Atlan is used as a data catalog and a data governance tool. The primary idea for implementing Atlan for data cataloging in day-to-day work was to create a business data catalog to support asset discovery and management.
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Improved data discovery has reduced redundant ingestion and now supports governed asset management
What is our primary use case?
What is most valuable?
Atlan offers excellent features including support for many different active metadata, such as lineage, which allows visibility into how an asset has been generated.
Active metadata and lineage features help quickly understand if an asset already exists, who the data steward is for that asset, and provide complete visibility on available fields and other technical aspects regarding the asset.
Atlan has positively impacted the organization since it helps in discovering already available assets, allowing for reduction of redundant ingestion of external data and reduction of time to market for any project.
What needs improvement?
Atlan can be improved by integrating agents that can support users in finding assets of interest.
For how long have I used the solution?
Atlan has been used for one and a half years.
What other advice do I have?
I rate Atlan overall a nine out of ten. I choose nine out of ten because even though Atlan is a very good product, it can be further improved. For others looking for a very complete data catalog tool with many features that can support data discovery, lineage, and more, I suggest choosing Atlan.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Transforms Data Collaboration with Ease and Efficiency
What do you like best about the product?
What I appreciate most about Atlan is how it transforms working with data into a truly collaborative and seamless experience. By bringing together people, data, and context in a single platform, it enables teams to genuinely understand and trust their data. I find it incredibly easy to locate what I need, document my work, and maintain organization without the usual confusion. Atlan is clearly designed to make data serve people, rather than forcing people to adapt to data. Its smooth integration with modern tools allows teams to work more efficiently, and it makes the entire data process feel more human, transparent, and enjoyable.
What do you dislike about the product?
One aspect I find challenging about Atlan is that it can sometimes feel overwhelming due to the sheer number of features and configuration options. For newcomers, the learning curve is quite steep before you can fully appreciate its value. There are also times when performance slows down, especially when handling very large datasets or complex integrations. Additionally, I would appreciate more flexibility in some customization options to better accommodate specific team workflows. However, these concerns are relatively minor compared to the overall experience—once you become familiar with the platform, Atlan proves to be both powerful and enjoyable to use.
What problems is the product solving and how is that benefiting you?
Atlan addresses one of the most significant challenges in the data world: the lack of visibility, collaboration, and trust in data. By bringing together scattered data assets, documentation, and context into a single, unified workspace, it becomes much easier to discover, understand, and confidently use data without wasting time searching or doubting its accuracy. Personally, Atlan has made collaboration across teams more efficient, reduced reliance on individual expertise, and enhanced overall data quality. It has also simplified onboarding for new team members and sped up decision-making by ensuring everyone has clear and reliable access to the right data when they need it.
Magically Boosts Productivity and Workflow
What do you like best about the product?
It significantly enhances productivity, streamlines workflow, and makes implementation across the business remarkably easy, almost like magic!
What do you dislike about the product?
The customer support isn't particularly impressive, but it is still an improvement over what I've experienced with other competitors.
What problems is the product solving and how is that benefiting you?
Integration of the source data made seamless and data privacy/storage are top notch that I can sleep without any issues.
Gratitude towards Atlan
What do you like best about the product?
Its user friendly data catalog and it is very supportive and collaborative.
What do you dislike about the product?
Limited customization for UI components.
What problems is the product solving and how is that benefiting you?
Ensuring good quality data.
Has struggled to meet business needs but supports technical data exploration and transparency
What is our primary use case?
I have been using Atlan for two years.
My main use case for Atlan includes data catalog, business glossary, import-export, data lineage, lineage import-export, and lineage generator, as I was the intense tester of Atlan.
In my day-to-day work, I use Atlan to analyze databases to find lineage or try to import business glossaries from Excel from several business sources, importing them into Atlan or trying to visualize the lineage, which is exactly what the customer needed. Importing a business glossary connects business terms with some IT tables and columns and makes it visible. What is unique about my main use case is that I was heavily involved in the import-export formatting of data, trying to automate some import-export, figuring out how the templates work for export-import, how the specific import-export workflows run in Atlan, what failures can happen, the uniqueness of data, and finding matching partners in lineage cases.
What is most valuable?
The best features Atlan offers for me include reading the data, having the interfaces, and reading the data from tables from different sources, performing automated import exports, and connecting sources to IT systems, which I consider the biggest strength of Atlan.
The interfaces and automated imports have helped me with transparency, as we have different sources from different techniques such as DBT, Snowflake, and other regular databases, making it effective to connect these sources and navigate through them, filter them, and enrich the data with additional meter information.
Atlan has positively impacted my organization by helping the business people use it to understand where the data is, what meta information is, and attempt to assume the roles of a data owner and a data steward, which was new for them. They find it much easier to search and navigate the data and better understand the data.
