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.
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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.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other
Enhanced data management with intuitive data lineage and metadata visualization
What is our primary use case?
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.
How has it helped my organization?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using Atlan for almost a year now.
What do I think about the stability of the solution?
Generally, I find Atlan stable. There have been instances where new features temporarily degraded performance. They were quickly optimized by Atlan's engineering team.
What do I think about the scalability of the solution?
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.
How are customer service and support?
The customer service and support are pretty good. Any issues are addressed promptly by Atlan's team.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
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.
How was the initial setup?
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.
What about the implementation team?
The implementation of Atlan required less than five people from different teams, mainly technical personnel.
What was our ROI?
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.
What's my experience with pricing, setup cost, and licensing?
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.
Which other solutions did I evaluate?
We evaluated Google Cloud Platform's Dataplex, among other tools, yet chose Atlan for its comprehensive capabilities in data lineage and metadata management.
What other advice do I have?
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.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other
One of a kind player in the data space!
What do you like best about the product?
One of a kind player in the data space! Our experience with Atlan from the Proof of Concept stage all the way to the implementation has been nothing short of exceptional. The team is incredibly thoughtful with communication and documentation. They bring to the table a strong willingness to understand the gaps and solve problems
What do you dislike about the product?
Looking for more customization for the different kinds of data practitioners
What problems is the product solving and how is that benefiting you?
- Ease of discoverability, reducing time to implementation
- Collaborating with various other data practitioners.
- Communicating and defining lineage
- Collaborating with various other data practitioners.
- Communicating and defining lineage
Atlan: Best-in-class data catalog with customer-centric product development & services
What do you like best about the product?
1. End-to-end view of data lineage across our whole data lifecycle: Atlan perfectly integrates with large parts of our tool stack (Snowflake, DBT, Tableau, Salesforce, Fivetran), which gives us great transparency into our data flows & structures. Especially being able to identify & export dependencies have been pivotal for our data incident management and requirements analyses for changes in our data structure. In order to paint a full picture of our tool landscape, we would love to see additional integrations being published for rETL tools like Hightouch, data quality tools like Metaplane or additional CRMs like Hubspot & Zendesk.
2. Outstanding support from our CSM & Customer Support team: Our CSM serves as a trusted advisor when it comes to the overall architecture of the tool and general data governance best practices. His level of support & commitment for us to get the most value out of the tool is far above the average SaaS CSM support. For instance, our CSM introduced us to a dedicated internal Data Governance Consultant, who took multiple hours of time to share his experience and advise us on how to effectively implement data governance at our organisation.
Likewise, the Customer Support team exhibit impressive technical understanding & structured problem solving, providing us with very timely and deep analyses of any issues coming up.
3. Great pace of delivery for new features: Atlan regularly publishes highly relevant new product features at a fast pace. Very regularly, if we provide feedback around missing or non-optimal functionalities, it turns out that the respective feature is already on the roadmap and might even be released within the next couple of weeks. It has become very apparent that the product strategy is very much centered around customer feedback & pain points, which we appreciate a lot.
2. Outstanding support from our CSM & Customer Support team: Our CSM serves as a trusted advisor when it comes to the overall architecture of the tool and general data governance best practices. His level of support & commitment for us to get the most value out of the tool is far above the average SaaS CSM support. For instance, our CSM introduced us to a dedicated internal Data Governance Consultant, who took multiple hours of time to share his experience and advise us on how to effectively implement data governance at our organisation.
Likewise, the Customer Support team exhibit impressive technical understanding & structured problem solving, providing us with very timely and deep analyses of any issues coming up.
3. Great pace of delivery for new features: Atlan regularly publishes highly relevant new product features at a fast pace. Very regularly, if we provide feedback around missing or non-optimal functionalities, it turns out that the respective feature is already on the roadmap and might even be released within the next couple of weeks. It has become very apparent that the product strategy is very much centered around customer feedback & pain points, which we appreciate a lot.
What do you dislike about the product?
