Experience with upgrading Tableau

Before upgrading the server, we had to make sure our server met the pre requisites for the upgrade.

As Automatic Login was going to be configured, previously a domain service account to the IT department needed to be requested (Non-expiring password and administrator rights granted) and the communication between the Tableau server and the Windows AD server needed to be established (Tested via ping and telnet commands in the CMD window, in our case we had to request to open ports on the firewall).

Once the pre-requisites were checked and made sure the server met all of them, we proceeded with the installation, as we needed to upgrade from version 8.0 (32 bits) to 8.1.7 (64 bits) it was necessary to uninstall the current software and install the new version after a previous back-up of the current the data. The installation went smoothly, no errors or warnings until we realized that the current data was not present and in the application only remained sample data, even though in the Tableau upgrade guide it is written that the data remains in the server and it is picked up automatically by the new version.

We needed to recover the data, therefore we proceeded to restore the backup saved before the installation and after the restore of the data was finished, we encountered another issue, the Administrator account was not working, so we had no access to the system and a few scary minutes went by until we found the tabadmin reset command to set a new password for the administrator account.

More info on Tabadmin reset command: http://kb.tableausoftware.com/articles/howto/resetting-the-admin-user-account

We finally recovered the data and Tableau was up and running, then it was time to configure Automatic Login, the way Tableau works regarding Automatic login is importing all users from the Windows AD group selected to allow access, we contacted the responsible for Windows AD in order to obtain a group suitable for us (In our case it is a big customer with many offices in many countries, we only needed users from Holland), after the Windows AD responsible provided the name of the group we proceeded with the synchronization, it took around 20 minutes to synchronize 3000+ users.

Once the users were synchronized, we tested and the automatic login was working fine but we could not find a way to link the existing local users to the new Windows AD users, due to this we had to tweak the user groups accordingly to the security already configured.

We also installed the PostgreSQL driver in order to allow access to the Tableau system database in order to create data governance reports, by default Tableau only provides access to a limited number of tables (Only tables which names start with underscore), but doing a little research we found a-way to provide full access to all tables. See below details for how to connect to the database and how to provide full access to all tables.

While doing the post installation testing, we found out that the Tableau maps were not being displayed correctly; this is due to the fact that we changed the account the Tableau service was running on, from local system account to a service account. At the moment of doing this, the proxy settings were not present in the session of the service account, configuring the proxy settings and restarting the Tableau server fixed the issue. More info on Maps not being displayed on: http://kb.tableausoftware.com/articles/issue/maps-do-not-display?keywords=map%20server

Summary of lessons learned

As a summary of this experience I can say that the lessons learned were the following:

• Always create a backup outside of the server before installing or modifying any application
• After configuring a new account to run the service, check all the settings configured in the local account session and apply them in the new session
• User groups administration and security for Tableau automatic login should be handle at the Windows AD group level and not in Tableau as the synchronization brings all users in the group.

“This certification reinforces Clariba’s continued leadership position that we have carved out for ourselves in the SAP Business Intelligence and Big Data domains,” says Marc Haberland, managing director, Clariba.

The SAP Recognized Expertise Certification in SAP HANA and Business Intelligence is valid for two years, and complements Clariba’s other certifications including its Partner Center of Expertise (PCoE) Certification.

Certification is only awarded to SAP Partners with proven competencies in selected solutions or industries. This new certification means that Clariba will be listed in the Directory for SAP Partners with Recognized Expertise and SAP will endorse Clariba for applicable tenders/RFPs.

Stay posted!

Data mining, as per definition, is a discipline whose main aim is to discover patterns and most importantly, to predict and gain more knowledge on your data. This is done by combining different methods and approaches from artificial intelligence and statistics disciplines. One of the most common problems is how to build accurate and optimal classifier that given raw data helps us to build a model to classify future cases. There are many algorithms and methods available and we will refer in this blog article to the C4.5 which is available in the SAP Predictive Analytics tool. SAP Predictive analytics (SAP Infinite Insight) provides data mining capabilities that help many companies to anticipate customer behaviors and demands. SAP Predictive Analytics is very easy to use and very powerful, it can be downloaded here.

The C4.5 algorithm goal is to make decision trees based on datasets. Its first version came from ID3 algorithm which was developed by Ross Quinlan.  Although C4.5 is highly popular, there are many other options like J48 and the extended C5.0.

