DataOS
DataOS, developed by The Modern Data Company and headquartered in Palo Alto, United States, is positioned as the first true data operating system on the market, designed from day one to be AI-native.
Publisher review
DataOS, developed by The Modern Data Company and headquartered in Palo Alto, United States, is positioned as the first true data operating system on the market, designed from day one to be AI-native. It targets data teams and enterprises seeking to build, deploy, and manage data products at scale while eliminating the complexity of fragmented data stacks. By acting as an operational layer that integrates with existing legacy systems rather than replacing them, DataOS aims to democratize data product development and reduce confusion around data definitions and ownership, making it suitable for organizations that need to modernize without rip-and-replace investments.
DataOS works by connecting fragmented systems into a governed, unified platform without moving data. Its key capabilities include a Data Depot for connecting data sources without data movement, a built-in lakehouse called Icebase that delivers ACID compliance for both structured and unstructured data, and an Automatic Data Catalog that provides real-time updates. The platform features a Knowledge Graph for real-time enrichment of metadata and lineage, Data as Software with versioning and data replay, and Data Quality tools with validation and profiling checks. Governance is enforced through Attribute-Based Access Control (ABAC) for flexible, scalable policies, while a Workbench offers low-code SQL querying for business users. Additionally, DataOS includes Observability for monitoring data health and performance, and a Live AI Scorecard that assesses data stack readiness for AI in three minutes.
In the market, DataOS competes with AWS Glue, FME, Swash, DataForSEO, MFour, Traject Data, and Global Source Data Solutions, but distinguishes itself by claiming to be the only holistic data operating system rather than a patchwork of products. Unlike competitors that require complex integration steps and often lead to pipeline weakening, DataOS is pre-integrated and composable out of the box, simplifying the data stack and eliminating integration worries. It is recognized as the Top Data Product Platform by CIO Review and is trusted by Fortune 500 companies, emphasizing a paradigm shift in how companies interact with data assets.
Honest trade-offs include a lack of publicly listed pricing specifics, requiring potential customers to contact sales for detailed quotes, which may hinder initial evaluation. The platform's focus on being AI-native may introduce complexity for teams not yet ready for AI-driven workflows. While DataOS integrates with legacy systems, organizations with heavily customized or niche legacy environments may still face integration challenges. Additionally, the emphasis on governance and ABAC could require upfront policy definition and maintenance effort, potentially slowing initial deployment for teams without mature governance practices.
How it works
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Observability
Monitors data health and performance to enhance reliability, providing real-time insights into data pipeline operations.
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Data Depot
Connects data sources to DataOS without moving data, enabling value extraction directly from source systems.
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Data as Software
Enables versioning and data replay, allowing data to be used and deployed with software-like lifecycle management.
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Data Quality
Ensures trustworthy data through validation and profiling checks, supporting high-quality insights.
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Data as a Product
Converts raw data into consumable data products with dictionary, schema evolution, lineage, impact, profiling, usage, and quality.
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Knowledge Graph
A semantic network enriched in real-time with metadata and lineage to reflect complex relationships between data sets.
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Icebase Lakehouse
Built-in lakehouse delivering ACID compliance, reliability, and performance for both structured and unstructured data.
Strengths and trade-offs
Strengths
- Empowers data teams to build, deploy, and manage data products at scale with a unified platform.
- Democratizes data product development by providing low-code access through the Workbench SQL query engine.
- Enforces governance through ABAC, enabling flexible and scalable policies that adapt to changing compliance regulations.
- Accelerates value delivery by abstracting operational complexity and integrating cleanly with legacy systems without rip-and-replace.
Trade-offs
- Pricing details are not publicly listed, requiring direct contact with sales for both Pay as You Go and Enterprise models.
- The AI-native focus may introduce unnecessary complexity for teams not yet ready for AI-driven data workflows.
- Organizations with heavily customized legacy systems may still face integration challenges despite clean integration claims.
- Upfront policy definition and maintenance for ABAC governance can slow initial deployment for teams without mature governance practices.
