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Compare 7 data governance tools on catalog reach, quality engine coverage, observability and price, with every limit cited to the vendor documentation it came from.
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A data governance tool is easy to shortlist and hard to compare, because every vendor describes the same feature set in the same words. So this list is ordered on one thing: how much of the governance job arrives in a single product, at a price you can see, without a second purchase for the part that tells you the data is wrong.
Every limit named in this article was read from that vendor's own documentation on 6 September 2026 and is listed at the end with the page it came from. Where a marketing page and a documentation page disagreed, the documentation is what we used. We did not reuse any research firm number that we could not fetch and read ourselves.
Demand for these tools is real and it is recent. Precisely and Drexel University's LeBow College of Business surveyed more than 550 data and analytics professionals for their 2025 Outlook report, published in December 2024, and found that 71 percent of organizations said they had a data governance program, against 60 percent a year earlier. Precisely also sells one of the tools covered further down this page, which is worth knowing when you read its survey. The number that matters more for a buyer is the second one: having a program is not the same as being able to prove data is correct, which is why data quality and data observability now sit inside governance evaluations rather than beside them.
If a lean data team has to run catalog, lineage, quality and observability together and needs a price before a procurement cycle, start with Decube. If a governance function already exists with its own budget and headcount, and policy depth outranks time to value, Collibra is the incumbent for good reason. If the estate is entirely on Azure and Microsoft Fabric, Purview costs least and integrates hardest. If the analytics team owns the decision and search experience is the thing being bought, Alation. If the stack is Snowflake, Databricks or BigQuery and you want quality rules executing inside the warehouse, Atlan. If you need data quality on premise as well as in the cloud, Ataccama. If the requirement spans dozens of legacy sources and you are already an Informatica customer, Cloud Data Governance and Catalog.
| Tool | Native quality engine | Where the quality engine runs, per vendor documentation | Native observability | Pricing model |
|---|---|---|---|---|
| 1. Decube | Yes | Sources connected to the platform. Plans cap sources at 3, 10 or unlimited. The per connector list for the quality engine is not published, so ask for it. | Yes | Published per user pricing from 175 US dollars per user per month |
| 2. Collibra | Yes | Sources reachable through Edge with the Data Quality Pushdown Processing capability. The application carries its own license key. | Yes | Modular. Data quality is licensed separately from the catalog |
| 3. Alation | Yes | 9 listed platforms, on Alation Cloud Service instances with the New User Experience. Oracle support is not enabled by default. | Yes | Data quality is a separately purchased feature |
| 4. Atlan | Yes | BigQuery, Databricks and Snowflake, with rules executing natively in each warehouse. | Partial | Platform subscription |
| 5. Informatica | Yes | Sources onboarded to the Intelligent Data Management Cloud. | Partial | Consumption based, described on its product page as pay only for what you use |
| 6. Microsoft Purview | Yes | Sources in the same Azure region as the Purview account. Managed identity is unavailable for Azure Databricks, Snowflake and Google BigQuery. | Partial | Pay as you go, billed with the Azure account |
| 7. Ataccama | Yes | Sources connected to Ataccama ONE across on premise, hybrid and cloud environments. | Yes | Quotation |
Two columns in that table are the ones to argue about. The third column is where a shortlist usually breaks, because it is the only place the products differ in a way you can check before you buy. The fourth is where existing comparisons of these tools are simply out of date, and the sections below say exactly what each vendor now documents.
Decube is a data trust platform built for regulated data teams. The data governance module classifies sensitive data and PII automatically against policies you define, or lets a steward tag an asset by hand in the catalog. Every change and every access request routes through an approval workflow, so an asset cannot quietly change owner or permission. Access is granted at field level rather than whole source level, through role based permissions and group membership, and the activity log records who read what and when.
On the quality side, Decube ships 12 test types covering both no code checks and custom SQL, with dynamic thresholding so a monitor adapts to the shape of the data instead of holding a number someone typed in a year ago. Tests can be configured in bulk and alerts are grouped rather than fired one per row. Observability is native in the same product: pipeline health, freshness, volume, schema change detection and machine learning based anomaly detection, with no third party monitoring tool underneath. Column level lineage runs across systems with a governance controlled approval flow on lineage changes themselves, which is unusual, and it is one of the two things Decube names as its own differentiator.