The change has affected my team's productivity and collaboration by reducing the time of finding the right data, navigating, and creating a new report, which helped the business people understand their jobs. When creating a new data governance team, it is a challenge for people to assume these roles, and using Atlan was a significant advantage in helping them identify what the role is and begin to assume it.
What needs improvement?
Atlan can be improved by concentrating more on business data since it is developed from developers for developers, and it needs to be more business relevant. For instance, when re-importing data model diagrams, Atlan provides some diagram automation that is not connected to the business glossary, which I consider a significant fault. Atlan needs to improve by focusing more on the business side of data, not only on technical aspects.
If you want to focus on technical considerations, it would be beneficial to have an interface with a real business data modeling tool such as Erwin or other business data tools, since data modeling is not the same as Draw.io. Additionally, Atlan can improve its workflows, which are hard to understand. Working with templates, Excel import, export, and running automations is not self-explanatory, and you always need help from Atlan support team. If business people want to use it and run their own reports, it must be easier to customize for their business needs.
What do I think about the stability of the solution?
Atlan is stable.
What do I think about the scalability of the solution?
I did not hear anything negative regarding Atlan's scalability. We started with a small group and brought business teams or new members on board, and there were no issues at all. Scalability is perfect.
How are customer service and support?
I can only share positive results about Atlan's customer support. They were keen on helping us, providing answers in a very comfortable time frame, making it quick and easy, and they always tried to offer the best solution even for our very customized requirements.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
I have experience with many modeling tools including DataSpot, ARIS, and Enterprise Architect, but those are not really data catalog tools. Depending on the purpose, it is important to start from the business side for implementations related to data understanding and data catalog, rather than from the technical side.
Which other solutions did I evaluate?
Before choosing Atlan, there were other options such as Collibra and Atlassian, but I was not involved in the proof of concept and we started working once Atlan was selected.
What other advice do I have?
For the user experience, it is sometimes tedious because when reading the source and finding the data tables, if I then try to find matching partners or visualize some lineages, it can be overwhelming. I need to either shrink the scope of my task, use lesser complexity, or use fewer tables to generate business values. Sometimes it can simply be overwhelming.
My advice for others looking into using Atlan is that they must be clear upfront about the purpose and scenario for which they want to use it. If they want to connect databases, read, surf, and do that, then I would recommend it. However, if they want to start a data governance organization or business understanding, that would be a totally different story because Atlan has strengths in technical connections.
Atlan should be made livable for business people. It should not be solely from developers for developers, as there is a vast range of business users who are happy if they can read Excel sheets but do not have knowledge about XML data formats and other technical considerations. If you want to expand your customer range, Atlan should learn business language and not focus so much on technical language, making it usable for business professionals. I rate this product a five out of five.
Atlan _review
What do you like best about the product?
The product stands out for its ease of use, making it accessible even for those who are not very tech-savvy. Implementation was straightforward, and I appreciated how the features were both comprehensive and practical. Integration with other tools was smooth, which made the overall experience even better.
What do you dislike about the product?
The AI capabilities are limited, and the automation feels quite rigid. Additionally, it lacks features for managing tasks across multiple teams.
What problems is the product solving and how is that benefiting you?
The platform offers data discovery and cataloging features, which help in organizing and managing information efficiently. Its automation and integration capabilities further streamline workflows, making it easier to connect different systems and reduce manual effort.
Powerful platform for data engineers, but lacking intuitiveness for business users
What do you like best about the product?
- Standardized data contracts using YAML templates
- Compatibility with open source frameworks like Apache Spark, Airflow
- Great technical catalog for data engineers
- Lineage packages and libraries
- Compatibility with open source frameworks like Apache Spark, Airflow
- Great technical catalog for data engineers
- Lineage packages and libraries
What do you dislike about the product?
- Not user friendly for line of business users
- UI / UX was perceived to be too technical
- Great technical catalog but doesn't cover business governance requirements like CDEs
- UI / UX was perceived to be too technical
- Great technical catalog but doesn't cover business governance requirements like CDEs
What problems is the product solving and how is that benefiting you?
- Data Lineage Tracking
- DataOps Monitoring
- Data Contracts Enforcement
- DataOps Monitoring
- Data Contracts Enforcement
Atlan for modern data management
What do you like best about the product?
Our experience with Atlan has been exceptionally positive, from initial exploration and technical evaluation to implementation and ongoing operational support. We included Atlan as part of our data infrastructure due to its intuitive user interface, robust product features, and forward-looking vision. Atlan's compatibility with the rest of our modern data stack (Snowflake, dbt, Sigma Computing) and other enterprise tools such as Slack, Google Workspace, and Chrome was a key factor, too. The Atlan team provided ample opportunity to carry out a Proof of Concept (POC) and Proof of Value (POV), which included live demonstrations of functionalities to the various data stakeholders. Additionally, Atlan's pricing and support model were both reasonable and adaptable.
