Sold by: Datahub
Deployed on AWS
DataHub vision is to bring clarity to your data through its next-generation multi-cloud metadata management platform. The technology is based on LinkedIn DataHub and Apache Gobblin - two successful open-source projects incubated at LinkedIn and battle-hardened in production at scale at major enterprises.
4.7
Overview
DataHub is an AI & Data Context Platform adopted by over 3,000 enterprises including Apple, CVS Health, Netflix, and Visa. Innovated jointly with a thriving open-source community of 13,000+ members, DataHub's metadata graph provides in-depth context of AI and data assets with best-in-class scalability and extensibility. The company's enterprise SaaS offering, DataHub Cloud, delivers a fully-managed solution with AI-powered discovery, observability, and governance capabilities. Organizations rely on DataHub solutions to accelerate time-to-value from their data investments, ensure AI system reliability, and implement unified governance - enabling AI & data to work together and bring order to data chaos.
For Data Analysts, developers, data scientists, and automated workflows:
Easily find trusted datasets with the most current data
- Access data where you work with a chrome extension for BI tools
- Discover data your way - personalization for multiple business and technical user profiles
- Support AI models and automations with a metadata graph that keeps up with today's data volume and velocity
- Understand data provenance with table, column, and job level lineage graphs
- Auto-enrich metadata with no-code automation
- Use AI-generated documentation and propagation to better understand context
- Always stay up-to-date with subscriptions to assets, activity and notifications
For Data Engineers:
Deliver reliable data quality
- Provide end-to-end observability with user-created data quality checks and reports
- Surface data quality results and impact analysis across all points in lineage
- Monitor freshness SLAs, data volume, table schemas, column quality, and custom SQL
- Use AI Anomaly Detection for freshness, volume, and column stats
- Easily keep an eye on data quality with assertions and AI-based smart assertions
- Evaluate data contracts and quality checks on-demand with API
- Get notified where you work (slack, email, and more)
- Easily manage data quality with a data health dashboard
For Data Governance:
Ensure continuous AI & data governance in production versus episodic compliance checks
- Ensure every AI & data asset is accounted for by defining and enforcing documentation standards
- Integrate governance practices early with automated shift-left governance
- Automatically classify your data as it moves and transforms with lineage-driven compliance
- Keep tags harmonized with seamless metadata flow between DataHub and source systems
- Deliver continuous compliance monitoring with forms, impact analysis, and reporting
- Create and implement bespoke compliance approval workflows
Highlights
- Search All Corners of Your Data Stack- DataHub's unified search experience surfaces results across databases, data lakes, BI platforms, ML feature stores, orchestration tools, and more.
- Trace End-to-End Lineage- Quickly understand the end-to-end journey of data by tracing lineage across platforms, datasets, ETL/ELT pipelines, charts, dashboards, and beyond.
- View Metadata 360 at a Glance- Combine technical, operational and business metadata to provide a 360 degree view of your data entities.Generate Dataset Stats to understand the shape & distribution of the data.
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Discover & Govern | Up to 20 Monthly Active Users | $75,000.00 |
Vendor refund policy
All fees are non-cancellable and non-refundable except as required by law.
Legal
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
Email support is offered Monday - Friday during regular business hours.