The results are published rather than described. Decube lists five customer stories at decube.io/case-studies, and every number in them belongs to a named organization type. A Nordic energy company onboarded more than 500 users and saves roughly 10 hours a week on metadata work. A fintech in Mexico removed around 400 hours of manual data work a month and cut regulatory reporting cycle time by more than half. A Nasdaq listed regional bank in the United States saves 110 hours a week. An Australian financial institution saves more than 15,000 US dollars a week on observability and governance effort. An Indonesian digital bank reached 95 percent column level lineage coverage and cut incident resolution time by 55 percent. Those five are the complete published set, and there is not a healthcare story among them.
Pricing is on the pricing page, which is rarer in this category than it should be. Starter is 175 US dollars per user per month, from 21,000 US dollars a year with a minimum of 10 users, and covers up to 3 data sources and 1,000 monitors. Growth is 225 US dollars per user per month, from 54,000 US dollars a year with a minimum of 20 users, and covers up to 10 sources and 3,000 monitors. Enterprise is on quotation with unlimited sources and monitors, private cloud deployment, an SLA and audit logs. Extra monitors beyond the plan cap are 0.59 US dollars each with no minimum commitment. Metadata management, automated lineage, schema drift detection, business glossary, API access, SSO and RBAC are on every plan.
Decube holds SOC 2 and ISO 27001, is HIPAA and GDPR compliant, and encrypts data in motion with TLS and at rest with AES-256. Its governance page names OJK, BNM, MAS and APRA as the regulatory frameworks its customers operate under, which is a set almost no competitor targets directly.
The honest limits. The plan caps mean a large estate lands on Enterprise quickly, and the integrations page groups connectors into categories without publishing a per connector list for the quality engine, so a buyer with an unusual source should ask for that list in writing before signing. Decube also does not claim to beat Atlan on column level lineage or Collibra on policy depth, and neither does this article.
Collibra is the tool a large regulated enterprise buys when governance has its own function, its own budget and its own head. Policy management, stewardship, workflow automation through its Workflow Designer, business glossaries and data ownership at scale are all mature, and nothing else on this list matches the depth. If governance is the requirement and time to value is not the constraint, this is the incumbent answer.
What its documentation makes clear is that the rest arrives as separate decisions. Data Quality and Observability is a distinct application that relies on Edge for every interaction with a data source, using a Data Quality Pushdown Processing capability so jobs run against warehouse compute. It offers quick monitoring at schema level for an immediate read on data health, then Data Quality Jobs at table level with custom SQL and scheduled runs. Its administration documentation describes a Data Quality license with its own key, name, expiration date and active or inactive state. That is a second contract to negotiate and a second thing that can expire.
Lineage is the same shape. Collibra's own documentation opens by describing Collibra Data Lineage as a cloud only product covering technical lineage for engineers and business lineage for everyone else. The CLI lineage harvester, the route most existing deployments use, reached end of life on 31 July 2026, with Edge as the recommended replacement. Any buyer inheriting a Collibra estate should check which method their lineage runs on before planning anything else.
The trade off is time. Decube's own comparison pages put Collibra deployment at 3 to 9 months, with a dedicated governance team and professional services, against weeks for a SaaS platform. That estimate is Decube's assessment rather than an independent finding, and it is quoted here as such, but it matches what modular licensing plus professional services usually costs in calendar time.
Alation built its reputation on search and behavioral metadata: the catalog learns from how people actually query data, and analysts adopt it because it feels like a search engine rather than a repository. Trust Flags, stewardship workflows and policy management sit inside that experience rather than beside it, which is why analytics teams tend to champion it.
Alation is still widely described as having no quality engine of its own. That description is out of date. Alation documents Intelligent Data Quality Monitoring, described as available on Alation Cloud Service instances with the New User Experience, covering completeness, validity, accuracy and freshness. It runs on a hybrid execution model: internal scheduling for standard monitors, or an SDK that runs checks inside your own pipelines in Airflow or a CI and CD workflow, which is how teams gate a pipeline on quality before bad data moves downstream.
The documented reach is where a buyer should look. The Supported Data Sources page lists Amazon Redshift, Azure Synapse, Databricks Unity Catalog, Google BigQuery, Microsoft SQL Server, Oracle version 21.3 or later, PostgreSQL, SAP HANA and Snowflake. That is 9 platforms, and Oracle is not enabled by default: the note says a customer who has purchased the Data Quality feature must contact Alation Support to have it switched on for their instance. Cloud Service is a requirement, not a preference.