1. Workflows/Playbooks: Atlan offers the Playbook functionality to automate metadata population, data owner assignments, tag assignments, etc. While this functionality has proven very useful already, I would love for the automation engine to become more powerful. Top features on our wish list would be event-based triggers, more effective approval processes, metadata propagation across lineage and outbound API call functionalities.
2. Pricing: While the Atlan team has been ensuring that pricing should not be a blocker for our data governance efforts, the list prices for connectors and member licenses are fairly high compared to other SaaS tools and only economical at high discounts.
3. Bugs: While Atlan publishes new features at a very strong pace, there are somewhat regular cases where features do not work as expected and need to be fixed by the engineering team. It's worth calling out that in those cases, the fixes are implemented very quickly as well, though!
4. Permission management: The general permission management based on Personas & Purposes is somewhat too complex compared to other SaaS tools and not very user-friendly. We would love for our users to immediately see all metadata and assets that they have access to rather than having to switch between personas & purposes.
2. Pricing: While the Atlan team has been ensuring that pricing should not be a blocker for our data governance efforts, the list prices for connectors and member licenses are fairly high compared to other SaaS tools and only economical at high discounts.
3. Bugs: While Atlan publishes new features at a very strong pace, there are somewhat regular cases where features do not work as expected and need to be fixed by the engineering team. It's worth calling out that in those cases, the fixes are implemented very quickly as well, though!
4. Permission management: The general permission management based on Personas & Purposes is somewhat too complex compared to other SaaS tools and not very user-friendly. We would love for our users to immediately see all metadata and assets that they have access to rather than having to switch between personas & purposes.
What problems is the product solving and how is that benefiting you?
Overall, we're very satisfied with Atlan and our collaboration with them. Atlan is a game changer for our data governance & data management efforts, especially for collaboration within our centralised data team.
Atlan enables us to effectively share knowledge both within our data team and with external stakeholders by serving as a source of truth for our most important term & metric definitions. Terms & metrics can be easily & extensively documented in the Glossary including descriptions, in-depth ReadMes, custom metadata and external documentation (e.g. Confluence). It's also very helpful to be able to link these terms to suitable dashboards and tables to indicate where the source of truth for the respective term or metric comes from.
We're also using Atlan for the documentation of our Snowflake tables. While some documentation has already been included in our DBT models directly, this documentation is not easily accessible for stakeholders or downstream consumers of the data (e.g. analysts). Using Atlan, we can democratise this documentation automatically by pulling the respective descriptions from DBT directly and populating them in Atlan. Using Atlans powerful API, we're also working on loading ReadMes from our directory directly, such that documentation for data engineers becomes easy to populate and consumable by stakeholders at the same time.
Atlan is proving to be highly effective to analyse up- & downstream dependencies of tables, data products and dashboards, significantly reducing communication efforts. For instance, exporting downstream dependencies for impact analysis and analysing upstream dependencies for root cause analysis in Atlan have become an integral part of our data incident management process. Being able to quickly identify impacted assets and their owners not only enables fast assessment of the incident impact. It also allows for immediate stakeholder identification, saving valuable time in the incident resolution.
Lastly, we're also currently starting to use the Data Products & Data Domain features in more depth. Atlan allows to extensively document data products (e.g. description, ReadMe, criticality, sensitivity) and relate relevant tables and dashbaords via automated rules. By being able to add both data products and individual tables & dashboards to data domains, the tool is becoming our source of truth for data roles & responsibilities and valuable input for domain-specific reporting on data governance maturity.
Atlan enables us to effectively share knowledge both within our data team and with external stakeholders by serving as a source of truth for our most important term & metric definitions. Terms & metrics can be easily & extensively documented in the Glossary including descriptions, in-depth ReadMes, custom metadata and external documentation (e.g. Confluence). It's also very helpful to be able to link these terms to suitable dashboards and tables to indicate where the source of truth for the respective term or metric comes from.
