An analysis of the lake

These kettle lakes in Alaska were formed by a retreating glacier. Ice Melting on Lake Balaton In addition to mode of origin, lakes have been named and classified in various other ways according to their thermal stratification, salinity, relative seasonal permanence, degree of outflow, and other factors.

An analysis of the lake

Now you can augment your big data analytics workloads in Azure Data Lake with Azure Analysis Services and provide rich interactive analysis for selected data subsets at the speed of thought!

If you are unfamiliar with Azure Data Lake, check out the various articles at the Azure Data Lake product information site. For starters, Azure Data Lake can process raw data and put it into targeted output files so that Azure Analysis Services can import the data with less overhead.

Moreover, with relatively little effort and a few small changes to a U-SQL script, you can provide multiple targeted data sets to your users, such as a small data set for modelling purposes plus one or more production data sets with the most relevant data.

Of course, you can also take advantage of Azure HDInsight as a highly reliable, distributed and parallel programming framework for analyzing big data. The following diagram illustrates a possible combination of technologies on top of Azure Data Lake Store.

An analysis of the lake

Take a look at the following screenshot, which shows a Data Lake job processing approximately 2. With the raw source data in a Data Lake-accessible An analysis of the lake, the next step is to define the U-SQL scripts to extract the relevant information and write it along with column names to a series of output files.

The following listing shows a general U-SQL pattern that can be used for processing the raw TPC-DS data and putting it into comma-separated values csv files with a header row. For this article, however, the following Microsoft PowerShell script suffices.

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You can use the Data Explorer feature in the Azure Portal to double-check that the desired csv files have been generated successfully, as the following screenshot illustrates. With the modelling data set in place, you can finally switch over to SSDT and create a new Analysis Services Tabular model at the compatibility level.

Make sure you have the latest version of the Microsoft Analysis Services Projects package installed so that you can pick Azure Data Lake Store from the list of available connectors. Currently, the Azure Data Lake Store connector only supports interactive logons, which is an issue for processing the model in an automated way in Azure Analysis Services, as discussed later in this article.

The Azure Data Lake Store connector does not automatically establish an association between the folders or files in the store and the tables in the Tabular model. In other words, you must create each table individually and select the corresponding csv file in Query Editor. This is a minor inconvenience.

It also implies that each table expression specifies the folder path to the desired csv file individually. If you are using a small data set from a modelling folder to create the Tabular model, you would need to modify every table expression during production deployment to point to the desired production data set in another folder.

Fortunately, there is a way to centralize the folder navigation by using a shared expression so that only a single expression requires an update on production deployment.

The following diagram depicts this design. To implement this design in a Tabular model, use the following steps: Create a new Tabular project at the compatibility level. Click Connect and then OK to create the data source object in the Tabular model.

An analysis of the lake

In the Content column, click on the Table link next to the desired folder name such as modelling to navigate to the desired root folder where the csv files reside. Right-click the Table object in the right Queries pane, and click Create Function.

In the No Parameters Found dialog box, click Create. In Advanced Editor, note how the GetCsvFileList function navigates to the modelling folder, enter a whitespace character at the end of the last line to modify the expression, and then click Done.

In the right Queries pane, select the Table object, and then in the left Applied Steps pane, delete the Navigation step, so that Source is the only remaining step.

Verify that the list of csv files is displayed in Query Editor, as in the following screenshot. For each table you want to import: Right-click the existing Table object and click Duplicate.

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After you created all desired tables by using the sequence above, delete the original Table object by right-clicking on it and selecting Delete. And prior to production deployment, it is now a simple matter of updating the shared expression by right-clicking on the Expressions node in Tabular Model Explorer and selecting Edit Expressions, and then changing the folder name in Advanced Editor.

The below screenshot highlights the folder name in the GetCsvFileList expression. And if each table can find its corresponding csv file in the new folder location, deployment and processing can succeed.

Another option is to deploy the model with the Do Not Process deployment option and use a small TOM application in Azure Functions to process the model on a scheduled basis. Processing against the full 1 TB data set with a single csv file per table took about 15 hours to complete.The California Budget & Policy Center is an organization devoted to timely, credible analysis of key policy issues facing California.

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It contains material presented in textbook format and as video presentations. Analysis: Lake Tana: how an invasive weed is threatening its survival.

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