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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.
Great tool for exploring data flow and finding data assets
What do you like best about the product?
Data lineage is amazing. Being able to find any data asset across a variety of warehouse or BI platform, then explore both upstream and downstream has been very helpful. I can tell how specific metrics or data infrastructure are made, which helps me quickly troubleshoot issues. The search functionality is extremely powerful and performant. AI recommended documentation is also helpful.
What do you dislike about the product?
Limiting searches can be difficult. In my organization we have a lot of legacy data assets with no documentation. Search and data lineage often find dozens of likely results for my search and it's difficult to narrow those searches down.
What problems is the product solving and how is that benefiting you?
There are a variety of data-product creators in my area of responsibility and I'm not always up to speed on what they're making at any given time. I'm often asked to create data assets or update assets upstream, or to collaborate with colleagues. Using Atlan's search and data lineage makes this so much easier. I can see how a column in a data table is made or I can see the downstream impacts of the work I'm doing. Particularly for our globally distributed team: being able to investigate independently is a life saver.
Data lineage provides clear visibility
What do you like best about the product?
Data catalog and data lienage between the source and final reports is very helpful for all the users , both technical and non technical folks.
What do you dislike about the product?
Initial setup is time consuming and takes effort
What problems is the product solving and how is that benefiting you?
Business users benefit from the data catalog, which often requires back and forth conversations between data and business teams
Powerful Data Catalog tool with great features
What do you like best about the product?
Atlan search is extremely useful, allowing you to easily find all data assets within your organization. The lineage tool is also very powerful, and can assist in a multitude of different areas from understanding dependencies, making informed decisions on developments, and checking overall use of an asset.
What do you dislike about the product?
It would be great to have more capabilities involved with the lineage views. We have wanted to be able to filter based on the asset source (only lineage for the dbt assets, or Snowflake and Sigma etc.), so that is a downfall of this feature. It would also be good to include more automations and templates for things like Glossary terms.
What problems is the product solving and how is that benefiting you?
Atlan is helping individuals at the organization identify what data is available to them, what it is and how to use it. It is also helping with broad release of primary metric definitions and linking them to relevant assets for users to review and gain even further business context.
Atlan: great potential, amasing help, but some obstackes on the way
What do you like best about the product?
Atlan meets all the requirements that one would expect from the catalog: great search experience, variety of connection possibilities, easy of use, and clear UI. It is also highly customizable to our needs where we can add our custom metadata and needed for the business information on the data assets. Automatic lineage inside Snowflake and Tableau saved us hours of work. Customer support and the community around the catalog help us to integrate the catalog very easily. Thanks to multiple walk-throughs, it was extremely easy to sez-up the tool and hook it up to our architecture. It is a every-day tool for our data people at the moment.
What do you dislike about the product?
We have experienced some problems with connections to Tableau and Matillion that were unexpected (we have not seen them during the POC) and took longer than expected to fix, hindering the implementation process. There is no UI for lineage building inside the tool, which would be highly beneficial
What problems is the product solving and how is that benefiting you?
Observability on our data and architecture, ownership, clear understanding of terms and data assets among teams
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