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Alation is a data catalog that serves as the foundation for its larger data intelligence platform. Context for data is centralized within the catalog, which feeds into data governance, data lineage, and data analytics capabilities.
While both bill themselves as data intelligence platforms, Collibra is a more traditional platform that is widely used in finTech, and is focused on data governance as their core feature. Alation is a slightly newer platform that focuses on Snowflake integrations and data mesh, with an emphasis on data catalog as their core feature.
Alation and Collibra, at a glance:
Both are data intelligence platforms that help organizations do more with trusted data.
Alation’s strengths lie in rapid implementation and self-serve capabilities
Evaluating data catalog vendors? Use the Data Catalog RFI Template to evaluate options and make a data-driven decision.
Alation is a data catalog and data intelligence solution designed to empower everyone in an organization to find, understand, and trust their data. It serves as a central repository where all data assets of an organization are cataloged, so that data is accessible and understandable to all users, regardless of their technical expertise. The primary goal of Alation is to facilitate a data-informed culture within enterprises.
The core functionality of Alation revolves around its data cataloging capabilities, which are foundational to the greater “data intelligence platform.” The data catalog automatically indexes an organization's data assets, so that they’re searchable and easy to navigate. Users find relevant datasets, reports, and analytics, accompanied by rich context like metadata, usage statistics, and user-generated content like annotations or questions.
Alation promotes collaboration among different teams and users. Through Alation, users can contribute knowledge, share insights, ask questions, and engage in discussions about data. Additionally, Alation incorporates data governance features, enabling organizations to ensure data quality, compliance, and proper management of data policies. Through data governance, organizations can maintain trust and integrity in data usage.
By making data easily accessible, Alation fosters a data-driven culture within organizations. It encourages users to leverage data in their daily decision-making processes, leading to more informed and effective strategies and operations.
In general, Alation is easier to deploy than Collibra and is less complex to operate. Alation minimizes the complexity often associated with setting up and managing data management solutions. Collibra, while user-friendly in its own right, is often seen as more complex due to its comprehensive data governance features. The scope of implementation for Alation is generally narrower compared to Collibra. Alation primarily targets data cataloging and discovery, which can be less complex to implement than a full suite of data governance processes.
Alation has easier implementation when compared to Collibra. See what Alation users think about the platform.
Take a people-centric approach to finding the right people, sharing the right information, and identifying the right initiatives
Curate metadata to offer context and guide compliant use
Centralize policies in Alation Policy Center, grouping them by type, from enterprise-wide to data standards and rules for auditing purposes
Find data stewards based on actual data usage
Align key definitions, rules, and KPIs in the business glossary
Deliver active governance to train models with accurate data
Feed metadata to datasets to support ML model training
Catalog ML Assets and models, including datasets, notebooks and vector databases to benefit ML workflows
Lead a successful cloud migration based on data use and data residency policies
Build a better data environment for future cloud users
Conduct deep impact analysis that identifies downstream process dependencies
Enable column profiling
Deprecation impact visualization on the lineage graph
Impact Analysis and Upstream Audit show impacted data and stakeholders
Surface a range of health metrics to signal data trustworthiness
Set Trust Flags to manually or automatically flag data assets as endorsed, warned, or deprecated
Enrich data fabric with behavior-driven metadata, collecting and interweaving metadata from other data sources
Surface insight details like popularity, search relevancy, usage recommendations with Behavioral Analysis Engine
Turn on bi-directional exchange of metadata
Automatically discover and classify sensitive data
Link data to related and well-defined policies
Control role-based access to sensitive data
TrustCheck highlights associated policies in connected applications, preventing data misuse
Little flexibility for centralized and federated data architectures
Little ability to make mass edits within the interface without another add on product
Underlying infrastructure of the on-premise version is fragile and complicated, with too many modules and technologies
Support is mostly self-serve through Documentation sections and email contact forms
The pricing model is complex
They limit of read-only users
As Alation follows a traditional stewardship approach, their automation can lag behind other competitors
It is difficult to catalog ML models
Without data custodians, a large effort is required from the team to curate
Term tables aren’t in analytics, making it difficult to manage term creation in business glossaries
Doesn’t feature trust badges for BI and analytics tools to supply data health status updates
Needs additional functionality to be able to customize templates
You have to pay to enable column level data lineage, which is key for impact analysis
There is no built in data quality engine
There is limited workflow capability, which can hinder governance tasks
Alation wasn’t built on a knowledge graph, so its accuracy capabilities are limited
Limited integration API for SaaS products
Limited opportunities to set up additional connectors
Focus on cloud could limit on-prem usage
Unable to give lineage with synonyms with Oracle
Offers collaborative data governance utilizing a shared environment
Enhanced data access and data searching capabilities
Poor UX for data mapping
Lacks proper support service
Collibra is a data intelligence platform that empowers organizations to achieve effective data management. At its core, Collibra focuses on data governance, data quality, and data privacy.
Collibra's data governance capability is central to its offering. The data governance platform includes tools for defining and enforcing data policies, standards, and processes. By doing so, Collibra helps organizations ensure compliance with internal policies and external regulations. It also facilitates the establishment of a common data language across the organization, so that the ways in which data is understood and used are standardized.
