Datameer versus Tableau Prep

Tableau Prep is a self-service data preparation tool offered within the Tableau product family.  It provides a visual, workflow style way to combine, shape, and clean data, making it easier for analysts and business users to start their analysis.

Before Tableau Prep, many Tableau users used Excel for data preparation, then reimporting the data back into Tableau.  Tableau Prep was designed to eliminate the need to use Excel for data preparation and give users a simpler way to prepare data on their desktop.

Tableau Prep has two major components:

• Tableau Prep Builder, a desktop tool to design data preparation workflows, and
• Tableau Prep Conductor, a server-side tool for running, monitoring, and managing flows.

Tableau Prep Builder provides a visual, point-and-click user experience to filter, cleanse, combine, and shape your data.  Like Datameer, the user interface is interactive, and users can immediately see the impact as they add various functions and how the shape of the data has changed.

Tableau Prep Builder offers many connectors, mostly to analytic data sources (databases, data warehouses, analytics databases) and simple structured files.  It contains close to 100 functions to transform and shape the data.

Tableau Prep Conductor is the engine behind Tableau Prep to run your workflows.  Tableau Prep Conductor is a part of the Data Management add-on for Tableau Server (at extra cost).  Tableau Prep Builder and Conductor also integrate with Tableau Catalog to allow dataflows to be cataloged, tagged, and made discoverable.

Datameer is a powerful SaaS data transformation platform that runs in Snowflake – your modern, scalable cloud data warehouse – that combines to provide a highly scalable and flexible environment to transform your data into meaningful analytics.  With Datameer, you can:

• Allow your non-technical analytics team members to work with your complex data without the need to write code using Datameer’s no-code and low-code data transformation interfaces,
• Collaborate amongst technical and non-technical team members to build data models and the data transformation flows to fulfill these models, each using their skills and knowledge
• Fully enrich analytics datasets to add even more flavor to your analysis using the diverse array of graphical formulas and functions,
• Generate rich documentation and add user-supplied attributes, comments, tags, and more to share searchable knowledge about your data across the entire analytics community,
• Use the catalog-like documentation features to crowd-source your data governance processes for greater data democratization and data literacy,
• Maintain full audit trails of how data is transformed and used by the community to further enable your governance and compliance processes,
• Deploy and execute data transformation models directly in Snowflake to gain the scalability your need over your large volumes of data while keeping compute and storage costs low.

At its core, Tableau Prep was created to eliminate the “Excel use case” for data preparation.  The design of Tableau Prep was to make things as simple as possible for the user.  Datameer and Tableau Prep have a few things in common:

• They both have the ability to transform data
• They both allow you to build data models and run data workflows
• They both help you organize (catalog) workflows and datasets

Beyond this, Datameer offers several key differentiated capabilities that Tableau Prep does not, allowing Datameer to facilitate faster analytic engineering at a lower cost:

• Multi-persona UI
• Snowflake integration
• Collaboration
• Data enrichment
• Rich data documentation

Tableau Prep has a standard data workflow style user interface.  As you define data transformation steps, you add new components to your workflow.  It is very typical for ETL tools.

Datameer wants to be attractive to a wide audience with many different skills across the entire data and analytics teams – data engineers, analytics engineers, data analysts, and more.  Some personas may be more technical and coding-centric while others may be less technical but more data-savvy.

To meet this goal, Datameer offers three different user experiences:

• No-code – Easy drag-and-drop modeling that lets non-technical analysts create base models specific to their needs.
• Low-code – A wizard-driven Excel-like interface that allows non-technical analysts to enrich data and dramatically improve data engineer productivity.
• Code – Full SQL coding and scripting that gives your data engineering teams the control to create and deploy highly optimized data models.

Each allows team members to participate in the data transformation process while using their various skills.  Models created in each of the three UXs can be mixed and matched together.

Tableau Prep has its’ own proprietary storage, storage format, and execution engine.  When you run Tableau Prep dataflow you end up creating copies of data that are stored in Tableau Server or on your desktop.

Datameer builds models directly into Snowflake using data already extracted and loaded to maintain extremely high data security and easy governance.  Transformations are executed using the Snowflake engine to take full advantage of its power and scalability.  Datameer also inventories and documents the loaded raw data to add further knowledge and discoverability.

Tableau Prep is designed for individual users to create their own dataflows.  In fact, Tableau Prep is a desktop tool solely for individuals to use.

In Datameer, shared workspaces are a foundational component.  Data engineers, data analysts, business analysts, and data scientists can work together in shared workspaces creating and reusing models using the different tools – code, low-code, no-code.  Beyond working together on modeling, the broader team can share knowledge about the workspace and models, including:

• Adding descriptions, which can both explain data and how best to use it,
• Applying tags, which can help organize and identify data,
• Supplying comments, which can add simple ideas around data or enable collaboration,
• Adding business metadata, which translates technical metadata into business terms
• Setting properties or status and certification fields, which describe the state of a data object

The main design point for Tableau Prep is to allow users to blend and cleanse their data.  It contains a limited set of functions to create new fields within the data.

Datameer offers a graphical formula builder and a deep array of functions to enrich your datasets with calculated columns.  Any function that is available in SnowSQL (Snowflake’s SQL version) is available to users in Datameer, and Datameer maps these functions one-to-one to SnowSQL for optimal performance.

Tableau Prep offers simple mechanisms to add descriptions on dataflows and datasets.  None of the information is searchable within Tableau Catalog.

Datameer facilitates capturing as much information as possible about the data it is working with, the transformations performed, and the resulting data models.  This includes:

• Auto-generated documentation and information such as schema information, transformations performed, data lineage, audits, and certain system-generated properties
• User-supplied documentation such as descriptions, tags, comments, and business metadata.

While Tableau Prep is a friendly, easy-to-use tool for simple self-service data preparation, it does not match the depth and breadth of Datameer, which was designed for true enterprise-class data transformation.  Datameer covers both analyst self-service data transformation and large-scale data engineering and lets your organization scale out and create a data transformation hub across departments, use cases, and analytics types.