Alation also documents machine learning based anomaly detection of its own, which is the other claim those descriptions get wrong. At table level it tracks row count, freshness and schema drift, and its own page notes that schema drift detection helps prevent downstream application failures. At column level it tracks duplicate count, missing count, maximum, minimum, average and standard deviation. Each metric enters a 30 day warmup during which the model learns normal behavior and raises no alerts, then runs against that baseline with a feedback loop where a reviewer confirms an anomaly or marks it as expected. Anomaly detection is available for manual monitors only, not SDK monitors. If you are comparing this with anomaly detection in data pipelines generally, the warmup period is the practical difference: you do not get alerts in month one.
For proof, Alation publishes a customer story on Sallie Mae, the private student lending and education finance company. It records more than 500 data users across the company, 250TB of accumulated data and 350,000 database fields to be cataloged, with the senior director of data governance quoted saying Alation reduces the time required for search and discovery of data. Note the wording on the third figure: it is the size of the job, not a completed count, so it is not evidence that 350,000 fields are already cataloged.
Atlan is the metadata platform teams on a modern cloud stack tend to shortlist first. Its column level lineage is genuinely strong, open through its API and available without extra setup, and Decube's own comparison pages concede the point rather than arguing it. If lineage is the whole requirement, this is a serious answer.
Data Quality Studio is the part worth reading closely. Atlan's documentation describes it as running data quality natively in your warehouse, with checks, alerts and governance workflows executing where the data lives, and names the integration as BigQuery, Databricks and Snowflake. The mechanics differ per platform: Snowflake gets auto re attachment, which reapplies quality rules after a schema change so monitoring does not silently stop, plus migration tooling; Databricks uses serverless compute; BigQuery runs rules through stored procedures. Rules validate assets automatically and quality trends are tracked over time.
That design is a real advantage and a real boundary at the same time. Running inside the warehouse means no data leaves it and no separate compute is billed. It also means the quality engine reaches three warehouses while the catalog reaches everything else you connect, so any source outside those three needs another tool or another process.
Data contracts are documented as a YAML template pushed to Atlan, version controlled, embeddable as asset metadata and enforceable through data quality rules. The documented limit is the asset types: contracts can be created for tables, views and materialized views only, and Atlan's own note says other types including Iceberg tables and non SQL output ports of data products are not currently supported.
One correction worth making, because it circulates widely and it is wrong. Atlan does not depend on OpenAI. Its AI security documentation describes a multi model architecture behind a centralized gateway routing to Anthropic Claude models hosted on AWS, OpenAI GPT models, Google Gemini models and open source models Atlan hosts itself. Atlan states that the Claude traffic stays inside AWS private networking. The gateway is deployed across United States, EU and APAC regions for data residency, each tenant gets its own key and namespace, and traffic between a tenant environment and the gateway runs over VPC peering or PrivateLink. A regulated buyer should evaluate that on its merits, not on a single provider claim.
Informatica is the answer when the estate is large, old and varied. Cloud Data Governance and Catalog, its current governance product, sits on the Intelligent Data Management Cloud and inherits decades of connectivity work. Its product page describes automated and inferred end to end lineage, automated profiling with rules, metrics and scorecards for data quality, and automatic discovery and classification across structured, semi structured and unstructured data. The AI layer is positioned as purpose built agents and AI assisted stewardship, with automated classification, association and recommendations.
Pricing is consumption based, described on the same page as pay only for what you use. That is genuinely flexible for a variable workload and genuinely hard to forecast before a pilot, which is the trade off a finance team will raise. Run a scoped proof of concept on your two noisiest sources and read the actual bill before you commit to an annual number.
If your data already lives in Microsoft Fabric, Azure Data Lake Storage Gen2, Azure SQL or Synapse, Purview is difficult to beat on cost because it is billed with the Azure account you already have. Microsoft documents data quality inside Unified Catalog as no code and low code rules, including out of the box rules and AI generated ones, applied at column level and aggregated upward into scores for data assets, data products and governance domains. Profiling is AI assisted: it recommends which columns to profile and a human refines the recommendation.
The constraints are the reason this sits at number 6 rather than higher, and they are all documented. Data quality is supported only when the Purview account and the data source are in the same Azure region. Data quality scans can run using managed identity only for Microsoft native sources such as Microsoft Fabric, Azure Data Lake Storage Gen2, Azure SQL, Synapse and Azure SQL Managed Instance, and Microsoft states plainly that managed identity cannot be used as an authentication option for Azure Databricks, Snowflake or Google BigQuery. Scans run on Apache Spark 3.5 and Delta Lake 3.2.1, and Azure Storage sources need either an open firewall, Allow Trusted Azure Services, or private endpoints configured to the documented pattern.
