Comparing Alteryx and Dataiku (2023)

This article explores the similarities and differences between Alteryx and Dataiku, and highlights Alteryx's unique features that make it the analytics platform of choice for data-centric organizations.

Here's a quick breakdown of the differences between Alteryx and Dataiku.

interface:

Alteryx: Uses an integrated user interface with a tool palette, workspace, tool configuration window, results window, and search bar.

Dataiku: It consists of three main windows: Stream, Datasets and Recipes.

Together:

Alteryx: Alteryx offers several options for merging disparate data, including standard join, multi-join, and fuzzy match capabilities. By configuring a join tool, users can rename and delete fields in their data and revise any mismatched records in the results pane.

Dataiku: Dataiku uses the join tool's visual recipe. Unlike Alteryx, this tool doesn't come with a built-in feature to change fields. A user would have to open the newly created record to make further changes to a column or field.

Data/profile quality check:

Alteryx: Alteryx's navigation tool allows the user to view basic summary statistics about their data, e.g. B. Field length, data frequency, null values, and records with leading and trailing spaces.

Dataiku: Dataiku displays summary statistics in the dataset window. A red identifier is used to indicate values ​​that do not correspond to the derived meaning of the column.

Formulas and functions:

Alteryx: Alteryx allows users to modify existing columns and create new columns using the formula tool. A single formula tool can contain multiple formulas or expressions for different columns. Formulas are processed in the order they were written, so new or changed columns can be referenced in the same formula tool. Users can also save their own frequently used expressions for future use or use pre-built frequently used expressions provided by Alteryx such as contains(), replace() and if/else to name a few.

Dataiku: Formulas and functions are part of the visual recipe for data preparation in Dataiku. Each function is written as a new step in the visual data preparation recipe.

Drag and drop:

Alteryx: Alteryx gives users the flexibility to drag and drop tools from the tool palette or search bar onto the screen and move or rearrange them as needed. Users can automatically align or evenly space tool groups for additional screen organization.

Dataiku: Dataiku visual recipes are placed in the stream window by the software. There is limited flexibility in scheduling prescriptions.

Learning paths and community:

Alteryx: Alteryx offers several ways to learn and become an analytics pro. They offer free materials and resources like interactive lessons, starter kits, and a solid community forum to ask questions and learn from other Alteryx users. Alteryx also offers free certifications for people at all different stages of their analytics journey: Fundamental Concepts, Core Tools, Advanced Tools, Expert, and Predictive Master.

Dataiku: Dataiku offers learning paths similar to Core Learning, ML Practitioner, Advanced Designer and Developer Path.

More about Alteryx…….

Alteryx is a software solution focused on analytics and automation. In fact, many Alteryx users are moving from Excel to Alteryx because they can automate a process of analyzing, cleaning, transforming, and enriching their data that, unlike Excel, is scalable and repeatable.

Alteryx allows users to read and connect data from many different sources, including flat files like Excel and CSV, databases like SQL Server and PostgreSQL, third-party integrations like Salesforce and Google Analytics, and cloud storage like Snowflake or Azure. After the data is brought into Alteryx, it can be combined using joins, unions, attachments, or a combination of these tools.

When the data is ready for cleaning and manipulation, Alteryx provides a variety of tools to facilitate transformations and aggregations, such as formulas, filters, pivot, and summary. Alteryx also provides input and output anchors in each tool so you can see changes in your data as they occur at each stage of a workflow. This functionality makes testing and analyzing different transformations easy and informative.

When users are ready to share their data or advanced analytics outside of Alteryx, they can leverage the same functionality to read data with additional connectors like Marketo, PowerBI, and Tableau. Alteryx offers a comprehensive and interoperable analytics solution for seamless integration with popular BI tools like Tableau and PowerBI. On the other hand, Dataiku software tries to replace these integrations with its software.

By automating these formerly manual processes with Alteryx, analysts can focus on strategic priorities instead of basic data cleaning and pre-processing. In turn, organizations can improve their advanced analytics practice in ways previously not possible due to resource constraints.

Alteryx software operates on a no-code framework, allowing non-technical users to build ETL pipelines and analytics applications without writing a line of code. In a world where organizations are increasingly data and insight-driven, software like Alteryx is critical to breaking down barriers between technical and business users. Tasks that once required the expertise of a software engineer can now be automated in a workflow created by an analyst.

Below is a detailed comparison of Alteryx and Dataiku:

Let us begin!

Alteryx x Dataiku

interface

The Dataiku interface consists of three main parts: Flow, Datasets, and Recipes. Flow shows a user's current workflow. Datasets shows a list of all used and unused data sources imported with the workflow. Recipes store all transformations for existing datasets. Dataiku offers users the ability to start a project in a central space that multiple users can access to collaborate. With Dataiku's visual recipe, you can clean, normalize, enrich, and aggregate data without writing a single line of code. On the other hand, Dataiku offers code recipes to run custom code on demand. In the workflow, the Action tab allows users to export, publish, or search the record and grant user access to recipes. The details icon displays information about a workflow, such as B. Date created, date last modified and user permissions. The discussion icon is visible to all users with access to the project and sends notifications about important updates. The lab icon shows all visual analysis tools and code notebooks. Finally, the timeline icon shows workflow changes over time, from creation to last modification.

