeseriesdata #opensource #database #databases #stats #performance #event #events

  37. Very excited to share that I'll be speaking at #PGDay #Chicago :postgresql: in less than a week! I'll be announcing the new #PostgreSQL #extension #pg_statviz for time series #analysis and #visualization of #Postgres internal #statistics.

    Bonus talk: "Don't Do This" - PostgreSQL bad practices and pitfalls!
    👇
    2023.pgdaychicago.org/

    #timeseries #timeseriesdata #opensource #database #databases #stats #performance #event #events

  38. Very excited to share that I'll be speaking at #PGDay #Chicago :postgresql: in less than a week! I'll be announcing the new #PostgreSQL #extension #pg_statviz for time series #analysis and #visualization of #Postgres internal #statistics.

    Bonus talk: "Don't Do This" - PostgreSQL bad practices and pitfalls!
    👇
    2023.pgdaychicago.org/

    #timeseries #timeseriesdata #opensource #database #databases #stats #performance #event #events