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Directorate-general | JRC

Big Data Analytics Platform

Multi-petabyte scale storage, data services and data analytics for policy support


The JRC Big Data Analytics Platform (BDAP) links data, data services, data scientists and thematic experts for generating policy relevant insights and foresight. It will play an instrumental role in advancing JRC to better mobilise and synthesise its collective knowledge and expertise in support to the EC priorities.

Building blocks

BDAP is build around a petabyte-scale storage system coupled with a processing cluster, accessible from anywhere through encrypted protocols and multi-factor authentication. It provides interactive data analysis tools, a remote data science desktop and distributed computing with specialized hardware for machine learning and deep learning tasks. It groups services for data analytics, data visualisation and data dissemination under the same platform.

Main services

All the BDAP services are built on top of the hardware layer, which consists of servers dedicated to storage and servers dedicated to processing. The multi-petabyte storage is managed by EOS, the distributed file system created by CERN, that allows all the services to see the full storage capacity as a single volume. Dedicated hardware for machine learning and deep learning is also available. On top of the hardware layer, three main services are built, as displayed in the following image:

Quick guide

A quick guide to BDAP and its services is available for downloading at the following link:

Quick guide to BDAP
(1.2 MB - PDF)

BDAP services requiring authentication are mainly serving JRC users. Please consult the JRC Service Catalogue for the Big Data Analytics Platform  for more information.


Pyjeo user guide

User guide of the pyjeo library: how to manage geospatial data in JEO-desk and JEO-batch

Interapro user guide

User guide of the interapro library: how to visualise and analyse geospatial datasets in the JEO-lab Jupyter notebooks

Vois library user guide

User guide of the vois library: library to simplify the development of impactful web applications as Voila' dashboards

Libraries for GUI development available in JEO-lab

Links to help pages of the main libraries to use to create graphical user interface elements in Jupyter notebooks and Voilà dashboards:

Widgets GUI elements:
ipywidgets         ipyvuetify         vuetifyjs        
Tabular data:
bqplot         plotly         bokeh         matplotlib
Custom drawing:
Hierarchical data display:
Events management:

How to access the platform

Access to BDAP services requires users to have a BDAP account. Registration is restricted to the Joint Research Centre and its contractors and partners. The access to the registration page requires to have already an EU-Login account.


List of services provided by the JRC Big Data Analytics Platform:

Quick access to BDAP services

The JEO-lab is a Jupyter notebook environment, intended primarily to interactively analyse and visualize data via a dedicated API. In addition to the usage of the API, JEO-lab is also used for starting up machine learning notebooks or accessing project-specific notebook containers for selected use cases. JEO-lab documentation


The JEO-desk is a desktop terminal service that provides a graphical Linux remote desktop terminal (Xubuntu based) accessible from within a modern web browser with support for HTML5. This virtual terminal is provided from machines located within the JEODPP infrastructure and therefore with fast connection to the JEODPP data. JEO-desk documentation


The JEO-batch service provides computing power to the JRC experiments for tasks such as high scale image processing, data analysis and simulation. It is an advanced, distributed system designed mainly for high-throughput scientific computing. JEO-batch documentation


The JEO-cloud service is based on Nextcloud and is aimed at facilitating collaboration between JEODPP users and making easier the transfer of documents (scripts, project files etc.) and small datasets between the user personal computer and the JEODPP Terminal Service. It also provides some functionalities useful to provide remote support to JEODPP users by the JEODPP team (screen sharing and videoconferencing). JEO-cloud documentation

Voilà dashboards

Voilà is a Jupyter notebook extension to automatically create standalone applications and dashboards. Notebooks are rendered by showing only the output of the cells, while the code is hidden. Suitable for non-technical experts for communicating insights and foresight to a wider audience. Single environment for full data analytics workflows from research and innovation to outreach engaging policy makers and citizens.

As an example of a Voilà dashboard, the CollectionsExplorer is a good starting point to explore the geospatial datasets stored in the platform.

GitLab instance

GitLab is the DevOps platform used at BDAP. It helps BDAP team members and users to collaborate on software development and to provide a place where everyone can contribute. Users can add issues in the BDAP GitLab instance to ask for new features, to evaluate new dataset downloading, to request the installation of new software packages and to report bugs or problems in the BDAP services.


Data provided by BDAP:

Data Catalogue

Web data catalogue for exploring and browsing all the collections stored in BDAP. It follows the SpatioTemporal Asset Catalog (STAC) specifications providing a common language to describe geospatial data.

Collections Explorer

Voila' application to browse, visualise and compare the main geospatial datasets available at BDAP.

