Atlan - Active Metadata Platform for AWS

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.

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.

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.

Atlan is stable.

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.

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.

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.

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.

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.