Comparing Alteryx and Dataiku (1)

Alteryx Designer consists of four main components: tool palette, canvas, configuration window, and result window. The tool palette contains all the tools that a user can use in his workflow to read, clean, merge, aggregate, enrich and generate his data. On the canvas, a user can connect different tools to perform a sequence of actions and create a workflow. The configuration window allows a user to configure specific tools within a workflow as well as the settings behind an entire workflow. Finally, the results window allows users to see their data as it is entered into a tool and how it looks after being modified by a tool. Seeing how data changes at different stages of a workflow is crucial when creating a complex analytical workflow.

Comparing Alteryx and Dataiku (2)

Similarities:

Alteryx and Dataiku have a similar method of feeding data into a workflow. Alteryx uses the Input Data tool to insert data sources, while Dataiku uses its dataset tab to import. Alteryx allows for a variety of input methods, including Excel and CSV files, cloud connections like Snowflake, Databricks, and Azure, and other ODBC connectors to popular on-premises databases like Microsoft SQL Server, Oracle, and PostgreSQL, to name a few.

Differences:

  1. After importing the data, Alteryx immediately provides information about the quality of the data, such as leading and trailing spaces, embedded values, and missing or null values. This information is also present in Dataiku, but the user must navigate to the stream's dataset window to see these details. When a user has many files and wants to examine the data quality, the Dataiku DSS environment can be time consuming. Alteryx Designer's UI is much more efficient than Dataiku in such a situation as it offers the ability to view multiple files in the results window as well as the data quality of each one when a user clicks the tool.
  2. Alteryx allows the user to drag tools from the tool palette and create logic with or without importing files into the workflow. This is useful for users who have an idea in mind and want to start creating an outline or the first pass of the workflow. In Dataiku it is not possible to develop a visual recipe without input data.

Comparing Alteryx and Dataiku (3)

Board-se

Almost all analytical processes involve combining data from different sources to generate deep and meaningful insights. The join tool in Alteryx can join rows from one data source to rows from another by concatenating at record positions or specific fields using a primary key. In Dataiku, joins are performed using the visual join recipe. However, Alteryx natively provides several types of joins in the tool palette for a better user experience:

Comparing Alteryx and Dataiku (4)

Similarities:

Both Alteryx and Dataiku offer custom field selection for joins.

differences:

  1. The Join tool in Alteryx allows users to rename, rearrange, delete, and change the data type of columns in their dataset. This can also be obtained from Dataiku, however the user must navigate to the newly created data set and make these changes there.Comparing Alteryx and Dataiku (5)
  2. The Join tool in Alteryx stores any unmatched records in the left or right anchor of the Join tool output, depending on which data source the record came from. In Dataiku, this can only be done by changing the join type.
  3. The Merge Tool in Alteryx allows users to rename fields after a merge in the Merge Tool configuration window. In Dataiku, the user needs to access the associated dataset to rename the columns.

Comparing Alteryx and Dataiku (6)

Quality Checking/Data Profiling

Similarities:

  1. Users can assess the quality and completeness of their data in Alteryx and Dataiku. In Alteryx, an overview of data quality can be viewed at a high level in the results pane when hovering over column names, and at depth with a browse tool. When using a browse tool, Alteryx displays summary data, length statistics, and common values. In Dataiku, this is done by clicking on each column and then clicking on "Analyze".
  2. Both platforms show valid, unique and empty values.

Comparing Alteryx and Dataiku (7)

Differences:

Unlike Dataiku, Alteryx identifies leading and trailing blanks when analyzing data quality and can distinguish between null and blank values ​​in data integrity summaries.

Apply a formula:

Similarities:

Both Alteryx and Dataiku show the preview of the data after writing the formula.

Differences:

The formula tool in Alteryx can be used to change data in an existing column or to create a new column. A single formula tool can contain multiple formulas for multiple different columns and includes a data preview to check an expression before applying it to the entire column. Users can also change the size and data type of new or existing columns referenced in the formula tool. In addition, Alteryx users can view recently used phrases and save their frequently used phrases for later use. Alteryx also offers suggested formulas based on the data type of the column.

In Dataiku, the formula tool is part of the visual recipe for data preparation. The tool would take the user to the data prep recipe window where in a new step he would have to write a function for each column. It can be difficult for Dataiku users to keep track of when and where a formula is used in stable workflows since they cannot see all formula apps in one place. Thus, consolidating multiple processes into the visual data prep recipe eliminates step-by-step tracking of users in Dataiku. Dataiku also enforces a new default column name, which needs to be changed in an extra step when new columns are created and existing ones are modified. These changes are not visible in the stream window in Dataiku like they are in Alteryx.

Comparing Alteryx and Dataiku (8)

drag and drop

Alteryx Designer offers its users a lot of flexibility to arrange the tools on the screen however they want. There are also ready-made options for aligning and distributing tools horizontally or vertically. The flexibility to customize the flow theme is useful for people who want to visualize their workflow logic. Dataiku does not provide this functionality. The visual recipes are placed in the flow window by the software after setup and do not give the end user any flexibility to change or move things.

Learning Paths and Community: Alteryx vs. Dataiku

Alteryx offers many opportunities to learn and become an analytics pro. They offer free materials and resources like interactive lessons, starter kits, and a solid community forum to ask questions and learn from other Alteryx users. Alteryx also offers free certifications for people at all different stages of their analytics journey: Fundamental Concepts, Core Tools, Advanced Tools, Expert, and Predictive Master.

Dataiku offers similar learning paths like Core Learning, ML Practitioner, Advanced Designer and Developer Path.

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