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 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.

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.

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.

We are a consulting company, and we propose Atlan as one of the tools in the market. We inform clients that if they are considering a data catalog, they can also consider Atlan. We evaluate it for them, determining whether it would make sense for their organization.

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.

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.

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.

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.

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.

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.

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.

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.

We evaluated Atlan alongside other data catalogs when working for a company.

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.

One of the primary use cases of Atlan is as an enterprise data catalog. It takes metadata from multiple types of source systems, such as Tableau and Google BigQuery. The platform helps surface everything into a unified data dictionary and data catalog.

From a business user's perspective, Atlan has reduced the need to bother subject matter experts by surfacing all data and context. It has significantly reduced instances of business users contacting technical SMEs with questions about data content, providing time savings from a human hours perspective.

As a senior analytics engineer, Atlan's ability to show end-to-end data lineage is the most important feature for me. It graphically displays the entire data flow, allowing me to understand the flow from source systems to Tableau, including the ability to see SQL scripts behind it and usage metrics. Its capability to automatically pull data descriptions and assign ownership are also noteworthy.

Certain UI changes could make Atlan more user-friendly. While they have an Excel add-in for interaction with Atlan, it's in its early stages and could be improved. Additionally, data observability capabilities could be enhanced to provide more alerts on stale or outdated data via communication tools like Microsoft Teams, Slack, or email.

I have been using Atlan for almost a year now.

Generally, I find Atlan stable. There have been instances where new features temporarily degraded performance. They were quickly optimized by Atlan's engineering team.

There have been no notable scalability issues with Atlan. Its cloud-native backend infrastructure is designed to scale and may require human intervention occasionally, but it handles scalability well.

The customer service and support are pretty good. Any issues are addressed promptly by Atlan's team.

We looked at Google Cloud Platform's Dataplex. It wasn't fit for purpose because we needed an active metadata platform that could provide comprehensive data discovery and data dictionaries.

The initial setup of Atlan was conducted as a minimum viable product, starting with basic capabilities and building up. From the user's perspective, it involved training a small community of super users to promote adoption.

The implementation of Atlan required less than five people from different teams, mainly technical personnel.

It's difficult to quantify ROI at the moment due to our immature use of Atlan, however, it has reduced interruptions for technical queries significantly.

The pricing model for Atlan is reasonable compared to other cloud-based platforms. There are different license levels, and we receive a discount on the unit price, making it very competitive.

We evaluated Google Cloud Platform's Dataplex, among other tools, yet chose Atlan for its comprehensive capabilities in data lineage and metadata management.

I would recommend dedicated resources from the beginning, including a product manager, as it facilitates better planning and adoption of the solution.

Overall, I rate Atlan a nine out of ten. They continue to innovate and listen to customer feedback.

Data governance is a broad area, and Atlas is used for various purposes including self-service discovery, lineage, root cause analysis, impact analysis, and capturing all the metadata.

Atlas is quite intuitive. In the market, tools like Informatica require multiple components, however, Atlas provides all capabilities within one tool. Its UI is appealing and intuitive.

Additionally, the AI part is integrated for enriching metadata engagement, making it a comprehensive tool.

The challenge is in the lineage, where it requires improvement. Atlas needs to capture areas where organizations use less known applications.

I've used the solution for more than a year now.

The UI exposure is very good, but as you consume more resources from the infrastructural point, such as managing bulk metadata, you might need the support of an infrastructure team. This is where Atlas's product team can help by increasing resources.

You can scale, but it's necessary to reach out to their architectural team. Out-of-the-box scaling capabilities are limited and defined as standard by Atlas for their clients.

The support team can be improved, especially the ticketing system. Though the team's knowledge is satisfactory, the overall ticketing process requires enhancements.

We evaluated other data governance tools before settling on Atlas.

The initial setup doesn't take much time since it's a cloud platform. However, issues may arise with the lineage aspects that Atlas needs to address.

Atlas is competitively priced compared to other data governance platforms like Collibra and Informatica. The only major drawback is its lack of an integrated data quality module.

We evaluated other data governance tools previously.

For stability, I would rate relevant features at seven out of ten. Atlas is suitable for smaller to mid-size companies, however, for larger organizations with multiple ERPs and data lakes, it may present challenges.