marketplace@datahub.com
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Datahub
By Alation
By Collibra
Top
10
In Data Catalogs
Top
10
In Data Catalogs, Data Governance, Master Data Management
Top
10
In Data Catalogs, Data Governance
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Metadata Management
"Multi-cloud metadata management platform with comprehensive metadata graph for AI and data assets"
Data Lineage Tracking
"End-to-end lineage graphs tracking data journey across platforms, datasets, pipelines, charts, and dashboards"
Data Quality Monitoring
"Automated data quality checks with AI-powered anomaly detection for freshness, volume, and column statistics"
Search Capabilities
"Unified search experience across databases, data lakes, BI platforms, ML feature stores, and orchestration tools"
Governance Automation
"Automated lineage-driven compliance with continuous governance monitoring and documentation standards enforcement"
Metadata Management
Centralized metadata aggregation from multiple disparate data sources with unified platform capabilities
Behavioral Analysis
Advanced engine for analyzing data usage patterns, interactions, and metadata insights
AI Governance Framework
Comprehensive system for tracking data lineage, ensuring data quality, transparency, and compliance for AI models
Search and Discovery
Automated platform enabling comprehensive search, description, and understanding of data assets including reports and models
Architectural Extensibility
Open and flexible architecture supporting integration across different data environments and platforms
AI Governance
Provides active metadata-driven governance framework for AI strategies with rules, processes, and responsibilities to mitigate risks and ensure ethical AI practices
Data Catalog Management
Enables comprehensive discovery and understanding of data assets across hybrid and multi-cloud environments with full business context and metadata insights
Automated Data Lineage
Offers end-to-end lineage tracking with complete transparency into data transformation and flow across systems, including summary-level and technical lineage details
Privacy Workflow Automation
Centralizes and automates privacy workflows to address global regulatory requirements and encourage collaborative data protection
Data Quality Management
Replaces manual processes with automated data monitoring and rule management to scale data quality across enterprise environments
Standard contract
No
No
Customer reviews
reviewer2784462
Centralized metadata has empowered governed data discovery and clarified ownership for all teams
Reviewed on Dec 27, 2025
Review from a verified AWS customer
What is our primary use case?
We adopted Data Hub in the context of a large enterprise customer operating in a regulated industry with a strong focus on data governance, data discoverability, and ownership clarity across multiple cloud-native platforms. The solution was deployed on AWS , and the main business problem was the lack of a centralized, reliable view of data assets, including poor data discoverability, unclear data ownership and stewardship, limited lineage visibility across ingestion and transformation layers, and high dependency on tribal knowledge held by a few individuals. Data Hub was selected as an enterprise data catalog and metadata backbone with the goal of enabling both technical teams and business users to easily understand, trust, and reuse data.
We used Data Hub to create very good data discoverability, assign data ownership and stewardship, improve data quality processes, and establish good data governance for our customer in terms of data catalog, data lineage, and metadata management in general.
What is most valuable?
Our key benefits that we achieved include centralized metadata management across multiple AWS services and data platforms and improved data discoverability, significantly reducing the time required to find relevant data sets. Clear data ownership and stewardship improved accountability and collaboration between teams. End-to-end lineage visibility enabled faster impact analysis and safer changes, and faster onboarding of new data users through self-service access to documentation and metadata. From a governance perspective, Data Hub became a single source of truth for metadata, supporting both compliance requirements that are very important in a data governance environment and day-to-day operational needs.
The main strengths we experienced with Data Hub are a strong metadata model and its extensibility because Data Hub offers a rich and flexible metadata model that adapts well to complex enterprise scenarios. Excellent lineage capabilities are provided because the lineage visualization is clear, actionable, and extremely useful for impact analysis and governance workflow. The open source foundation with enterprise readiness is significant because the open architecture avoids vendor lock-in while still being suitable for production-grade environments.
Data Hub is very effective for us because we build the data lineage from the beginning, from origination to visualization, to the final use of the data. We follow and track a path of the data, which improves analysis and enables us to find where data is used and the impact of deleting data. This is also very important in a regulatory environment.
What needs improvement?
The impact is very positive, and there are many benefits for us using Data Hub because it was easier to make data governance, create centralized metadata management, improve data discoverability, and manage data in general. The areas for improvement, in my opinion, are the initial setup and configuration that can be complex without prior experience, especially in large-scale environments. User experience for non-technical users could be further simplified, particularly around advanced metadata concepts. The out-of-the-box governance workflow, for example, approvals and certification, could be more prescriptive for customers at early maturity stages.