Pricing context
Two models: Pay as You Go with no upfront fees and pay-per-use; Enterprise with yearly subscription and no usage limits. Exact figures require contacting sales.
Getting started with DataOS
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Sign up for DataOS
Visit the DataOS website and click the Get Started button. Fill in your company details and contact information. A sales representative will reach out to provision your tenant and provide login credentials.
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Connect your data sources
Log into DataOS and navigate to the Data Depot. Select your source system from the list of supported connectors. Provide connection details such as host, port, and credentials. DataOS will connect without moving your data.
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Define governance policies
Go to the Governance section and create Attribute-Based Access Control (ABAC) policies. Specify user attributes and data attributes to control access. Test policies with a sample dataset to ensure correct enforcement.
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Create a data product
Use the Workbench to write a SQL query that transforms raw data into a consumable dataset. Save the query as a data product, enabling versioning, lineage, and quality checks. Publish it to the catalog for team access.
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Schedule observability checks
Open the Observability module and configure monitors for data health and pipeline performance. Set up alerts for anomalies or failures. Run a test to verify that notifications reach your team's communication channel.
Frequently Asked Questions
What is DataOS and how does it work?
DataOS is an AI-native data operating system from The Modern Data Company that connects fragmented data systems into a governed, unified platform without moving data. It integrates with existing legacy systems to simplify data product development and management at scale.
What are the key features of DataOS?
Key features include a Data Depot for connecting sources without data movement, Icebase lakehouse with ACID compliance, an Automatic Data Catalog, Knowledge Graph for metadata enrichment, Data as Software with versioning, Data Quality tools, ABAC governance, and a low-code Workbench for SQL querying.
How does DataOS pricing work?
DataOS offers two pricing models: Pay as You Go with no upfront fees and pay-per-use, and Enterprise with a yearly subscription and no usage limits. Exact pricing details are not publicly listed and require contacting sales for a quote.
How does DataOS integrate with legacy systems?
DataOS acts as an operational layer that integrates with existing legacy systems without requiring rip-and-replace investments. It connects fragmented systems into a unified platform, though organizations with heavily customized legacy environments may still face integration challenges.
What governance capabilities does DataOS provide?
DataOS enforces governance through Attribute-Based Access Control (ABAC), enabling flexible and scalable policies that adapt to changing compliance regulations. It also includes an Automatic Data Catalog and Knowledge Graph for real-time metadata and lineage tracking.
Who are DataOS's main competitors?
DataOS competes with AWS Glue, FME, Swash, DataForSEO, MFour, Traject Data, and Global Source Data Solutions. It distinguishes itself as a holistic data operating system that is pre-integrated and composable, unlike competitors requiring complex integration steps.
Alternatives
How DataOS compares
Direct head-to-head against 3 competitors. Picked by 7wData.
DataOS
- Pricing
- Two models: Pay as You Go with no upfront fees and pay-per-use; Enterprise with yearly subscription and no usage limits. Exact figures require contacting sales.
- Target
- DataOS, developed by The Modern Data Company and headquartered in Palo Alto, United States, is positioned as the first true data operating system on the
- Strength
- Empowers data teams to build, deploy, and manage data products at scale with a unified platform.
- Watch for
- Pricing details are not publicly listed, requiring direct contact with sales for both Pay as You Go and Enterprise models.
AWS Glue
- Pricing
- Pay-as-you-go, custom enterprise pricing
- Target
- Cloud-native data integration
- Deployment
- Cloud
- Strength
- Seamless AWS ecosystem integration
- Watch for
- Steep learning curve for advanced features
FME
- Pricing
- Custom pricing, subscription-based
- Target
- Geospatial data workflows
- Deployment
- On-prem, cloud
- Strength
- Robust geospatial data transformation
- Watch for
- Complex setup for non-geospatial use cases
Informatica PowerCenter (Legacy)
- Pricing
- Custom enterprise pricing
- Target
- Enterprise ETL workflows
- Deployment
- On-prem, cloud
- Strength
- Reliable ETL for large-scale data
- Watch for
- Legacy product, limited innovation
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Sources
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