Recently Tableau released an exciting new feature: R integration via RServe. Tableau with R seems to bring my data science toolbox to the next level! In this tutorial I’m going to walk you through the installation and connecting Tableau with RServe. I will also give you an example of calling an R function with a parameter from Tableau to visualize the results in Tableau.
1. Install and start R and RServe
You can download base R from r-project.org. Next, invoke R from the terminal to install and run the RServe package:
[Update 26 Jun 2016]: Tableau 8.1 screenshots were updated with Tableau 10.0 (Beta) screenshots due to my upcoming Advanced Analytics session at TC16, which is going to reference back to this blog post.
There are four key issues to overcome if you want to tame Big Data: volume (quantity of data), variety (different forms of data), velocity (how fast the data is generated and processed) and veracity (variation in quality of data). You have to be able to deal with lots and lots, of all kinds of data, moving really quickly.
That is why Big Data Analytics has a huge impact on how we plan CERN’s overall technology strategy as well as specific strategies for High-Energy Physics analysis. We want to profit from our data investment and extract the knowledge. This has to be done in a proactive, predictive and intelligent way.
The following presentation shows you how we use Big Data Analytics to improve the operation of the Large Hardron Collider.
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.
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.
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.
just realized that today is my first year anniversary working for CERN. Thanks for all the memories made within the year!
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