How to Create the Google for Earth Data (XLDB 2015, Stanford)

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Rainer Sternfeld presented on creating a Google-like platform for earth data using Planet OS. He described the challenges NOAA faces in managing tens of terabytes of weather data per day across scattered systems. Planet OS could index NOAA's metadata and downsample remote datasets via APIs. It would store chunked array data in object stores like S3 and provide on-demand computing via cloud services. This would make NOAA's large-scale data easily discoverable and machine-readable while addressing issues like data volume, transport, and real-time dissemination.

Data & Analytics

XLDB CONFERENCE 2015, STANFORD UNIVERSITY MAY 2015
How To Create the Google for Earth Data
Rainer Sternfeld
CEO & co-founder
in the example of NOAA Big Data Project

2 MAY 2015XLDB CONFERENCE 2015, STANFORD UNIVERSITY
Finding and accessing the right data is really hard
Planet OS Data Discovery makes it easy
Crawl the web and index the most recent data
without moving the data itself until you need it

3 August 2014
Cloud Platform for Industrial Sensor Networks
Building with:
Scala, Akka
RabbitMQ, Kafka
HDFS, HBase, Elasticsearch
Spark, Spark Streaming
GIS Libraries (raster, geometry) 
Compressed array formats
Sensor Data Discovery & Exchange
Search, Exploration
Visualisation, Analytics
Data Management
Marketplace
APIs

4 MAY 2015XLDB CONFERENCE 2015, STANFORD UNIVERSITY
marinexplore.org — the biggest deployment of Planet OS
43,000+ data streams, 35 organizations, 8,000 users
Data Discovery Raster data, heatmap overlays Access with third party applications
Raster data with quiver plot overlays Rich graph visualizations Build custom datasets

5 April 2015
Marine data website
Visualising
Browsing
Filtering
Export
Built with:
Python/Cython
RabbitMQ, Celery
Postgres, PostGIS, Vertica 
GIS Libraries (raster, geometry)
NumPy
marinexplore.org

6 MAY 2015XLDB CONFERENCE 2015, STANFORD UNIVERSITY
How to make NOAA’s large-scale weather and climate data
easily discoverable and machine-readable?
Real-time weather & climate data is
a global multi-billion dollar opportunity

7 MAY 2015XLDB CONFERENCE 2015, STANFORD UNIVERSITY
NOAA’s Challenge
• Tens of thousands of devices deployed in the ocean, on land, and space
• Critical for the government and industries
• Hundreds scattered web services (FTPs, flat files, THREDDS/OPeNDAP API)
• Data grows 10TB+ per day
• 26,595 NOAA datasets with ISO-19139 metadata
• 4,894 NOAA datasets with OpenDAP interface

8 MAY 2015XLDB CONFERENCE 2015, STANFORD UNIVERSITY
So what’s wrong with how it’s done now?
An enthusiastic young researcher starts downloading data to an
external HDD connected to his laptop — data keeps coming,
external HDDs pile up…
 
the beard’s getting longer and longer, and when the data is
finally downloaded, we have a middle-aged, bored professor

9 MAY 2015XLDB CONFERENCE 2015, STANFORD UNIVERSITY
Technical Challenges in
the NOAA Big Data Project
• Storing, processing, and indexing spatio-temporal time-series & array data
• Processing data at 10s (100s) of TB/day
• Transporting and processing archives at volumes 10s (100s) of PB
• Disseminating real-time data at latency of minutes
• Indexing 100K+ logical datasets and 100M+ technical datasets/files
• Providing uniform API/export for various data formats/protocols/projections

10 MAY 2015XLDB CONFERENCE 2015, STANFORD UNIVERSITY
Potential solutions
under consideration
• Indexing spatio-temporal and semantic metadata
• Indexing downsampled remote datasets acquired via OpenDAP and others
• Store chunked array data (MBs) in object store (e.g. Amazon S3)
• Provide on-demand computational infrastructure for analyzing data (e.g. Amazon)

11 MAY 2015XLDB CONFERENCE 2015, STANFORD UNIVERSITY
What would make it even better?
• Incremental data compression
• BitTorrent-like data dissemination
• Sending pre-filtered data to consumers (e.g. by area)
• Computations scheduled next to the data storage
• Fast interconnect (10 GBit / Inifiniband) and GPUs
• Run analytical scripts (eg IPython Notebook, Matlab) to
work with array data in the cloud

12 MAY 2015XLDB CONFERENCE 2015, STANFORD UNIVERSITY
DATA
EXCHANGE
APIsPLANET OS
ALGORITHMS
3RD PARTY
INTEGRATIONS
DISCOVER
COLLABORATE
VISUALIZE
ENTERPRISE
DATA
ALL PUBLIC DATA
MODELS APPS
ANALYZE

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