Examind's big data plug-in

By integrating Examind Datacube into your data infrastructure, you extend its ability to process very large volumes of georeferenced data in real time.

Geospatial datalakes at your fingertips

Datacube gives you a unified interface for a wide range of files and database types

Using the OGC GeoAPI standard, Examind Datacube enables the interconnection of geographic analysis and processing technologies in both Java and Python.

Combining Examind Server with the Datacube and a notebook, we can create a GeoDatascience workshop that provides real-time visualization of the results of elaborate algorithms.

The key points of the extension

Access multiple data sources

At the core of your infrastructure, Examind Datacube indexes the available resources on the target Datalakes. This comprehensive extension, capable of managing a wide variety of geospatial formats, allows libraries that do not natively support these formats to handle the data.

Process massive amount of data

Examine Datacube features advanced mechanisms (indexing, optimized data readers, scalability...) to extract and transform data on the fly, while guaranteeing performance. The entire DataLake can thus be exploited without any prior data preparation.

Handle all dimensions of your data

All data is accessible through products for which you choose the granularity and structure.

Work with this data in 2D, 3D, or even 4D using programmatic APIs or standard OGC dissemination services.

Metadata

CS-W & STAC : for data and metadata management

Cartographie

WMS et WMTS : for data visualization with classic services

Données Raster

WCS : for Coverage data distribution

Vector data

WFS : for vector data distribution

Senors data

SOS & SensorThings : for distribution of observation data and retrieval of information from connected objects

Geoprocessing

WPS : for distributed processing 

G.H.O.M problematics

data fusion

CREATE INFINITE PROCESSES

Take advantage of a wide range of processing options for data extraction and manipulation, which can be combined with third-party libraries through the DataCube APIs. This enables you to prototype and then move to production with the processing solution that perfectly meets your needs.Prototyping can be carried out using Notebooks (Jupyter or Zeppelin).

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