Atlan alternatives For data-driven organizations

Atlan is a modern data collaboration platform that streamlines how teams work with data. It combines data cataloging with tools like SQL Editor and notebook environments to provide a unified view of data assets and pipelines and help downstream consumers across their modern data stack.

Atlan integrates various aspects of enterprise data, from data discovery and governance to quality management and compliance — enabling teams to maintain high productivity and data coherence across an organization.

Many businesses choose Atlan as their data catalog because it provides an "active metadata" layer that keeps metadata fresh by automatically ingesting it as data workloads are executed. Atlan also provides an easy-to-use interface that encourages wider adoption across non-technical teams.

Evaluating data catalog vendors? Use the Data Catalog RFI Template to evaluate options and make a data-driven decision.

Although many businesses prefer Atlan’s data catalog software because of its intuitive interface and collaborative environment, Atlan also has several drawbacks. For example:

• AI and machine learning capabilities can only be accessed after paying additional fees

• Rigid data governance automation and workflows that are difficult to customize to specific business needs

• No centralized governance task management or notification system for users to track and complete assigned data reviews and approvals

• Lack of streamlined DataOps communication features like pipeline monitors to collaborate on data pipeline issues

• Limited ability to create and customize data lineage visualizations beyond basic lineage views

• Resource-intensive to set up and maintain metadata ingestion for complex data environments

• Weak metadata search capabilities

• Complicated access control and permission management for multiple personas, user groups, and user types

In summary, Atlan may not be the best data catalog solution for those who are looking for super robust features with extensive customization options or frequent documentation updates.

While Atlan has its strengths, Atlan’s customers also mentioned drawbacks when compared with other data catalog solutions on the market. Here are some of the top data catalog alternatives to Atlan.

data.world is a cloud-based data catalog and management platform that uses AI to simplify data governance. Built on a knowledge graph architecture, it allows users to collect metadata from any source and create a visual map of their entire data system. This functionality enables users to search through their metadata in real-time, facilitating better-informed decision-making.

data.world continues to grow in popularity due to the extent of its capabilities. For example, where Atlan struggles to provide automation solutions, data.world utilizes AI to automate manual tasks, including metadata enrichment, ultimately saving companies a lot of time and resources. And when tasks cannot be automated, data.world provides plenty of tools like AI-assisted data search and guided data exploration, all personalized to fit your business needs. That’s why companies continue to transition to data.world for data governance… just look at what customers say about data.world!

Moreover, data.world aims to simplify your data governance processes while providing you with all the tools and support necessary to grow your business and handle exponential increases in data volume. Schedule a free demo today and learn how a partnership with data.world will transform your company’s data governance process.

Overall, data.world outranks Atlan with its knowledge graph architecture, model accuracy, unified data view, cross-platform querying, and utilization of AI technology. data.world uses their Archie Bot, Eureka Bot, and BB Bot to create workflows and efficiencies on top of your datasets. Meaning anyone, not just data analysts, can query it to get answers or perform tasks.

Check out a full comparison of data.world vs. Atlan.

Pros

• Backend API and SPARQL features enabling users to quickly and easily generate reports 

• Intuitive user interface with drag-and-drop features for non-technical users

• Backed by a flexible graph database

• Scalable solutions, regardless of database size or volume

• Elimination of repetitive tasks using AI to reduce the risks of human error

• Automatic data context enrichment and AI-assisted search algorithms

• Easy-to-use ontology-based notation

• Prompt customer service for any occasion, whether it is installation, configuration, or general troubleshooting

Cons

• Initial setup and data integrations can be difficult to navigate for some users

• Complex JSON integrations

Knowledge graph architecture

• Query accuracy 3 times greater than conventional data catalogs like Atlan

  Facilitating diverse data format integrations with automatic discovery of relationships between them

• Context-rich answers to questions, not just a list of query results

• Visual map of an organization’s data workflows and data architecture

• Automatic organization of data, enabling the elimination of manual tasks and reducing the likelihood of human error

• Supporting workflows requiring multiple rounds of approvals and eliminating the need for advanced coding skills

Ready to transform your company’s data governance? Download Data Discovery Without Limits and learn how knowledge graphs enable data catalog users to explore beyond traditional data and metadata boundaries.

