So excited that Tableau has joined the Amazon Web Services Marketplace with an option to quickly deploy Tableau Server in the cloud.
What this means to our Tableau customers in comparison to the traditional manual installations are:
- AWS pre-config deployment for Tableau Server can save a lot time & money on the configuration and service provision fronts. (e.g. just imagine how much time can be saved when it comes to the configuration the tableau server public IP configurations...)
- AWS pre-config installation for Tableau Server can set up the Demo/Production environment faster if the underlying data sources are already made available in the AWS Cloud Environment (such as SSAS cubes hosted on the same/separated AWS instances)
- Lastly, the security configurations (e.g. ports/roles) will be easily taken care by the AWS pre-config set-up, rather than the traditional approach to go through the Tableau Server Admin console to manually configure those individual setting, then re-booting the server repeatedly in order to make sure that all the setting has been made effectively.
So excited about this ground-breaking SQL Server 2016 public preview release in the coming months. In summary, the top capabilities for the release include:
- Always Encrypted - a new capability that protects data at rest and in motion,
- Stretch Database - new technology that lets you dynamically stretch your warm and cold transactional data to Microsoft Azure,
- Enhancements Real-time Operational Analytics & In-Memory OLTP - leading in-memory technologies for real-time analytics on top of breakthrough transactional performance.
- Built-in Advanced Analytics, PolyBase and Mobile BI with R integration
Also additional capabilities in SQL Server 2016 include:
- Additional security enhancements for Row-level Security and Dynamic Data Masking to round out our security investments with Always Encrypted.
- Improvements to AlwaysOn for more robust availability and disaster recovery with multiple synchronous replicas and secondary load balancing.
- Native JSON support to offer better performance and support for your many types of your data.
- SQL Server Enterprise Information Management (EIM) tools and Analysis Services get an upgrade in performance, usability and scalability.
- Faster hybrid backups, high availability and disaster recovery scenarios to backup and restore your on-premises databases to Azure and place your SQL Server AlwaysOn secondaries in Azure.
The further details for the SQL Server 2016 public preview information can be found via http://www.microsoft.com/en-us/server-cloud/products/sql-server-2016/
P.S. the latest MS Power BI preview will be also fully released this summer as well along with much more appealing visualization and sophisticated data capabilities.
As you may be aware, the major version releases of Tableau Desktop 9.0 and Server 9.0 are on the way for public downloading in the coming days.
As for this new release of the Tableau Server 9.0, you will get the following major upgrades:
- New Intuitive User Interface
- Better Security Permissioning
- Extended Command Line and REST API client integration
- High Availability by new Cluster Management
- Performance Enhancements by Server Processor Cores & Server Caching
The future details can be viewed via http://interworks.co.uk/blog/whats-new-tableau-server-9-0/
With this new release of the Tableau Desktop 9.0, the new stellar key features are:
- More and more data connections (Yes, it can easily connect to IBM SPSS and SAS data files directly at your fingertip now!)
- More fluid Web 3.0 User Interface
- New stunning Mapping graphics (Even outshine the classic Tableau maps)
- New shining responsive Tooltips
- More sophisticated In-line Editing capabilities along with much more powerful Calculated Field Editor.
Please feel free to view the details via
Note: From my personal point of view here, PowerBI and Tableau seem to be serving for quite different purposes of tools to the business users/current markets as PowerBI is more powerful and flexible for the reporting capabilities whereas Tableau is just more of a quick graphical dashboard-ing tool for the data discovery purpose.
Have been tasked to put together a quick write-up about Tableau and R integration for the IM Dashboarding solution based upon the Tableau latest 8.2 releases.
Please see my research details as follows and more than welcome for any feedback from all of you:
As many people might know that Tableau just released the latest 8.2 version to the global market with a bunch of new exciting features (http://www.tableausoftware.com/new-features/8.2), do you also know that the Tableau also supports R Integration since version 8.1 on the Desktop & Server tools (Not on Tableau Reader yet).
Benefits for using R
As an open-source almost 2.5 million user base analytics platform, R provides a wide variety of analytic analysis goodies here – it includes that statistical tests, linear and nonlinear modeling, time-series analysis, classification and clustering.
There are many benefits by leveraging R into the Tableau toolkits and please see the key points as follows:
· It’s free and open-source backed by the community and millions professionals.
· The free functions and rich features can be created all the time by the domain experts and other industrial professionals and the quality of the libraries will be assured by the community and experts.
· Enrich Tableau with a growing collection of statistical analysis and data mining libraries to help them gain deeper insights from their data.
Integration with R
Getting R set up with Tableau Desktop/Server requires R and Rserve installed and configured.
· R can be simply grabbed from http://cran.us.r-project.org/
· Rserve is a package created from Tableau communities in order to connect to the Tableau applications
· Rserve required to be installed on the command line and got connected to the Tableau via “Rserve Connection” configuration Screen or the tabadmin command line on the Tableau Server environment
· R functions and models can now be used in Tableau by creating new calculated fields that dynamically invoke the R engine and pass values to R.
Common Question about Tableau + R Integration
Q: Can we use precompiled packages, models, and other things with Tableau and R?
A: Yes. The general rule is, if you can do it in R, you can easily integrate it with Tableau. This includes any statistical packages, parallel computing packages, models and libraries, whether they are standard within R or if you create them independently. This also includes commercialized versions of R, including Revolution Analytics. You can also return data frames from R one column at a time.
Q: When integrating Tableau and R, what is the best practice for debugging R scripts or discovering errors?
A: There are two ways to do this. The first is to use the 'write.csv' command within the calculated field that calls an R script. The other, is to use the debug version of the standalone executable of Rserve (Rserve_d.exe) which will print out any code that R is performing as Tableau calls the R scripts.
Q: Can you use R to reshape data?
A: Yes, see this example (http://www.tableausoftware.com/solutions/r-integration) where R and multidimensional scaling are used to reshape 1600+ columns into Tableau.
Q: Can Tableau pass data from a relational database to R?
A: Yes, Tableau can pass data from any source and run R scripts on that data, whether a flat-file, relational database, cube, or an unstructured data store.
Q: Can you dynamically pass various levels of drill-down dimensions to an R function?
A: Yes, and very easily too. In Tableau, R scripts are run in table calculations and therefore can be run against various dimensions. Simply change the aggregation level of the desired dimension and compute the table calculation accordingly.
Q: How do you run a function with a mix of variable types?
A: Tableau can send R mixed types of data. In SCRIPT_X, the X represents the variable type that is returned to Tableau. In most cases, the one column returned to Tableau will contain a single variable type. If there are mixed types (e.g. mixed number and text values), it can be returned as a string using SCRIPT_STR.
Q: What is the best practice for R Integration with Tableau that R models can be reused within the same session?
A: See this getting started whitepaper(http://www.tableausoftware.com/learn/whitepapers/using-r-and-tableau) about Rserve. It provides more details about saved sessions, and the difference between Windows and Linux installations of Rserve in private vs. shared environments.
Q: Does Tableau Reader integrate with R?
A : At this time, you will need Tableau Desktop or Tableau Server to view a Tableau workbook with R scripts.
Lastly, it’s also worth pointing out here that Tableau by default comes with its own built-in Analytics Models and capabilities, so please feel free to check out the following resources if you would like to explore:
Hope you would find it useful.