None of that is a defect for an all Microsoft estate. It becomes one the moment a second cloud appears, which is the usual reason a Purview evaluation turns into a wider one. The same question is worth asking of any cloud governance model you are planning: which sources does the tool actually reach, and what happens to the ones it does not.
Ataccama is the option to look at when a meaningful part of the estate is not in a cloud warehouse. Its documentation describes ONE Data Quality & Catalog, currently at version 17.1.0, as unifying data quality, data catalog and data observability in one AI augmented platform across on premise, hybrid and cloud environments. That deployment range is the clearest thing separating it from the cloud only tools higher up this list.
The product is documented in five parts. Knowledge Catalog connects sources and runs domain detection, profiling, data quality evaluation, anomaly checks, term suggestions and lineage. Business Glossary manages terms and their hierarchies, and matters more than it sounds because quality rules are applied to terms rather than to columns one at a time, so one rule change propagates. Data Quality holds the rules, detection rules, monitoring projects, reconciliation projects and transformation plans. Data Observability alerts when an item changes schema or shows a problem with structure, freshness, anomalous values or record volume, or when a new term is detected. ONE Data handles reference data and in place remediation.
These four are named in this category often enough to be worth covering, with the current status of each checked on 6 September 2026. Three of the four have changed name or owner, which is the main reason they no longer sit in the top 7.
Talend is now a Qlik product and its documentation is published on Qlik's help site. The Talend Studio User Guide there runs on a monthly release train, with 8.0 R2026-08 as the current release at the time of writing. The strength is unchanged: broad data integration with quality checks applied in the pipeline rather than after loading, which suits teams who want to reject bad records at ingestion.
The product formerly sold as IBM Watson Knowledge Catalog has been renamed twice. IBM's own product URL for Knowledge Catalog now resolves to the watsonx.data intelligence governance and catalog page, checked on 6 September 2026. IBM documents it as unifying governance with an AI driven data catalog, with data protection rules supporting secure data handling through lineage tracking and audit trails, automatic analysis and enrichment of technical metadata with context, labels or descriptions, data quality discovery and assessment across assets wherever the data resides, and policies describing how data can be used and handled.
This is the one entry where nothing could be verified today, and saying so is more useful than repeating a general description no one can check. SAP's product pages return 403 to our requests and the SAP Help Portal serves a JavaScript application rather than readable documentation, so no current statement about the product, its maintenance status or its successor could be read from SAP directly on 6 September 2026. If SAP Data Intelligence is on your shortlist, ask SAP in writing for its current maintenance position and the migration path before you evaluate it, because the answer changes what you are buying.
Data360 came to Precisely through the Infogix acquisition, and Precisely's own product page now states that the features and benefits of Data360 Govern have moved to the Suite's Data Governance service, meaning the Precisely Data Integrity Suite. The documented feature set is data policy documentation, a data catalog, a business glossary, data stewardship, metrics and scoring, workflow, advanced profiling, and data detection and tagging. One point of confusion is worth clearing up: Precisely Data360 and Salesforce Data 360 are different products from different companies, and sources about one do not describe the other.
Compare the two reaches side by side and the pattern is obvious. In every one of these products the catalog covers whatever you connect to it. The quality engine covers a subset, and the subset is defined by deployment type, region, warehouse or license rather than by anything you control. That is the single most useful thing to establish in a demo, and it is almost never on a feature grid.
| Tool | What the catalog reaches | What the quality engine reaches, per vendor documentation |
|---|---|---|
| 1. Decube | Sources connected to the platform | The same sources, capped by plan at 3, 10 or unlimited. The per connector list is not published, so request it in writing |
| 2. Collibra | Enterprise wide, including self hosted deployments | Sources reachable via Edge with the pushdown capability, under a separate license key. Collibra Data Lineage is documented as cloud only |
| 3. Alation | Broad connector coverage across the estate | 9 listed platforms, Alation Cloud Service with the New User Experience only, as a separately purchased feature |
| 4. Atlan | Broad, with strong column level lineage across connected sources | BigQuery, Databricks and Snowflake, executing natively inside each warehouse |
| 5. Informatica | Very broad, including legacy and unstructured sources | Sources onboarded to IDMC, billed by consumption |
| 6. Microsoft Purview | Multicloud through the Data Map | Same Azure region as the account, with managed identity unavailable for Azure Databricks, Snowflake and Google BigQuery |
| 7. Ataccama | On premise, hybrid and cloud sources connected to ONE | The same sources, with rules applied through glossary terms |
This is why Decube leads the list rather than because it is the largest vendor on it. The two layers are the same product with the same price, so data observability never becomes an integration to buy afterward. The same logic explains why the honest answer for a large Azure estate is still Purview, and for a large regulated enterprise with a governance team already in place is still Collibra. The gap matters most for a lean team that cannot run two procurement cycles.