How does it work?

By taking a group of examples, C4.5 builds the simplest decision tree (not necessarily binary) in order to classify new cases. It establishes the most important attribute as a base and adds new nodes by evaluating the importance of the following attributes.

In the example above, we can classify an object according to its attributes and get whether is a class A or a class B object. The leaf of the tree shows the result of the analysis.

Going into more detail, getting the shortest decision tree is a problem known in Computer Science as NP-Complete. This means basically that there is no way to find the most optimal solution for this problem in a reasonable time. And how is this possible? The order of the nodes is going to affect the size of the decision tree and this increases the complexity to a very high level. However, C4.5 uses a greedy approach to get a solution which works reasonably well. This solution relies on the concept of Information Entropy:

Note: The entropy measures the lack of homogeneity of an examples set

That is, the algorithm builds the tree by selecting the attribute with the smallest entropy possible. There are many references about the C4.5 operation and performance, but this may be something to cover in another post.

How can SAP help?

Data-mining capabilities are a must for many companies. They provide an insight for the future and analyst can anticipate events or behaviors that will improve the whole decision making procedure. On one hand, SAP Predictive analytics tool will provide the user interface to build predictive models and apply data mining procedures to forecast and analyze. It is built on top of SAP Lumira, therefore is graphic-based and very easy-to-use.

On the other hand, SAP HANA provides an extremely fast and powerful in-memory database. It would make sense that we can actually take advantage of this horse power and run the algorithm in HANA.

• HANA’s Predictive Analysis Library (PAL) defines functions that can be used to perform predictive analysis algorithms.

Example

For this analysis, we will use the following raw data. The below table represents a list of customers from an insurance company and the last column defines if it is a fraudulent customer or not.

We would like to build a decision tree based on this data, that is going to help us to classify a customer to see if it’s probably fraudulent or not.

Let’s assume we have a model in HANA with this data available (check how to build a model find SAP reference guide  http://www.saphana.com/docs/DOC-1074) in an Attribute View Called “PRED_SAMPLE”.  So First we open SAP PA and click New Document and select Connect to SAP HANA.

After introducing host name and credentials, follow the process and select the view which contains the data to be analyzed and click on create.

 Note: C4.5 is only available in HANA PAL and not in PA itself, so it will only be available when we are working with data in HANA.

After data has been loaded, we click on Predict to access the Predictive view. In the right panel we can select the available predictive algorithms and other procedures to use. HANA C4.5 should be available if we have established the connection successfully. We just have to double click and a node will be added in the composer.

We don’t need any data transformation since we assume the data has been prepared before.

Next step is to configure the execution, so we double click on the HANA C4.5 icon in the workflow and we will access the configuration panel. Once opened, in the Column Selection we select as Features the columns which are going to be the attributes of the decision tree:

• Policy
• Age
• Nationality
• Occupation

The value which is going to be the target of the classification is the column “FRAUD” (which is actually going to be the leaf of the tree). Therefore we specify it in the “Target Variable” field and click “Done”:

Now we simply click on “Run” and we will be redirected to the results view where we can get the decision tree design. Note that all leafs are providing a result for the analysis, while the nodes provide the probability of the result by going that way:

Some considerations

->  Check that you have HANA PAL installed on your HANA server

AFL (Application function library) includes PAL. We can open a SQL console and run the following command to check:

SELECT * FROM “SYS”.”AFL_FUNCTIONS” WHERE SCHEMA_NAME = ‘_SYS_AFL’ AND AREA_NAME = ‘AFLPAL’;

-> Check if the user used to log in HANA through PA has the role AFL_SYS_AFL_AFLPAL_EXECUTE granted. Same way, check in the Object Privileges tab if the procedures AFL_WRAPPER_GENERATOR and AFL_WRAPPER_ERASER are granted with Execute:

Example above of R with RGui frontend plotting a 6 million rows dataset.

Acquiring R expressiveness in our skill set is now a strategic achievement in the BI sector, so we have composed a list of resources available on the Internet for auto-training in R skills:

Try it online!

Learn R in minutes

Introduction to R Course​

Quick-R (how to’s)

SAP HANA R Integration Guide

Data Analysis and Statistical Inference Course

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