Bime release 1.90: Data Security, Delegated Aggregations, Filtered Attributes, Advanced Color Coding and more! [RELEASE]

Bime 1.90 is out and it's a massive release in terms of new features. Here is a quick overview of all the data goodness you'll find in this release.

Data Security through named viewers and groups

With the addition of data filter prompts in release 1.85, named viewers and groups are used to secure published dashboards. Now, Bime goes one step further by allowing you to secure the data that those groups can see in your dashboards: Enter data security.

Basically, data security is used to permit or deny access to members of an attribute (read the column in the datasource) and any data associated with those members.

For example, consider the example of a connection containing a customer state attribute and a measure called Profit.

Let's say you want to secure this data and give access to the regional sales manager group of California only. This is what the data will look like for the exact same query when a member of this group looks at it:

To enable this feature, in the pivot table, click on the attribute you want to secure, and click on "secure":

Then enter the configuration of the data security:

Then save and that's it! Your attribute is now secured and every person belonging to the "Regional Sales" group will only see "California" data in the dashboards that use it.

Delegated Aggregations For RDBMS connections

Bime is now able to delegate the heavy work of answering a query to the relational database engines. This is a huge improvement as we were limited before V1.90 by the amount of data we could put in the memory on the client side. Now Bime is more or less limited by the capacity of your database, as it will do a translation between what the user asks and the language of their database (i.e., Bime generates SQL queries).

The "Delegated" mode is particularly suitable if you have a relational database with a medium to large data volume (e.g.: more than 1 million lines).

The only trade-off is that you can't use the "Déjà Vu" technology to take snapshot of your data. Of course, you can still put query results in Déjà Vu for this type of connection.

To enable this mode you just have to check the little check box in connection builder:

Flash player 10.1 and AIR 2.0

We upgraded to the very last runtime of Adobe. Why? Mainly for speed and memory efficiency. Consequently, you'll notice an overall boost in performance with this release.

We had a long internal talk about the pros and cons of upgrading the runtime, as it requires a more painful upgrade process for Bime. In the end, we agreed that the advantages by far outdo the only drawback of the installation.

This page describes the upgrade process and we are here to help!

Filtered Calculated Attributes

Filtered calculated attributes are another weapon in your calculated formulas arsenal. The aim of this one is to restrict the number of values contained in one attribute (i.e.: a column of your data source). With it you can request things like "give me the top 10 products for Sum of Profit" or "give me the bottom 10 products for Sum of Profit", or both.

Click on one of black arrows in the dimensions explorer and choose "create a filtered attribute".

Then just choose a measure and an attribute from the range you want to use and you are done.

Now let's talk a bit about when you should use filtered attributes. So you already have the ability to top elements in post processing filters:

There are 2 advantages of using filtered attributes:

First, performance. As filtered attributes are executed in our engine on the raw data, it performes much better than filtering out the result in post processing. Another performance advantage is that it is only calculated once and reused in other queries.

Second, is expressivity. Indeed you can create filtered attributes from other calculated attributes, so you are able to express really complex logic in the way you filter elements.

Advanced Color Coding

4 styles of color coding can now be applied on top of your data.

Default Style 1 is a gradient style that will show the value of the result on a sliding color scale across the dataset. The color coding below shows you how customer states perform against each other in terms of shipping cost. Also, please note that to switch to a different color style, you just have to right click on the color legend of the chart.

Style 2 is not a gradient style. You specify a value above which the color will be red. Everything below this value will be green. In the example below, you see all the customer states that have shipping costs above $3000 in red.

Style 3 is a divergence gradient style. You see a strong red when the value goes far above the median and a strong green when the value goes far below the median of the result. Anything close to the median is shown by a sliding scale of grey.

Style 4 is a divergence non gradient style. All the states in grey are as close to the median as you want, and all the states in red and green are at the extremes of the dataset.

We hope you'll like these new features. Happy data analysis!