Platform information


BDAP is the successor of the JRC Earth Observation Data and Processing Platform, widening the scope of the platform towards any type of Big Data analytics. The commonly known name "JEODPP" is continued to be used in most documentations and services URL's. Here a timeline of the project progress:

  1. 1 January 2015
    Earth Observation and Social Sensing Big Data pilot project
    Kick-off of EO&SSBD as a pilot project
  2. 1 March 2016
    Purchase and installation of the first hardware
    JRC Big Data Platform starts its operativity with the first servers installed and configured
  3. 1 January 2019
    Big Data Analytics project launched as an institutional project
    JRC Big Data Platform ends the pilot phase and enters full institutional
  4. 12 December 2020
    Big Data Analytics Platform recognised as an official EC IT platform
    EC ITC and Cybersecurity Board (ITCB) provide a positive opinion on the evolution of the platform towards a Big Data Analytics Platform as a component of the EC Data Platform


~175 servers
In the JRC Data Center
For storage, processing jobs and services
~4,500 cores
12-19 GBs of RAM per core
For JEO-batch/desk/lab and other services
10 GPU servers
38 Nvidia GPU's in total
For machine learning and deep learning
28.4 PiB storage
14.2 PiB net capacity
For datasets and satellite images storage

Software stack

The JRC Big Data Analytics Platform is mainly built on Open Source Software. Here a partial list of the tools and libraries used:


The publications listed here correspond to all publications registered in pubsy and containing at least one co-author from the Big Data Analytics Platform. Numerous publications are the result of fruitful collaborations with other JRC projects and external partners.

List of all publications
(159 KB - PDF)

Reference publication to be used for citing BDAP in papers:

P. Soille, A. Burger, D. De Marchi, P. Kempeneers, D. Rodriguez, V. Syrris, and V. Vasilev. “A Versatile Data-Intensive Computing Platform for Information Retrieval from Big Geospatial Data”. Future Generation Computer Systems 81.4 (Apr. 2018), pp. 30–40. doi: 10.1016/j.future.2017.11.007.

Recent publications:

P. Soille, S. Loekken, and S. Albani, eds. Proc. of the 2023 Conference on Big Data from Space (BiDS’23). ESA-JRC-SatCen. Publications Office of the European Union, Nov 2023. doi: 10.2760/46796 D. De Marchi, A. Burger, F. Eyraud, and P. Soille, “VOIS library: Pushing data science dashboards to the limits,” in Cloud Storage Synchronization and Sharing (CS3 2023), CERN, Mar. 2023, pp. 2–4. [Online]. Available: R. d’Andrimont, M. Claverie, P. Kempeneers, D. Muraro, M. Yordanov, D. Peressutti, M. Batic, and F. Waldner, “AI4Boundaries: An open AI-ready dataset to map field boundaries with Sentinel-2 and aerial photography,” Earth System Science Data, vol. 15 (1), pp. 317–329, 2023. doi: 10.5194/essd-15-317-2023. P. Kempeneers, M. Claverie, and R. d’Andrimont, “Using a vegetation index as a proxy for reliability in surface reflectance time series reconstruction (RTSR),” Remote Sensing, vol. 15 (9), 2023. doi: 10.3390/rs15092303. P. Soille and P. Vogt, “Morphological spatial pattern analysis: Open source release,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, vol. XLVIII-4/W1-2022, 2022. doi: 10.5194/isprs-archives-XLVIII-4-W1-2022-427-2022. P. Kempeneers, T. Kliment, L. Marletta, and P. Soille, “Parallel processing strategies for geospatial data in a cloud computing infrastructure,” Remote Sensing, vol. 14 (2), 2022. doi: 10.3390/rs14020398. P. Kempeneers, O. Pesek, D. De Marchi, and P. Soille. “A Python Package For The Analysis of Geospatial Data”. International Journal of Geo-Information 8.10 (2019). doi: 10.3390/ijgi8100461

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Ask for support
Create an issue in BDAP GitLab instance
Follow issues
by accessing BDAP GitLab instance or by email (reply to GitLab email notifications)
Request the creation of a use case
Create a new use case by filling and returning this Excel template
BDAP Community Teams channel
BDAP Community Channel on Teams


BDAP is made by people who love IT and Data Science:

Project leader:
Pierre Soille
Statutory staff:
Armin Burger
Pieter Kempeneers
Davide De Marchi
Paul Hasenohr
Marco Scavazzon
Roberto Ugolotti
Chiara Chiarelli
Silviu Onofrei

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