Data Hub can be improved in the initial setup and configuration that is somewhat complex, and also in operational monitoring that could benefit from more native dashboards and alerts. However, these are not blockers, but areas where additional guidance or product enhancement would further accelerate adoption.
For how long have I used the solution?
I have been using Data Hub since 2023.
What other advice do I have?
Based on internal measurement and feedback from the data teams, there are many impacts. Time to locate and understand a data set was reduced by approximately 40-50 percent. Manual documentation effort was reduced by around 40 percent. Dependency on senior data engineers for data explanation dropped significantly. Data onboarding time for new team members decreased from weeks to days.
I would rate this product a 9 out of 10. I chose nine because Data Hub proved to be a robust, scalable, enterprise-ready data catalog that is well-suited for AWS-based architecture and complex organizational environments. It is always possible to improve and useful to maintain space for further optimization.
My advice is to use Data Hub to move from fragmented metadata and manual processes to a modern, governed, and self-service data ecosystem, delivering clear value in terms of efficiency, cost saving, and data trust. We would confidently recommend Data Hub to organizations looking to improve data governance, data discovery, and metadata management on AWS.
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?
Amazon Web Services (AWS)
reviewer2784771
Analytics work has become more efficient and now processes large datasets with flexibility
Reviewed on Dec 04, 2025
Review from a verified AWS customer
What is our primary use case?
My main use case for Acryl Data is analytics.
What is most valuable?
Acryl Data helps with processing large amounts of data as it is a very good tool that gives good flexibility to store a huge amount of data and is easier to use. The UI is good.
The best features Acryl Data offers include storage. When I mention storage, I refer to its scalability.
The positive impact of Acryl Data is that it has increased efficiency.
What needs improvement?
I do not have comments on how Acryl Data can be improved.
For how long have I used the solution?
I have been using Acryl Data for two years.
What do I think about the stability of the solution?
Acryl Data is stable.
What do I think about the scalability of the solution?
Acryl Data's scalability is good.
How are customer service and support?
The customer support is good.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
I did not previously use a different solution.
How was the initial setup?
My experience with pricing and setup was good.
What was our ROI?
I have seen a return on investment as it has saved time.
Which other solutions did I evaluate?
Before choosing Acryl Data, I did not evaluate other options.
What other advice do I have?
My advice to others looking into using Acryl Data is that they can use it. I gave this product a rating of 9.
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?
reviewer2784384
Simple data insights platform has boosted development speed and revealed top purchasing customers
Reviewed on Dec 04, 2025
Review from a verified AWS customer
What is our primary use case?
My main use case for Acryl Data is to extract insights from customer data. I use Acryl Data for a project in order to identify all the customers and find out which customer buys a lot of items.
What is most valuable?
The best feature Acryl Data offers is the simplicity of the UI. The UI is simple for me because it is easy to navigate. Acryl Data has positively impacted my organization by speeding up all the development. It sped up development because the team can access data faster, improving speed by approximately 50%.
What needs improvement?
The product cannot be improved in just one area. There are no points in support or documentation that require improvement. There are no improvements needed for Acryl Data that I have not mentioned yet.
For how long have I used the solution?
I have been using Acryl Data for five months.
What do I think about the stability of the solution?
Acryl Data is stable.
What do I think about the scalability of the solution?
I think the scalability of Acryl Data is a good point.
How are customer service and support?
The customer support is fine; we do not need any customer support, but I think it was fine.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
I did not previously use a different solution; I have no experience with any other solutions.
What was our ROI?
I have seen a return on investment through time saved and also money saved. I do not have specific numbers or examples about the time or money saved.
Which other solutions did I evaluate?
I did not evaluate other options before choosing Acryl Data; I evaluated only this option.
What other advice do I have?
My advice to others looking into using Acryl Data is to start faster with the analytic insights. I would rate this product a 10.
Which deployment model are you using for this solution?
Public Cloud