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Massive data streams

Massive data streams. Space …. are big. Really big. You just won't believe how vastly, hugely, mindbogglingly big they are. Douglas Adams – Hitchhiker’s Guide to Big Data. Massive Data Streams . SDO (The devil we know!) 650 TB of data a year Storable / Distributable

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Massive data streams

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  1. Massive data streams Space …. are big. Really big. You just won't believe how vastly, hugely, mindbogglingly big they are. Douglas Adams – Hitchhiker’s Guide to Big Data

  2. Massive Data Streams • SDO (The devil we know!) • 650 TB of data a year • Storable / Distributable • Possible perhaps for 6TB per day • Efforts to locate data of interest • FFT / HEK • For FFT some modules need to run in near real time for space weather applications • Visualize the data stream • Helioviewer • But people still want full resolution / full cadence science data • No real access to level 0

  3. Massive Data Streams • ATST • ATST ~7 PB a year (8 hour duty cycle) • Process at night?! • Is it distributable? • Products probably but not like SDO • No access to “raw” data products • You’ll just have to trust us • Not reprocessable?

  4. Massive Data Streams • Moore’s Law (like increases) won’t help you now …

  5. Massive Data Streams • Astronomy • Large Synoptic Survey Telescope (LSST) • 30 TB per night • Google will help them!

  6. Massive Data Streams • Particle Physics • LHC • 15 PB a year • Very bursty • Hardware challenges • But rarely on!! • Reduced data products are very small

  7. Massive Data Streams • EST • 3.5 PB per day!!! • Will throw away data …

  8. Massive Data Streams • Challenges • Storing data locally • Processing data • Need for true real time pipeline for data reduction etc. • High speed, high quality, robust, algorithms for real time science analysis, modeling / forecasting • More computer scientists in solar physics? • Distributing data • Visual data will always hard to distribute • Abstract information to databases using automated methods • NMM? (Not my method!) • Database mining • User interaction with the data? • Bring on the statisticians!

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