AI and automation

• data.world uses three AI bots to simplify data governance - Archie Bot, Eureka Bot, and BB Bot

• Archie Bot uses generative AI to simplify data search and discovery. It functions like a data analyst by providing analytical insights, search and query assistance, and automation of data workflows

• Eureka Bot automates metadata management, provides simple access workflows, and creates agile processes to streamline data governance

• BB Bot creates seamless communication between data teams and data consumers to improve DataOps efficiency and data quality

Agile data governance

• Automated data enhancement. No manual input. No complicated processes

• Easily customizable data workflows to improve productivity across an organization

• A complete top-down picture of an organization’s data ecosystem and data pipelines

Streamlining DataOps

• A centralized hub for data producers and consumers to collaborate and share real-time updates

• AI automation to reduce time spent on repetitive communication and cataloging activities

• Real-time visibility into an organization’s data supply chain with column-level lineage across its entire modern data stack

• Team collaboration and productivity at an enterprise level

Cloud data migration

• A holistic view of organizational data governance systems, securely stored and maintained on the cloud

• Better search and discovery performance that can capture, enhance, and utilize metadata in real-time

• Assignment of migration tasks to other users and elimination of data silos through transparent collaboration

Collibra is a cloud-based data intelligence platform that improves data understanding, governance, and use. With Collibra, businesses can comprehensively view their data assets, manage data quality, adhere to regulatory compliance, and derive meaningful insights.

Many businesses use Collibra because of its centralized system. By leveraging its data catalog, companies can align their data management practices with their strategic objectives, ensuring data asset accuracy, accessibility, and security.

However, Atlan outranks Collibra when it comes to customer experience and support. Overall, Atlan has better compatibility and a smoother implementation process across systems. Collibra's customers also prefer Atlan's evaluation and contracting phase as it has a more simplistic and straightforward pricing model.

While both are reputable data intelligence platforms, Atlan surpasses Collibra in search querying, data integrations, pipeline monitoring, and AI capabilities.

Pros

• Automatic data cataloging, lineage, quality, and governance

• Highly customizable platform

• Automatic capture and sorting of essential metadata 

Cons

• Search features don't narrow results by specific table, column, or schema names

• Complicated onboarding process with a hefty learning curve

• No queryable data features

• Lack of a system to indicate data reliability, quality, and trustworthiness

• Missing AI tools to assist in query search

• AI governance: Automated workflows, rules, and processes for simplified AI management across an organization

• Data catalog: Tools to help users discover, understand, and classify relevant data assets

• Data governance: Automated data governance workflows and processes enforcing governance policies and standards 

• Data lineage: Visual map of all relationships between systems, applications, and reports, providing a context-rich view of data across an entire organization

• Data quality and observability: Predictive data quality capabilities to ensure data compliance standards

System security: Risk mitigation for data access associated with the discovery, protection, and definition of newly integrated data

Alation is a data governance platform that helps organizations manage and derive value from their data assets. It provides a centralized repository for data discovery, collaboration, and governance. 

Businesses mostly like Alation for its simplicity. The interface is straightforward and user-friendly, yet it facilitates complex functions like visualizing your data lineage, tracing data origins, indexing data assets across your organization, and transitioning data to cloud platforms. Overall, Alation aims to maximize an organization's return on its data investments.

One of Alation’s main drawbacks is its onboarding process. Atlan is easier to use, set up, and administer than Alation. Whether it's ease of use or availability of customer support, Alation's customers tend to rate it lower in these categories than Atlan.

Pros

• Users can connect related business concepts and objects hierarchically to see the bigger picture.

• Extensive customization options, like easy-to-build custom fields, machine learning (predictive analytics), and a built-in SQL Editor. 

• Filter options on the search feature to simplify the process of finding specific data stacks.

Cons

• Data lineage UI is difficult to understand, even for advanced users.

• Term tables are not available and creating terms across different business glossaries is complicated.

• The Business Intelligence (BI) section is especially confusing. For example, Tableau dashboards are listed as folders.

• Compose: Intelligent SQL query editor with auto-suggestions, collaborative features, and integrated data governance

• Data catalog: Centralized data knowledge to simplify discovery and ensure the use of only trusted data

• Data governance: Enforcing data policies and data compliance over an entire organization

• Data lineage: Visualization of data origins, transformations, and dependencies

IBM Watson Knowledge Catalog is a cloud-based data catalog and centralized governance solution offered by IBM. With an established centralized repository of data assets, it simplifies locating, accessing, and analyzing data from various sources. It also provides data profiling, lineage, and classification capabilities to help organizations understand their data's context, quality, and sensitivity.

Businesses are primarily drawn to the IBM Watson Knowledge Catalog because of the extent of its capabilities. For example, its cloud-based platform activates information for AI and machine learning supported by active metadata. So, as time goes on, these models can learn from your organization’s data and begin to make predictive analyses, which can be especially useful for companies with a lot of data.