Collibra also features a data catalog that enables users to discover and understand data assets within the organization. This catalog is enriched with metadata management capabilities, allowing users to annotate and document data assets for better understanding and usage. The metadata includes information about the data's source, content, structure, and relationships with other data assets.
As part of its platform, Collibra provides tools to manage data privacy and ensure that data usage complies with various legal and regulatory requirements. By automating compliance processes and providing clear oversight of how data is used, Collibra helps organizations mitigate data breach and non-compliance risks.
Collibra offers a more comprehensive set of data governance capabilities compared to Alation. See what Collibra users think about the platform.
Automated workflows, processes, and policies for AI governance
Integrate with data and AI infrastructures
Assess feasibility and define the AI use case, including the data and model leveraged and the intended purpose
Comprehensive business glossary
Stewardship management and role assignments
Reconcile data between systems for more accurate reporting and analysis
Centralized policy management
Data helpdesk to raise, manage, and resolve issues
Rich context by connecting business, technical and privacy metadata with quality and column-level lineage
User-friendly search
Preconfigured services
Automatic classification and categorization of physical data assets
Connect to more than 40 databases and file systems
Monitor data quality and data pipeline reliability
Out-of-the-box repository of industry-specific, auto-validation rules
End-to-end lineage mapping across data sources
Native lineage harvesters that source automatically from SQL dialects, ETL tools, and BI tools
Interactive lineage diagram that shows summary lineage from source to destination
Detailed technical lineage at the table, column, transformation and SQL query levels
View direct data flows across data assets as well as indirect relationships
No-code path to write and push policies to the cloud
Leverage metadata and business context to inform who, how and why data should be accessed
Advanced algorithms to classify sensitive data, improve accuracy and save time
Ready-made assessments to assess risk in data processes
Users report a steep learning curve for both users and deployment teams
Understanding the full potential of Collibra's features requires high time investment plus effort on training and familiarization
Collibra is one of the older solutions in the market
Not known for best user experiences, especially for certain personas
Can be unresponsive
With flexibility comes the potential for confusion: a plethora of options and customizable features can overwhelm new users
Users report that Collibra often won’t communicate the product roadmap, so they may be blindsided by changes that impact releases and enhancements
Lacks AI-assisted search and guided research via generative AI
Difficult to power data discovery and analytics
Users report a lack of visualization and reporting capabilities
Non-technical users don’t understand how metadata is structured
Lacks key functions: security administration, connectivity, and user friendliness
Less data observability maturity than the competition
Customers don’t always receive sufficient support when creating custom connections
Asset characteristic changes don't reflect straight away, but adding them again will cause duplications
No chat functionality
Some reviews have noted that transferring data from one system to another system leaves data open to vulnerabilities
Users report that security-wise, other solutions in the market are much better
Collibra is considered a premium solution and can be expensive for smaller organizations
Workflow automation for data stewardship and data governance processes
Robust data dictionary, helping organizations establish a common data language and glossary
Overly complex and difficult to implement
Relatively costly
Steeper learning curve than other competitors in the market
Alation and Collibra leave much to be desired when it comes to managing data. That’s why more and more companies are relying on data.world’s data catalog platform for data management and governance. Data.world was built with a unique architecture, on top of a Knowledge Graph. A Knowledge Graph represents a collection of real-world concepts (displayed as nodes) and relationships (displayed as edges) in the form of a graph used to link and integrate data coming from diverse sources. They bridge the “data-meaning gap,” connecting business terminology and context with data and enabling data access via a commonly understood language. This architecture dramatically improves search, findability, clarity, and accuracy in a data catalog.
Data.world has faster onboarding and more robust data governance when compared to Alation and Collibra.
Check out a full comparison of Alation vs Collibra vs data.world:
No need for your engineering team to configure data queries
Don’t reinvent the wheel on every team with data: see what data people are working with, who owns it, and where it’s coming from
Surface previously unimagined opportunities for improvement
Discover logjams and red flags in the data lifecycle
Make decisions with real-time data for maximum impact
Partner intake and dedicated customer support, from implementation to scaling
Request assistance at any time with installing, configuring, and troubleshooting
Conduct AI-assisted search with our GPT-like bots
Enrich your data automatically
Dramatically reduce the manual human effort to find and understand data
Data.world gives you a complete picture of all your data, both within the platform and across all other integrations
Quickly identify any breach or fraud
Collaborate with data experts and security specialists, to nip data vulnerabilities in the bud
Simple, clear user onboarding
Unify your unique organization, so anyone can understand what all teams are working upon
A data catalog platform that’s tailor-made for your unique technical architecture and data culture
As your data volume grows, the complexity of your data.world instance does not
Data.world’s data catalog platform was built for scale and hockey stick growth
Responds to complex data pipelines and data-driven applications with automations, lineage, and in-app notifications
Evaluating data catalog vendors? Use the Data Catalog RFI Template to evaluate options and make a data-driven decision.
With 2+ million users and counting, data.world is the most-used data catalog on the market today
Built on a Knowledge Graph, data.world’s technical architecture means its data queries are 3x more accurate than the traditional data catalog
Data.world is the only data catalog built on Knowledge Graph architecture, allowing users to review all objects (metadata, tables, documents, etc.) as objects on a graph that have some relationship to each other.
To learn more about why data teams prefer data.world, get a demo today.
