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About Atlan

Atlan is a collaborative data workspace platform from Atlan that supports data teams in their workflow. It combines project management, data governance, and documentation features so teams can work together effectively on data projects. Atlan provides a centralized hub for data-related tasks, allowing users to organize their work, maintain data integrity, and document processes clearly. Key capabilities: project tracking data cataloging version control integrations with various data tools collaborative documentation Best for: data teams and analysts that need a unified platform to manage data tasks and coordinate efforts.

Atlan Details

Vendor
Atlan
Year Launched
2019
Location
India
Deployment
cloud
Training Options
documentation, videos, live online, in person
Countries Served
All Countries
Languages
English, Spanish, French, German, Italian, Japanese, Dutch, Portuguese, Russian, Chinese
Users
Data Analysts, Data Engineers, Data Scientists, Business Analysts, Data Stewards, Chief Data Officers, Data Governance Managers.
Industries Served
Healthcare, Education, Finance, Retail
Tags
Data and Analytics, Data, Analytics, Data Catalog, Data Governance, Data Lineage, Data Team, Data Culture, DataOps, Data Engineering, Metadata, Active Metadata, and Metadata Management

Atlan's In-App Market Place

Does Atlan have an in-app market place?

Yes

How many Mini-Apps in the marketplace?

0

Mini Apps

Pricing Options

Free trial
Free version
Request a quote
Promo Offer

Accepted Payment Currencies

USD ($), EUR (€), GBP (£), JPY (¥), AUD (A$), CAD (C$), CHF (Fr), CNY (¥), INR (₹)

Pros & Cons

  • 1. Centralized Data Management: Keeps everything in one place, making it easier to manage data across teams.
  • 2. Collaborative Features: Promotes effective collaboration among data teams, ensuring efficient communication and decision-making.
  • 3. Wide Integrations: Integrates well with popular data tools and cloud platforms.
  • 4. Real-Time Data Sharing: Enables teams to access and collaborate on data in real time.
  • 5. Automation: Reduces manual work with automation features for data pipelines and workflows.
  • 1. Learning Curve: Some users may find the platform difficult to navigate initially.
  • 2. Feature Overload: Might be overwhelming for smaller teams who need only basic data management features.
  • 3. Requires Technical Expertise: While designed for collaboration, users may still need a basic understanding of data operations.
  • 4. Complex Setup for Large Organizations: Large teams may require significant configuration to align with specific workflows.

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