Alteryx is known as a self-service data preparation and analytics tool. Alteryx’s purpose is to make BI teams far more productive by spending less time preparing data and invest more time analyzing data.
All the processes used to prepare the data in a suitable format to perform the analysis are created with data workflows. Users can build their workflows, which are also called pipelines, in Alteryx by preparing data without any programming skills. It aims to speed up the data preparation process with drag and drop workflows and data cleansing techniques to produce pipelines to cleansed, formatted data.
After finalizing a workflow in Alteryx, it is “published” for downstream consumption. Published work streams are the pipelines to clean data. The workstreams can be exported to places such as cloud data platforms, directly to Tableau, or stored in a file.
Alteryx aims to have BI teams spend less time preparing data and invest more time analyzing data within their own tools or 3rd party tools such as Tableau.
Alteryx is too complex to use for the average Excel user. Also, Alteryx only exists as an on-premise tool.
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:
Datameer’s data transformation and modeling features, similar to those need for data preparation, help model and sharpen data for analysis – for example, blends, filters, enrichment, and other forms of transformation. Alteryx builds the data and creates executable pipelines to fill with data. You can see where there is some common ground between the two offerings. However, there are major differences between the two offerings:
|Multi-persona UI/tools: no-code, low-code, code||Single dataflow tool/UI requiring in-depth data knowledge|
|Direct integration and execution of data models in Snowflake||Proprietary, in-tool model metadata and execution|
|Rich Catalog-like data documentation||Limited attribute information about dataflows|
|Collaboration and shared workspaces||Limited collaboration features|
|Shared and crowd-sourced data governance||Limited, policy-based data governance|