Setting up the platform was swift, with a few hours of onboarding calls spread over a week. We could quickly run scans on our Snowflake instance and set up integrations with other platforms like dbt. Later, when support for Sigma Computing was introduced, we could integrate the same on our own. Features such as web search of metadata and detailed column-level lineage for root cause and impact analysis were an instant hit with our data power users.
The Atlan customer success team was meticulous in providing us with the necessary support to improve the adoption and engagement on the platform. They consistently strive to understand our specific use cases, deliverables, and business outcomes to provide optimal support. We appreciate how the Atlan team has supported us so far with active metadata management and helping improve the governance of our data and machine learning products.
Setting up the platform was swift, with a few hours of onboarding calls spread over a week. We could quickly run scans on our Snowflake instance and set up integrations with other platforms like dbt. Later, when support for Sigma Computing was introduced, we could integrate the same on our own. Features such as web search of metadata and detailed column-level lineage for root cause and impact analysis were an instant hit with our data power users.
The Atlan customer success team was meticulous in providing us with the necessary support to improve the adoption and engagement on the platform. They consistently strive to understand our specific use cases, deliverables, and business outcomes to provide optimal support. We appreciate how the Atlan team has supported us so far with active metadata management and helping improve the governance of our data and machine learning products.
What do you dislike about the product?
A strong product roadmap backs the vision, but the communication regarding the upcoming features and releases can be better. The concepts of Personas and Purpose overlap, and explaining them to most stakeholders is difficult.
What problems is the product solving and how is that benefiting you?
Helping find information about data is easier in a self service way that reduces dependency on data engineers and helps them focus on writing code. The lineage helps during impact analysis and troubleshooting. Establishing a better data governance is easier with a tool like ATlan.
Using Atlan for Data tagging/Classification
What do you like best about the product?
The ability to apply definitions and tags for almost any data asset is useful, especially the feature to attach existing documentation as a resource. Great search feature, and ability to propagate tags downstream from objects.
What do you dislike about the product?
The UI is sometimes difficult to navigate, and there are some screens that may be more suitable to view as a table. There's also sometimes a steep learning curve to go from basic read-only use (or single asset management), to enabling mass-tagging/documentation efforts.
What problems is the product solving and how is that benefiting you?
Atlan is supporting our initiative to tag and classify all sensitive data in our source system and downstream applications. Being able to propagate tags and view lineage has been really useful.
Integration with communication platforms streamlines data access
What is our primary use case?
What is most valuable?
The best feature of Atlan is its integration with communication platforms like Microsoft Teams and Slack, so business users don't have to go into a data catalog to see metadata about data assets. This integration feature is the coolest thing about Atlan. The ML capabilities that suggest data classifications and provide data descriptions are also impressive.
What needs improvement?
One of the main areas for improvement is its governance capabilities. Atlan supports only basic out-of-the-box workflows, and it becomes challenging to customize features like how data owners should approve access to data assets. Its performance is not optimal when dealing with larger datasets, particularly legacy data assets, as the performance declines when scanning datasets running in terabytes.
For how long have I used the solution?
I have done a couple of POCs using Atlan while working for a company. We were evaluating a few data catalogs, and we included Atlan as one of the prospects.
What do I think about the stability of the solution?
During our POC, the recently launched ML classification system was a hit-and-miss. However, the support team acknowledged it and since then, the feedback from various users indicates that the issues have been resolved, and it's now working well.
What do I think about the scalability of the solution?
Atlan integrates well with smaller datasets, making it suitable for agile companies. However, it struggles with performance when dealing with larger datasets, particularly those running in terabytes.
How are customer service and support?
During the POC, we had a dedicated account executive, and we received good support from them. They helped us navigate our challenges and brought in technical resources whenever required. There were instances when responses took longer than expected, but this could be attributed to us not being a full-time paid customer at that time.
How would you rate customer service and support?
Positive
How was the initial setup?
If implementing the cloud instance, the setup is straightforward and simpler than other tools I have experienced. However, placing it on-premises requires support from data engineering or technical associates.
What's my experience with pricing, setup cost, and licensing?
In comparison to established players like Collibra and Informatica, Atlan is cheaper. However, compared to the next generation of data catalogs like Castor, Atlan is pricier. For mid-sized organizations, Atlan provides a good pricing fit.
Which other solutions did I evaluate?
We evaluated Atlan alongside other data catalogs when working for a company.
What other advice do I have?
Atlan is an eight out of ten, primarily due to its need for improved governance features. If these features are enhanced, it is a ten on ten tool.
Atlan has unique ML/AI capabilities that aid engineering teams in documenting without having to start from scratch.
Additionally, its integration with communication platforms helps users understand context without accessing the data catalog directly.
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