We're also using Atlan for the documentation of our Snowflake tables. While some documentation has already been included in our DBT models directly, this documentation is not easily accessible for stakeholders or downstream consumers of the data (e.g. analysts). Using Atlan, we can democratise this documentation automatically by pulling the respective descriptions from DBT directly and populating them in Atlan. Using Atlans powerful API, we're also working on loading ReadMes from our directory directly, such that documentation for data engineers becomes easy to populate and consumable by stakeholders at the same time.
Atlan is proving to be highly effective to analyse up- & downstream dependencies of tables, data products and dashboards, significantly reducing communication efforts. For instance, exporting downstream dependencies for impact analysis and analysing upstream dependencies for root cause analysis in Atlan have become an integral part of our data incident management process. Being able to quickly identify impacted assets and their owners not only enables fast assessment of the incident impact. It also allows for immediate stakeholder identification, saving valuable time in the incident resolution.
Lastly, we're also currently starting to use the Data Products & Data Domain features in more depth. Atlan allows to extensively document data products (e.g. description, ReadMe, criticality, sensitivity) and relate relevant tables and dashbaords via automated rules. By being able to add both data products and individual tables & dashboards to data domains, the tool is becoming our source of truth for data roles & responsibilities and valuable input for domain-specific reporting on data governance maturity.
Easy data asset visibility and cataloguing for compliance needs
What do you like best about the product?
Atlan is easy to implement and integrate with existing systems. It can ingest data schemas from various systems and provides a clean user interface to add metadata such as tags. Atlan enables data and compliance teams to easily identify ownership, and sensitive or private data objects.
What do you dislike about the product?
The Atlan supplied tooling to ingest schema from PostgreSQL databases could use some dependency management as many of the dependencies contained vulnerabilities at the time of use. This tool was also only available for download by requesting a link and not available as a container image enabling automated updates.
What problems is the product solving and how is that benefiting you?
We have a fairly complex data ecosystem, and it can be difficult to keep track of what data is stored where. Atlan makes it much easier to catalog, search for, and classify information across our organization; enabling our data governance and compliance goals.
Great business-friendly data catalog for smaller enterprises
What do you like best about the product?
Atlan has a comfortable UI that is fairly intuitive to navigate, even for team members who don't interact with data often.
What do you dislike about the product?
The lack of a native Airbyte connecter has prevented us from getting as much value as possible from Atlan, and while the integrations with other systems such as Tableau are there, it isn't as in-depth as it could be.
What problems is the product solving and how is that benefiting you?
Atlan helps us to establish a common language around our data, find data that is available, and understand how to utilize it.
Great catalyst for data discovery and governance
What do you like best about the product?
Atlan can be fast and simple to configure and integrate, while still enabling more complex use cases later on.
What do you dislike about the product?
Few downsides, though occasionally new features also contain bugs.
What problems is the product solving and how is that benefiting you?
Atlan makes it easy for business and technical users to find data they need, enrich it with context, and understand lineage + impact
Atlan: Centralizing Data Assets in One Place
What do you like best about the product?
Ease of use, clean interface, search capabilities, lineage tool.
What do you dislike about the product?
Seach feature can get overwhelming if there are so many data assets to sort through. Having a data source filtering capability in the lineage tool would be helpful.
What problems is the product solving and how is that benefiting you?
Atlan centralizes all my org's data assets/sources so I am able to easily navigate through my org's data in one area.
Atlan Improves Data Visibility and Context
What do you like best about the product?
Atlan is helpful for searchign data objects and understanding the context/lineage surrounding them. In addition to providing descriptions of data objects and their lineage, it also provides recent usage statistics, which can be helpful for identifying the significance of a data object, and source for the data (e.g. Snowflake etc). The search itself is easy to use and has good accuracy.
What do you dislike about the product?
Search can be overwhelming if you don't know what you're looking for or are searching general terms. Additionally, the lineage is helpful but doesn't provide much context on how exactly parent objects are leveraged in the context of the object you're using.
What problems is the product solving and how is that benefiting you?
We have a fairly complex data ecosystem, and it can be difficult to know where to find the data we're looking for, particularly when starting a new project. Atlan makes it easier to search across our organization for the most relevant results as well as understand the sources and dependencies of that data.
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