Business Intelligence: Erkenntnisse aus der Praxis für erfolgreiche DWH-/BI-Projekte (German Edition)
Hier werden individuelle Fragestellungen rund um die Ausrichtung des Unternehmens beantwortet. Kurz nach dem Release von Tableau 8. Im Ganzen umfassen diese mehr als neue Features. Auch in der Tableau 8. Was ist neu? Mit dem Release der neuen Version ist man direkt an die Struktur der Tableau Software gegangen und hat dort viele Optimierungen vorgenommen.
Ein wichtiges Augenmerk wurde auf Performance und Komprimierung gelegt. Tableau 8 erweitert diese Funktion: z. Mit Tableau 8. Der Tableau Server 8.
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PDW v2 Big Data. The time frame can be real time, near time, a few hours or maybe even days. This depends on the business requirement. The size of data may get bigger than expected because you need additional data sources for example external data from market places for your analysis. Remember that storing a huge amount of data is not the big problem.
But retrieving the data and doing analysis on this data is much more challenging. But what are complex analytical computations? In SQL we can do a lot of computations by aggregating detailed values sum, average, min, max etc. And for many of the typical business performance indicators, this works quite well. But what about the following tasks:.
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Decomposing time series can helpful to analyze periodicity and trends of sales data for example. This could be important for calculating the effect of promotions or to understand seasonal effects. And this is just one example. Some people even say, that this is where Business Intelligence starts. Everything else is just an analysis of the past which is also important, but there is so much more to find.
The current discussion about data scientists clearly shows the rising demand for getting more out of your data. And to be honest, having a data scientist working just with a tool like Excel is like having Dr. So, there are a lot of calculations that go far beyond the capabilities of traditional SQL.
Therefore, we usually need to load the data from our data warehouse into some kind of analytical or statistical tool which is specialized such calculations. The results can then be transferred back into the relational database tables. As the focus of such tools differs from the focus of databases, these tools are usually separated from the database but offer interfaces for example ODBC or flat file to load data. R is open source and can easily be extended using packages libraries.
Today, a huge amount of such packages exists for all kinds of different tasks. However, when it comes to Big Data, the process of unloading all the required data can be very time consuming. This would be the perfect match. For doing so, the following two options are most promising:. PDW v2 offers a seamless integration with Hadoop using Polybase. This makes it easy and fast to export data on a Hadoop infrastructure. Research level analytics can then be performed on the Hadoop cluster.
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Yes, but in this scenario, each infrastructure is used in an optimal way:. Preparing the data for analytics can be a complex and challenging process. Usually data from multiple tables needs to be joined and filtered. Using SQL is the best choice for this task. For example, for preparing call center data for a mining model, it may be necessary to create variables single row of data that contain the number of complaints per week over the last weeks.
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This can then be used to build a decision tree. For the decision tree we need to perform a feature selection at each node of the tree.
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This involves statistical functions and correlation which reach far beyond SQL. Using the analytical environment is the best choice for such advanced calculations. The resulting decision tree rules, lift chart, support probabilities etc. Another approach is to operate the analytical engine on the same platform and on the same data as the MPP database system. However, in other MPP environments, this approach is not uncommon. Specialized toolsets like R are the the best solution.
A closer integration is necessary. Using Hadoop or In-Database Analytics are promising approaches for this scenario. So you need a tool to analyze unknown data and search for patterns or other relevant information. This is a key strength of Tableau — most moves while ad-hoc queries are dragged and dropped to analyze data are represented visually, so you get a fast understanding and insight of the data.
The demo scenario is based on a web log file that is imported to HDInsight and can be queried via Hive. News from June 26, For Microsoft clients like Excel they are used the following way:. Only via Tableau data extracts. It works but I guess in the future there will be a specialized Tableau data connector for HDInsight like there is for Cloudera or Hortonworks today. Power Query.
A powerful function in Power Query is to unpivot a given data set which means to rotate data in columns to rows.
This is useful for a lot of statistical data sets that you will find on the web because those data sets usually have the time for example the year on the columns. In order to process the data, you need to unpivot it first. In order to transform the columns into rows, I select all columns with years and choose unpivot from the context menu:. This was quite easy. Now, what happens if a new columns is added to my source table. By clicking Refresh in the context menu of the Excel table resulting from my Power Query, the query is executed again.
The result looks like this:. As you can see, the year is not included in the unpivot operation but became an additional column.