Meet the Perfect Alteryx Alternative

Alteryx’s drag-and-drop interface is a good option for UI business created ETL workflows. However, it may be harder to scale, expensive to deploy, and has a higher switching cost due to its proprietary nature.

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Datacoves

Alteryx simplifies, but Datacoves empowers.

Alteryx was a pioneer in making data preparation accessible with its drag-and-drop interface especially for users coming from Tableau. However, companies find that Alterix ELT pipelines can lack governance and as with other GUI based ETL tools there are limitations. Alteryx’s approach can limit teams needing better integrated tools and capabilities. If you need a GUI based workflow and don't expect to have many pipelines, Alterix may be a good choice, but if you care about empowering your data team with a SQL based friendly environment that seamlessly integrates all parts of the data stack, you should consider enterprise ready Datacoves.

Why choose Datacoves?

End-to-end ELT

With Alteryx
Alteryx provides a drag-and-drop platform that simplifies the Extract, Load, and Transform (ELT) process for its users. It supports a wide range of data sources and destinations, making it easy to extract and load data. However, the platform focuses heavily on ease of use, which can limit the level of customization and control available for more complex transformations. While this low-code approach works well for less technical users, it may fall short for teams needing more advanced SQL-based transformations or detailed management of their ELT pipelines.

Flexibility for the organization

With Alteryx
Alteryx excels at providing non-technical users with an accessible platform to build workflows without writing code. However, the platform’s focus on ease of use can limit customization and flexibility when it comes to control of the generated code. While its low-code approach is beneficial for less technical users, it may not be the best choice for teams that are comfortable with SQL-based transformations and want control over their ELT pipelines without black boxes.

Scalability as you grow

With Alteryx
By default, Alteryx does not push down operations to the database which is not scalable. Its native orchestration includes basic workflow scheduling, automation, and task chaining within a single workflow. However, as pipelines grow, so does the complexity of managing and deploying all the pipelines. Alteryx's orchestration features may be limiting, potentially requiring additional tools to manage complex workflows efficiently.

DataOps compatibility

With Alteryx
Alteryx’s GUI-based ETL can act like a "black box," limiting visibility into the underlying processes and making it difficult to do code reviews and ensure consistent standards. This can make debugging more difficult, as it’s harder to trace issues or pinpoint errors compared to more transparent, code-based tools.

Vendor lock in

With Alteryx
Alteryx’s proprietary platform and tools may appear to be user-friendly and effective, but it can lead to vendor lock-in by making it difficult to migrate workflows to other platforms or adopt new tools without significant rework. The reliance on Alteryx’s specific ecosystem means that organizations may face challenges if they want to switch to an open-source or more customizable environment in the future. While Alteryx provides a comprehensive, user-friendly platform, teams looking to maintain long-term flexibility might find it harder to transition away from the proprietary infrastructure.

What our clients have to say

Thought leadership on data engineering

Datacoves allowed us to switch to more modern technology stack but most of all adopt a different mindset and approach to building and maintaining data pipelines and working with data as an asset.

Tobias Temmink
Data Management Lead
The Janssen Pharmaceutical Companies of Johnson & Johnson

Enabling modern DataOps

Datacoves does not just offer a platform, but helps teams adopt new ways of working. Datacoves is helping us build better data products by providing tooling and processes for groups of data engineers to work together efficiently.

Denny Verbeeck
Sr Manager, Data Architect
R&D Life Sciences