Product Overview

Dataprep by Trifactaenables you to explore, combine, and transform diverse datasets for downstream analysis.

Within an enterprise, data required for key decisions typically resides in various silos. It comes in different formats, featuring different types. It is often inconsistent. It may require refactoring in some form for different audiences. All of this work must be done before you can begin extracting information valuable to the organization.

Data preparation (or data wrangling) has been a constant challenge for decades, and that challenge has only amplified as data volumes have exploded.

Some organizations have pushed these cleansing efforts onto IT, which may take weeks to come up with a custom, scripted solution that requires inevitable back-and-forth between coder and analyst. As formats, feeds, and requirements change, these rigid solutions require frequent updating, which cannot be done by the people who really know the data. Instead of producing insights, analysts are filing requests and waiting for weeks for solutions.

The Alteryx solution delivers the tools to wrangle data to the people who understand the meaning of the data. With Designer Cloud, analysts have the means to apply their expertise to the preparation of the data, in a way that is faster and more productive.

Designer Cloud helps to do the following:

Featuring a leading-edge interface, powerful machine intelligence, and advanced distributed processing, Designer Cloud renders the time-consuming, complex, and error-prone process of preparing datasets of any volume into a point-and-click exercise. What took six weeks in the IT lab can be done in less than two hours at the analyst's desk. Predictive Transformation

Humans are pretty good at identifying singular problems; software is better at solving them at scale. The platform leverages this concept through predictive transformation.

You see an issue in your sampled data. Whether it is part of a value, multiple values in a column, or the entire column itself, you select it. Immediately, the platform surfaces a set of suggestions for you. How would you like to transform this data?

Figure: Select data elements to receive context-specific suggestions on transformations to apply to the element or to patterns that describe it. Preview the results before you add the change.

Through an innovative interface and leading-edge machine-learning techniques, Designer Cloud surfaces potential actions on the data in an intuitive manner for rapid triage. Select something in the data grid, and the platform provides a set of context-specific recommendations of actions to take on the selected data and similar matching items. Click the recommendation, and your data is transformed. Modify the particulars of the transformation to get it right. No coding required.

For more information, see Overview of Predictive Transformation.

The scale and complexity of these transformations can quickly overwhelm even the most powerful of machines. Designer Cloud utilizes a number of techniques to deliver high performance at scale.

Figure: Platform interactions and data movements

Basic Steps:

Through a standards-compliant web browser, users interact with the platform through the user interface to complete the following tasks:

The above steps create a single sequence of steps from a single dataset. Datasets and sequences (recipes) can be combined or chained together to address much more complicated data wrangling requirements.

In Designer Cloud, the primary object that you create is the recipe. A recipe is a sequence of transformation steps that you create to transform your source dataset. When you select suggestions, choose options from the handy toolbar, or select values from a data histogram, you begin building new steps in your recipe. After selecting, you can modify them through the Transform Builder, a context panel where your configured transformation can be modified and the changes previewed before saving them.

When you finish your recipe, you run a job to generate results. A job executes your set of recipe steps on the source data, without modifying the source, for delivery to a specified output, which defines location, format, compression and other settings.

Datasets, recipes, and outputs can be grouped together into objects called flows. A flow is a unit of organization in the platform.

Depending on your product, flows can be shared between users, scheduled for automated execution, and exported and imported into the platform. In this manner, you can build and test your recipes, chain together sets of datasets and recipes in a flow, share your work with others, and operationalize your production datasets for automated execution.

In addition to the above, the following key features simplify the data prep process and bring enterprise-grade tools for managing your production wrangling efforts.

Visual Profiling

For individual columns in your dataset, data histograms and data quality information immediately identify potential issues with the column. Select from these color-coded bars, and specific suggestions for transformations are surfaced for you. When you make a selection, you can optionally choose to display only the rows or columns affected by the change.

Figure: Click the red bar to select all mismatched values in the column. Show only the affected rows. Review suggestions for how to fix these specific values.

Tip

Dig into the column details to explore distributions of values based on the column's data type.

As part of your transformation job, you can optionally generate a visual profile of your dataset, which allows you to quickly identify areas for additional iteration.

For more information, see Overview of Visual Profiling.