Alteryx is a 10+ year old software and can be complex to use. It works on your desktop creating more data silos and governance issues. In January of 2022, Alteryx announced it would acquire a competitor, Trifacta, to bolster Alteryx’s floundering cloud and SaaS efforts. Learn about the best Alteryx Alternative and Competitor.
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:
Alteryx’s purpose is to make BI teams far more productive by spending less time preparing data and investing more time analyzing data within their own tools or 3rd party tools such as Tableau.
Alteryx is too complex to use for the average analyst. The user experience and interface make it difficult to define data preparation flows.
The traditional Alteryx products only exist as an on-premise, desktop tool. The company’s “Designer Cloud” is still in limited availability a full year after it was announced leaving potential buyers with no cloud or SaaS solutions. The on-premise, desktop, and departmental use can create more data silos, will create governance problems, and makes it difficult to see and understand data transformation workflows.
Alteryx recently announced that it was acquiring Trifacta, a competitor, and would merge the product families. The main reasons for the acquisition were to “buy” a customer base and use Trifacta’s engineering talent to bolster their failing cloud and SaaS offerings. Even with the engineering talent from Trifacta, transitioning the Alteryx products to the cloud will still be complex and potentially fraught with many problems.
Datameer’s data transformation and modeling features, similar to those needed 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|