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Throughout the tutorials, we will work with a fictional online t-shirt retailer called Haiku T-Shirt, and use their enterprise data to illustrate the basic steps to working with Dataiku. Before jumping into the hands-on portion of the tutorial, you can watch the following video, which walks through the outline of the steps.
This creates the new project and lands you on the project home page. The project home acts as the command center from which you can see the overall status of a project, view recent activity, and collaborate through comments, tags, and a project to-do list. Dataiku DSS lets you connect to a wide variety of data sources.
Click on Upload your files. You are presented with the Upload dialog.
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Click on Add a file, select the orders. Dataiku DSS automatically detects that the file has delimiter-separated values with a comma separator. You can see the data is in a tabular format, with columns features and rows records or observations. Dataiku DSS provides the default dataset name orders in the field on top of the screen.
This creates the new dataset and lands you on the Explore page for the orders dataset. A CSV file like orders. A sheet in an Excel file is also a dataset. More generally, companies and people have systems to store all their data. They can store it in an Excel file, a relational databaseor a distributed storage system if they have larger amounts of data.
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Most of the time, creating a dataset means that you merely inform Dataiku DSS of how it can access the data. These external or source datasets remember the location of the original data.
The data is not copied into DSS. The dataset in DSS is a view of the data in the original system.
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This sample makes it possible to display your dataset, even if the data itself is huge. You control the size of the sample and the sampling method should Dataiku DSS simply grab the first records, which is fast but can result in a biased sample, or should it extract a random sample?
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To see these settings, click Configure sample, which opens a panel on the left. The meaning of a column affects whether Dataiku DSS considers a value to be valid for that column. Below the meaning is a gauge that indicates the proportion of column values that do not seem to match the meaning in red or are completely missing in gray. Click on the meaning and select Text to update it. Drag and drop Count of records as the Y variable.
Note Key concept: recipe A Dataiku DSS recipe is a set of actions to perform on one or more input datasets, resulting in one or more output datasets. A recipe can be visual or code.
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A visual recipe allows a quick and interactive transformation of the input dataset through a number of prepackaged operations available in a visual interface. A code recipe allows a user with coding skills to go beyond visual recipe functionality to take control of the transformation using any supported language SQL, Python, R, etc.
Click on the Actions button at the top right of the orders dataset and choose Prepare. The Prepare Recipe allows you to define a series of steps, or actions, to take on the dataset. The types of steps you can add to a Prepare Recipe are wide-ranging and powerful.
When creating a recipe, you must provide the input dataset the one with the initial columns and values and an output dataset the one with the prepared columns and values. In this example, the output is written on the local filesystem, but the output could be written to a relational database or a distributed filesystem, if the infrastructure exists. Click on the column name, which opens a dropdown, and select Analyze.
The analyze box opens. It allows you to view a quick summary of the data in the column for the sample of data that is being displayed by DSS. The analyze box also gives you the ability to perform various data cleansing actions. Repeat this process for other categories as needed. You just added the first step to your script. You can create this step explicitly in the script, but the Analyze dialog provides a quick and intuitive shortcut to build the step.
Note Key concept: preparation script When using a preparation recipe, you are building a sequence of actions, or steps, that are registered in the script. Each step in the sequence is called a processor and reflects a single data transformation.
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The original data is never modified, but you are visualizing the changes on a sample of your data 10, lines by default. A preparation script has many benefits: First, it is dating basics 101 a Cancel menu on steroids. Second, it is a history of actions that tells you how a dataset is being modified for future reference.
You will learn more about the power of processors in the Tutorial: From Lab to Flow At this point, you will see some values in yellow. In this mode, you can see what the step changes. If you want to see your data as it will appear after processing, click on the Disable dating basics 101 green eye button in the top bar. Dates are notoriously difficult to work with, but DSS makes it very easy. Open the column dropdown and select Parse date.
The Smart Date dialog opens, and shows you the most likely formats for your dates and how the dates would look like once parsed, with some sample values from the dataset. The orders dataset includes the number of t-shirts in each order and the price per t-shirt, but we need to compute the total of each order so that we can analyze the value of each order.
We are going to use a Formula step for this. DSS formulas are a very powerful expression language to perform calculations, manipulate strings, and much more. Click on the column header Choose Delete Recall that the data visible in the recipe is merely a sample, meant to give you immediate visual feedback on the design of your Prepare Script.
With the Prepare Script complete, you must now Run the recipe on the whole input dataset. Click Run in the lower left corner of the page. Dataiku DSS uses its own engine for this recipe runtime, but depending upon your infrastructure and the type of recipe, you can choose where the computation takes place.
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You will also be asked to update the schema. When you initially created this recipe, the dating basics 101 output schema was identical to the input schema because there were no steps in the recipe.
A natural question at this point is which categories of t-shirt are most popular? We can begin to answer this with the cleaned data and a simple bar chart. The resulting bar chart shows that, on average, orders for tennis shirts are larger than orders for other t-shirts.
However, what about overall customer spending? Open the total dropdown and choose Sum as the aggregation.
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This changes our perspective. While each order for tennis shirts is relatively valuable, there are relatively few orders for tennis shirts. Hoodies and male t-shirts are more commonly ordered, and the total value of those orders exceeds that of tennis shirts. From this menu, choose Group in the list of Visual recipes.
The Group Recipe allows you to aggregate the values of some datings basics 101 by the values of one or more keys. The Group Recipe has several steps on the left. The core step is the Group step, where you choose which columns to aggregate and what aggregations you want performed.
In the Output step, you can select to rename the output columns. Click Run to create the new grouped dataset, then Explore the new dataset once run is complete. The Flow is the visual representation of your project pipeline. You can see the Preparation Recipe, represented by a broom, followed by its output dataset, followed by the Group Recipe, followed by its output dataset. Proceed to Tutorial: From Lab to Flow to work on your flow and learn more about the power of preparation scripts and processors!
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