Atlan Reviews

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.

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.

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.

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.

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.

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.