IBM Databand Alternatives

Considering alternatives to IBM Databand? See what this market IBM Databand users also considered in their purchasing decision. When evaluating different solutions, potential buyers compare competencies in categories such as evaluation and contracting, integration and deployment, service and support, and specific product capabilities.

Check out real reviews verified by Gartner to see how IBM Databand compares to its competitors and find the best software or service for your organization.

Reviewed in Last 12 Months

How Alternatives Are Selected

1. Monte Carlo

4.7

My overall experience with MC is very good. It is super nice that many aspects are automated and it helps my team to easily get to the root cause of a quality issue.Read all insights and reviews for Monte Carlo

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2. Bigeye

4.6

BigEye is an essential tool for us as it provides an easy way to hook into our value added services and get early preemptive warnings and alerts to circumvent issues before they arise.Read all insights and reviews for Bigeye

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3. Synq

4.9

I/we have been working with Synq and the team for a few years now, and not only does the product itself help us in getting alerts on our dbt models and health status over time, the team are always interested in understanding our use cases and are adapting those into their product. And they always try to help us get the most out of the product.Read all insights and reviews for Synq

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4. Pantomath

4.8

Working with the Pantomath team has been a wonderful experience. It's been very satisfying to see all levels of management actively engaged in the Pantomath product. The product and implementation teams have been very easy to work with and are there to answer questions that come up during any part of the process. The product itself is very easy to use. I look forward to seeing how this product evolves in the next year!Read all insights and reviews for Pantomath

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5. Soda

4.4

Soda is a complete product for executing Data Quality checks, as it offers both an interactive UI but also the means to run them programmatically. Their Customer Support has also been reliably on time and helpful and it fostered collaboration in order to develop a better tool.Read all insights and reviews for Soda

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6. Telmai

5

Telmai was very easy to deploy and maintain compared to other tools I have used in this space. The UI is intuitive and easy to use, onboarding data to the tool is very simple, and using out of the box ML based data observability (ex., data drift, freshness, schema drift, etc.) is as easy as flipping a toggle. It works with all of the cloud based technologies we use in our stack, and is forward looking with a focus on the ability to work with open data formats (i.e., iceberg). I have been using Telmai to scan large data assets (hundreds of columns, some with nest formats / json, billions of rows), and have had no issues beyond initial cluster sizing setup one time during initial deployment, which the Telmai team was able to help with and resolve quickly. I have been very happy with this tool and look forward to continuing to use it.Read all insights and reviews for Telmai

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7. Sifflet

4.5

Sifflet provides full stack lineage; it really helps you understand where it's coming from, how it's being transformed, and where it's ending up. It demystifies data in context of the asset you're looking at can help all types of personas understand and use data. Sifflet is dedicated to improvement, their friendly and engaging customer success reps are always open to feedback, improvements, and working together.Read all insights and reviews for Sifflet

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8. DQLabs Platform

5

easiest to use data observability tool, fully automated - saves a lot of time for data engineers Read all insights and reviews for DQLabs Platform

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