CERN: Where Big Bang Theory meets Big Data Analytics

Screenshot of SQL Plan Baselines with Oracle Enterprise Manager at CERN
Screenshot of SQL Plan Baselines with Oracle Enterprise Manager at CERN

The volume, variety, velocity and veracity of data generated by the LHC experiments at CERN continue to reach unprecedented levels: some 22 petabyte of data this year, after throwing away 99% of what is recorded by the LHC detectors. This phenomenal growth means that not only must we understand Big Data in order to decipher the information that really counts, but we also must understand the opportunities of what we can achieve with Big Data Analytics.

The raw data from the experiments is stored in structured files (using CERN’s ROOT Framework), which are better suited to physics analysis. Transactional relational databases (Oracle 11g with Real Application Clusters) store metadata information that is used to manage that raw data. For metadata residing on the Oracle Database, Oracle TimesTen serves as an in-memory cache database. The raw data is analysed on PROOF (Parallel ROOT Facility) clusters. Hadoop Distributed File System (HDFS), however, is used to store the monitoring data.

Just as in the CERN example, there are some significant trends in Big Data Analytics:

  • Descriptive Analytics, such as standard business reports, dashboards and data visualization, have been widely used for some time, and are the core applications of traditional Business Intelligence. This ad hoc analysis looks at the static past and reveal what has occurred. One recent trend, however, is to include the findings from Predictive Analytics, such as forecasts of sales on the dashboard.
  • Predictive Analytics identify trends, spot weaknesses or determine conditions for making decisions about the future. The methods for Predictive Analytics such as machine learning, predictive modeling, text mining, neural networks and statistical analysis have existed for some time. Software products such as SAS Enterprise Miner have made these methods much easier to use.
  • Discovery Analytics is the ability to analyse new data sources. This creates additional opportunities for insights and is especially important for organizations with massive amounts of various data.
  • Prescriptive Analytics suggests what to do and can identify optimal solutions, often for the allocation of scarce resources. Prescriptive Analytics has been researched at CERN for a long time but is now finding wider use in practice.
  • Semantic Analytics suggests what you are looking for and provides a richer response, bringing some human level into Analytics that we have not necessarily been getting out of raw data streams before.

As these trends bear fruit, new ecosystems and markets are being created for broad cross-enterprise Big Data Analytics. Use cases like the CERN’s LHC experiments provide us with greater insight into how important Big Data Analytics is in the scientific community as well as to businesses.

CERN: The world’s first website went online 20 years ago today

CERN website dispayed in Line Mode Browser
CERN website dispayed in Line Mode Browser

On this day 20 years ago the world’s first website went live. The website, created by Tim Berners-Lee at CERN, was a basic text page with hyperlinks and went live on August 6, 1991.

The website was hosted on Berners-Lees’ NeXT computer, the first web server ever, which had a note taped to the front that said: “This machine is a server. DO NOT POWER DOWN”.

NeXT computer used as first World Wide Web server
NeXT computer used as first World Wide Web server

Today this computer is displayed in the CERN Computer Center, which is just located next to my office.

[Update 30 Apr 2013]: CERN is bringing the very first website back to life at its original URL. If you’d like to see it, point your browser to:

Data Science: Enabling Research at CERN with Big Data

Wow, time flies. One year has passed since I started to work at CERN as a data scientist. CERN, surrounded by snow-capped mountains and Lake Geneva, is known for its particle accelerator Large Hadron Collider (LHC) and its adventure in search of the Higgs boson. Underneath the research there is an tremendous amount of data that are analysed by data scientists.

Filters, known as High Level Triggers, reduce the flow of data from a petabyte (PB) a second to a gigabyte per second, which is then transferred from the detectors to the LHC Computing Grid. Once there, the data is stored on about 50PB of tape storage and 20PB of disk storage. The disks are managed as a cloud service (Hadoop), on which up to two millions of tasks are performed every day.

High Level Trigger data flow
High Level Trigger data flow, as applied in the ALICE experiment

CERN relies on software engineers and data scientists to streamline the management and operation of its particle accelerator. It is crucial for research to allow real-time analysis. Data extractions need to remain scalable and predictive. Machine learning is applied to identify new correlations between variables (LHC data and external data) that were not previously connected.

So what is coming up next? Scalability remains a very important area, as the CERN’s data will continue to grow exponentially. However, the role of data scientists goes much further. We need to transfer knowledge throughout the organisation and enable a data-driven culture. In addition, we need to evaluate and incorporate new innovative technologies for data analysis that are appropriate for our use cases.

MS SQL Server: ETL mit Data Transformation Services

Screenshot von SQL Server Enterprise Manager mit SAP MaxDB
Screenshot von SQL Server Enterprise Manager mit SAP MaxDB

Kürzlich stand ich vor der Herausforderung einen Datenbestand von einem Datenbanksystem (SAP MaxDB) in ein anderes (Microsoft SQL Server) zu überführen. Das Unterfangen war manuell jedoch kaum zu realisieren, da die Datenbank mehrere hundert Tabellen und unzählige Datensätze umfasst.

Abhilfe schaffte der Microsoft SQL Server Enterprise Manager. Dort finden sich die Data Transformation Services wieder, Hilfsprogramme, die es erlaubt, ETL-Prozesse (Extract, Transform, Load) beim Import in oder Export aus einer Datenbank zu automatisieren. Dabei werden verschiedene Datenbanksysteme unterstützt, sofern diese über eine ODBC– oder eine OLE DB-Schnittstelle verfügen, was auch bei SAP MaxDB der Fall ist.

Konkret bestehen die Data Transformation Services (DTS) aus folgenden Komponenten:

  • DTS Import/Export Wizard: Assistenten, die es erlauben Daten von oder zu einem MS SQL Server zu übertragen, sowie Map Transformations ermöglichen.
  • DTS Designer: Ermöglicht das erstellen von komplexen ETL-Workflows einschließlich event-basierter Logik.
  • DTS Run Utility: Planung und Ausführung von DTS-Packages; auch via Kommandozeile möglich.
  • DTS Query Designer: Eine GUI für das Erstellen von SQL-Abfragen für DTS.