Users of IBM Watson Knowledge Catalog and Atlan say that both solutions meet their expectations to a certain extent, albeit in different ways. Atlan is considered a more holistic solution suitable for companies of any size, whereas IBM is more complex and primarily geared towards larger enterprises with substantial data needs. Likewise, when it comes to ease of use, search and discovery, and the quality of ongoing product support, IBM Watson Knowledge Catalog's customer ratings are lower than those of Atlan’s. 

All in all, while both are good data catalog solutions, Atlan seems to outrank IBM in terms of performance and customer satisfaction.

Pros

• Intuitive user interface

• Auto-saving of data assets

• Automatic privacy assessments to mitigate risk and protect sensitive data

Cons

• Cloud-based architecture significantly reduces platform speed

• Difficult to integrate with external applications like Slack

• Overpriced as compared to similar tools on the market

• Automatic data governance: Streamlining of data discovery, quality management, lineage, and protection to save organizational time and resources

• Data protection and compliance: Sensitive data masking to ensure regulation compliance and safety

• A complete view of organizational data: Automatic matching of data from external sources to existing customer profiles to give a full view of critical data entities within an organization

Google Cloud Data Catalog, a metadata management service within Dataplex, is a centralized catalog for data governance. It functions just like a regular data catalog for capturing technical and business metadata and uses it to build an inventory of data assets for your company. It also indexes data from other Google Cloud sources like BigQuery and Cloud Storage so businesses can utilize and manage their data to make well-informed decisions.

Many businesses choose the Google Cloud Data Catalog because it simplifies data discovery with a familiar search interface and provides a flexible system for adding business context through tags and templates.

Google Cloud Data Catalog users like the platform because of its metadata search and discovery capabilities. Likewise, many users argue that its main functions offer a more holistic solution to data governance than Atlan. For example, Google Cloud Data Catalog offers natural language querying, ML recommendations, and automatic data cleansing that outperform traditional data governance platforms.

Pros

• Enabling authorized users to search and tag data entries with specific metadata

• Improved visibility and team collaboration through elimination of data silos

• Centralized location for all metadata, regardless of the source

Cons

• Unintuitive interface, making it difficult to interact with IAM roles

• Time-consuming setup and data integration

• Data discovery: Classification and automatic enrichment of structured, semi-structured, and unstructured data to enhance data discovery

• Integration with Google Cloud services: Seamless integration with other Google Cloud services, such as BigQuery, Cloud Storage, and Dataproc, enabling metadata management across an entire data ecosystem

• Metadata search access: Restriction of metadata search access so users can only search for data entries they already have access to

• Column-level security: Integration of column-level security for BigQuery tables to save company time

• Advanced search function: Built-in search interface that uses the same search technology as Gmail to simplify data discovery

Secoda is an AI-powered data search and cataloging platform. It helps businesses understand and utilize their data by creating a searchable catalog of their data assets. Secoda also builds "lineage maps" that show how data flows through companies’ systems, making tracking changes and improving data governance easier compared to traditional data catalog solutions.

Many businesses turn to Secoda as an alternative to Atlan because it simplifies data exploration for everyone in the company, regardless of technical background, while ensuring all users can access accurate and reliable data.

While Secoda and Atlan are both great data catalog solutions, Secoda has a leg up on Atlan when it comes to user experience. With unlimited viewers, built-in charting, and a simple setup process, Secoda is dedicated to simplifying data governance.

Secoda's customers particularly favor ongoing support systems compared to those of Atlan. However, for those prioritizing staying ahead of the curve with new features, Atlan is a better fit based on its development roadmap.

Pros

• Budget-friendly and popular among smaller organizations

• Responsive customer support team

• Comprehensive tool documentation making onboarding easier

Cons

• Users often run into bugs during data integration processes

• Insufficient real-time collaboration features for teams

• Learning curve for new users

• Comprehensive data pipeline observability: End-to-end visibility into the health and performance of the entire data infrastructure

• Streamlined data tracking: A single searchable repository for managing and tracking all data requests, documentation, and metadata

• Automation capabilities: A suite of automation tools for data teams to scale manual tasks and processes

• Integrated analytics workspace: Queries, charts, data dictionaries, wikis, and applications are consolidated into a single platform to streamline ad-hoc data analytics workflows

Evaluating data catalog vendors? Use the Data Catalog RFI Template to evaluate options and make a data-driven decision.

Book a free demo with data.world and see how our AI-powered and user-friendly platform can take your data management to the next level.

data.world doesn't just simplify data governance. It will sort all your data privacy concerns while providing you with the best tools on the market to save time and resources.

Schedule a free demo today to see how powerful your data catalog can be.