JRC Big Data Analytics Platform: Home page

Vision

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

English

(1.2 MB - PDF)

Download

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

Documentation

Home page

Main page of the documentation wiki

Overview

How to access the platform

User documentation on authentication methods and how to access BDAP platform

Authentication

Services

Documentation for the platform services

JEO-lab JEO-desk JEO-batch Storage Document sharing Catalogue

BDAP libraries

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
Mapping:: ipyleaflet
Tabular data:: qgrid
Charting:: bqplot plotly bokeh matplotlib
Custom drawing:: ipycanvas
Hierarchical data display:: ipytree
Events management:: ipyevents

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.

Authentication wiki

Link to registration page

Request a password reset

Change password

Services

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

Quick access to BDAP services

JEO-lab

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

JEO-desk

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

JEO-batch

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

JEO-cloud

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

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.

Data browser

Geospatial data browser for Web mapping applications using WMS standard and served by the Big Data Analytics Platform. Licenses for service data

Platform information

Timeline

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 January 2015

Earth Observation and Social Sensing Big Data pilot project

Kick-off of EO&SSBD as a pilot project
1 March 2016

Purchase and installation of the first hardware

JRC Big Data Platform starts its operativity with the first servers installed and configured
1 January 2019

Big Data Analytics project launched as an institutional project

JRC Big Data Platform ends the pilot phase and enters full institutional
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

Numbers

~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:

Publications

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

English

(159 KB - PDF)

Download

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: