Atlan Reviews

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.

Positive

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.

My main use case for Atlan is to define a flexible set of metadata, including measuring the quality of said data. Atlan is also a partner of ours, so we use it to integrate with our own tool. It also enables our team to automate manual and high-risk tasks to make them error-free as much as possible and save time. In terms of scope, I only know that our Sales, CSM, and CX teams use it for similar uses. Our business may use it to a larger scope than that in the future.

A quick specific example of how our team uses Atlan to automate a manual or high-risk task is the robust functionality for the quality management of metadata sets. The most helpful thing here is all the dashboards that provide visibility in one place. We use this tool because it is very easy and integrable with other data sources, which is very convenient and time-saving. By switching to Atlan, we have increased our productivity and saved a lot of business time through automation. My team, whether technical or non-technical, adapted Atlan very quickly because of its simple and intuitive user interface.

The best features Atlan offers include the ease to extract and modify the data. We can do very quick and smooth integration. It is an open-source framework that provides extension capability to the APIs it uses.

Tons of automation help to automate tedious and time-consuming tasks, thereby reducing error and saving time and cost. It is extremely user-friendly and intuitive to navigate, especially the user interface.

Atlan has positively impacted my organization by saving time and money while consolidating our metadata sets. Integration with Amazon and Tableau have definitely sped up our time to value.

We have saved fifty-five percent of our time through the automation of tedious and time-consuming tasks. Extraction of data is easy. In this data catalog solution, data modification is really simple and easy. Integration with other software is very seamless and easy, thereby simplifying our workflow.

Atlan is very scalable and helps to grow our organization while catering to our needs.

It enables the team to automate manual and high-risk tasks to make them error-free as much as possible and save time and cost. It is a very cost-effective tool that should be highly adaptable and usable in any organization.

Atlan can be improved by enhancing their support process by reducing the resolution time.

They have really bad and incomplete documentation, so they should work on the documentation as well, especially to help new users who are onboarding to make their process more intuitive and smooth.

I have been using Atlan for five years.

Atlan is stable; I have not experienced any downtime.

Atlan is very scalable and helps our organization to grow while catering to our needs.

Customer support for Atlan is not so proactive. They should improve their proactiveness to be more proactive and responsive.

Negative

Previously, I used Informatica, specifically Informatica Application ILM.

I decided to switch from Informatica to Atlan because the other tools were solid. We ultimately ended up going with Atlan for this reason: it improved our compliance and risk management. It is easy to adopt and has good pricing. It seemed to have the most robust functionality, especially in terms of integration. It is easy to use, anyone could pick it up relatively quickly, and the cost was affordable.

We purchased Atlan through the AWS Marketplace.

Atlan has certainly saved time and money by consolidating our metadata sets.

My experience with pricing, setup cost, and licensing is that the licensing cost is a bit flexible but not affordable for smaller organizations. It might be way out of their budget, but it is very affordable for medium and large enterprises.

Before choosing Atlan, I evaluated Talend Data Catalog.

Day-to-day, we introduce some new technologies in our system, and every time we integrate those tools with Atlan, it is way easier and smoother. I would rate this review as a nine out of ten.

My main use case for Atlan is that it is very easy and integrable with other data sources, which is very convenient and time saving. By switching to Atlan, we have increased our productivity and saved a lot of business time. Our team, whether technical or non-technical, adapted to Atlan very quickly because of its simple user interface.

A quick, specific example of how my team uses Atlan day-to-day is that we introduce some new technologies in our system and every time integrating those tools or technologies with Atlan is much easier and smoother.

My team uses Atlan to define a flexible set of metadata, including measuring the quality of said data. Atlan is also a partner of ours, so we use it to integrate with our own tools. It also enables the team to automate manual and high-risk tasks and to make them error-free as much as possible.

The best features Atlan offers are that it is very easy to extract and modify data. We can perform very quick and smooth integration. Its open-source framework provides extension and capability to the API it uses. It has a user-friendly interface, easy access to data, data tables, and a lineage feature.

Atlan provides extensive and seamless integrations, a ton of automation, and an extremely user-friendly and intuitive interface to navigate.

Atlan has impacted my organization positively because it has been very helpful. Atlan has certainly saved time and money by consolidating our metadata sets. Integration with Amazon and Tableau have definitely sped up our time to value.

We have been able to save time and money by consolidating our data by at least 45%.

Atlan can be improved because API documentation could be enhanced.

I wish the support team could be quicker in their response time.

The reason why I rated it a nine is that it has improved our compliance and risk management. What could have made it a perfect ten for me is if it had improved API documentation and if the support could be much quicker in their response time. It is also very easy to adopt and has good pricing.

I have been using Atlan for about six years.

My advice to others looking into using Atlan is that it is a well-suited tool because it is very cost-effective and easy to use. It also has robust functionality for the quality management of metadata sets. The most helpful thing here is all the dashboards that provide visibility in one place. In my experience, it might be less suited for collaboration across teams.

Atlan is very easy to use, and anyone could pick it up relatively quickly. I rated this product a nine out of ten.

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.

Positive

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.

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.

Positive

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.

We use Atlan as a data discovery tool for data assets in our organization. It is particularly valuable for its ability to serve as a business glossary and a metrics glossary. 

Additionally, it helps in lineage tracking to find data sources and overall dataset schema and is used extensively for data quality checks.

Atlan is helpful for identifying datasets and discovering PI data, such as the classification levels of datasets (gold, silver, bronze). The tool facilitates easy navigation by keeping descriptions and source information in one place. It supports data glossary terms, which helps with understanding business terms.

One area that could be improved is the capability to find duplicates of datasets. This would allow users to identify and consolidate similar datasets, having one point of access for consistent data.

I have been working with Atlan for about a year and a half.

Atlan is quite stable. I have never seen it crash, and it has always been available to meet our needs. This consistent availability ensures we can find the right dataset when needed.

Regarding scalability, recent updates in Atlan have included data contracts and specification levels for datasets. The company has a good relationship with Atlan, which regularly visits our office to understand our needs. As a result, I would rate the scalability as nine.

I have never spoken to technical support, as I have not experienced any issues that required their assistance.

Positive

I am not aware of any previous solutions the organization used before Atlan.

It's challenging for me to comment on the ROI since I am just a user of Atlan and do not have visibility into metrics or financial improvements.

I am not aware of the pricing or setup cost.

I am not aware of any other solutions that were evaluated.

Overall, I rate Atlan as a ten out of ten. It is an excellent solution for data discovery and glossary needs. I suggest improving duplication detection for datasets as an enhancement.

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.

Neutral

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.

Its core offering is a data catalog. However, it goes beyond that. We are building a hub of knowledge, encompassing business and data knowledge across the entire company. 

The idea is that any employees or new hires needing onboarding can use it to understand the data they'll be working with, the data sources in the company, and the business knowledge of their field.

Atlan has many branches and features. Perhaps the most valuable one is the abundance of out-of-the-box connectors. As soon as you get access to the product, you can start using their connectors, which are easy to use and connect across a wide variety of data sources. This eliminates the need for lengthy development efforts to create new integrations. 

They also offer automated lineage embedded in the connectors, allowing us to track where the data is coming from and where it's going. We find it very efficient in cataloging data sources. 

Additionally, the user-friendly interface is a big plus, making it easy for users to familiarize themselves with the solution. These two features, in my opinion, stand out the most.

There are some improvements. There is a feature called Playbooks, which basically allows me to automate certain activities that would otherwise be manual. It's a very interesting feature, but there is room to improve it because, depending on the task you automate, the playbooks seem to have a hard time handling the task. So, it could be improved there. Even though it's a great feature, it can evolve further. 

And the product as a whole Atlan could invest in features like a data marketplace. It would be interesting to have access to data because it is a data catalog, but it could also be the main marketplace where users request access to data to be able to define, and that access should be given in a seamless manner.  

There is room for improvement on workflows as well, like the solution. It might not be the core of the solution, but they could start investing in having very robust workflows, especially for metadata data analysis. There is a simple workflow that could be improved.  

We have been using Atlan for seven months now since we started the partnership. But we began using Atlan for more than a year because we had a POC before. 

So, we tested it a bit during the POC, finalized the partnership, and then began working with the tool in a productive environment, which is seven months now.

We haven't had many problems with stability. In terms of the connectors, for instance, we have been able to catalog more than a  million assets, and we haven't had a lot of problems with the connectors or with retrieving the lineage of these assets. 

However, playbooks might fall a little bit from this category. The playbooks are a feature of automated tests, and we see that depending on the test we want to automate, sometimes the playbooks have a little bit of trouble scaling. So, that's the only area of improvement in Atlan. 

We have almost 400 users. Among these users, we have business analysts, data analysts, data scientists, data engineers, architects, InfoSec, and information security officers. So, we have a very wide variety of professionals taking advantage of it.

We had to reach out to  customer service and support for a couple of reasons, maybe questions regarding the product, questions on how to use it in the future, but also with technical issues as well, like maybe a connector not working or the playbooks weren't working. And they were always available. They helped us solve our issues. So support is very good.

We had a legacy platform. We worked outside the site with it, and that's why we decided to look at the market for a different solution, and that's how we found Atlan.

It was fairly easy. The connectors are very easy to use. So, we just have to get user information into our environment, and we plug that user into the connector, and it's just gonna crawl all our assets for cataloging. The setup was very easy for us. 

Also, because it's a SaaS solution, it's software. We don't have to set up infrastructure. We don't have to install it. It's just our website. We log in to our website, and we are good to go. So, a very easy setup, very easy to start using, and start delivering value.

The infrastructure of the solution is a fast solution for us. We're using the software and the user licenses. The infrastructure is from Atlan, and it's cloud-based, and it's on AWS.

We have three people who look like admins. I am one of them. We have two others, and each one of us takes care of one aspect. I do overall management and work on expanding our scope of governance and, so, working on integrations with other data sources. I have colleagues who take care of day-to-day activities, keeping the environment updated, managing user permissions, and similar tasks. So, basically, three people.

We pay per-user license. It's a different classification model than with other solutions, where they usually charge you for resources. 

So, the more assets, the more data sources, the more processing you do, the more you pay. 

Atlan only charges for users, and we established a model with them where we had a baseline of users. If we went past this baseline, we would pay proportionally to the number of users on the solution. 

So, that was a better model for us. And because of this difference in models or classification, it was cheaper for us to go with Atlan. 

We did look for other solutions. We actually had a period of work meetings with different vendors and did POCs with different vendors. 

We compared a few open-source solutions as well and did a sort of checklist. We compared all the vendors in terms of which was a better match for us in terms of features, fulfilling our expectations, and costs—trying to figure out which of them made sense overall.

We looked into Informatica, Collibra, Big ID, MANTA, and OpenMetadata as open-source data lineage solutions.

I would recommend using it because my experience with it has been very positive. To our strategy and our current maturity, it made a lot of sense, also within our budget. It was a very good cost/opportunity ratio, as other solutions were very expensive.

Overall, I would rate the solution a nine out of ten. Overall, it's a very good solution. It's a recent, brand-new, company-friendly solution. They have been able to fulfill our expectations. 

We have been able to do what we wanted, to be where we wanted to be. They have a few issues here and there, but that's normal for any product or solution. Overall, we are satisfied.

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.

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.

Atlan can be improved by integrating agents that can support users in finding assets of interest.

Atlan has been used for one and a half years.

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.

In my role focused on data governance, Atlan is crucial for cataloging and managing assets from various sources like SAP into our company's data lake.

Atlan has significantly streamlined our data governance processes by centralizing asset management and improving metadata handling. However, the absence of scheduled email reports has been a minor inconvenience, as it would enhance our workflow efficiency.

The feature I find most valuable in Atlan is its exceptionally user-friendly interface. Unlike other more technical data catalogs I've used, it has an intuitive design that facilitates easy adoption among business users.

The product could be improved by offering scheduled email reports for managed assets. This feature would greatly benefit users like me who need regular updates without having to log into the platform daily. 

For future releases, enhancing reporting functionalities, particularly the ability to schedule and receive email reports, would be valuable.

I've been using Atlan for about five months now.

The product is stable. 

Although I haven't been directly involved in large-scale user deployments, the platform is scalable. Adding users has been manageable and aligns well with our current needs.

The technical support services are good. 

The initial setup process was straightforward. 

The support from Atlan's vendor team has been excellent in assisting with any setup queries.

Using Atlan for day-to-day operations has been straightforward. I would recommend it for its user-friendly interface and robust data governance capabilities.

I rate it a nine.