An agent reads your tables the way an analyst does, except it does not pause when a number looks wrong and it cannot be asked what it assumed. Six things have to be in place before an agent touches production data, and every one of them is checkable.
This is also the practical difference between AI governance and data governance, which is a question buyers ask constantly. Data governance is about the data an AI system consumes: classification, ownership, lineage, quality and access. AI governance is about the model and the system itself: what it is allowed to do, how it is evaluated, how decisions are documented and who is accountable. They are separate disciplines with one hard dependency, because AI governance rests on data you can already describe and prove. You cannot govern a model whose inputs nobody owns.
The honest answer is that the logo matters less than three provable things: whether classification of protected health information is automatic and policy driven, whether access control operates at field level rather than table level, and whether the access trail is exportable as evidence. A tool that does all three natively is a shorter project than a catalog plus two integrations.
On that test, Decube documents HIPAA and GDPR policy enforcement, automated PII and sensitive data classification, field level access control with role based permissions, and audit and activity logs, and holds SOC 2 and ISO 27001. Microsoft Purview is the stronger answer if the estate is already Azure and every source sits in one region. Collibra is the stronger answer if a governance function already exists and the budget covers professional services and a separate quality license.
One thing to be straight about: none of the five customer stories Decube publishes is a healthcare organization. They are a Nordic energy company, a fintech in Mexico, a Nasdaq listed regional bank, an Australian financial institution and an Indonesian digital bank. The regulatory depth is real and it was built for banking, insurance and telecom. Ask any vendor on this list for a reference in your own sector rather than accepting a compliance logo as proof.
Alation is an analytics first catalog. It is bought because analysts adopt it, its search learns from query behavior, and stewardship and policy live inside the same experience. Its quality engine now exists but is bounded: 9 listed platforms, Alation Cloud Service with the New User Experience only, purchased separately, with machine learning anomaly detection that needs a 30 day warmup before it alerts on anything.
Atlan is a metadata platform first. It is bought for column level lineage and for how well it fits a modern cloud stack, and its quality rules execute natively inside the warehouse rather than in Atlan, across BigQuery, Databricks and Snowflake. Data contracts exist as YAML documents applied to tables, views and materialized views.
The practical split: choose Alation if the buyer is the analytics organization and search and stewardship adoption is the outcome you are paid for. Choose Atlan if the buyer is the data platform team, the warehouse is one of those three, and lineage is what you are solving. Both leave the same thing open, which is quality coverage for everything outside the supported list, and that is where a unified platform earns its place.
Collibra is most often compared with Alation, Atlan, Informatica, Microsoft Purview, Ataccama, Precisely and Decube. Which one displaces it depends entirely on why the evaluation started.
Most governance evaluations stall because they compare feature lists. Compare behavior on your own data instead, and four weeks is enough to decide.
If you want a reference point for what a governance implementation produces at the end, what data lineage means and why it matters is the piece to read next, because lineage coverage is usually the first number a steering group asks for after go live.
The 7 tools on this list all catalog, classify and document data well. They diverge on the part that tells you the data is wrong, and they diverge in ways their marketing pages do not mention: a deployment type, an Azure region, a warehouse list, a separate license key. Decube sits at number 1 here because catalog, column level lineage, 12 quality test types with dynamic thresholding and native anomaly detection arrive together, at a price published on the website, with five customer results published alongside it. Collibra, Alation, Atlan, Informatica, Microsoft Purview and Ataccama each win a specific evaluation, and this article says which one.
The decision rule is short enough to use today. Write down your sources, ask every vendor which of them the quality engine can monitor and under what deployment, and compare the two lists. If you want that conversation with Decube, request a demo and bring the source list with you.
A data assurance platform is a single product that both governs data and proves it is correct. It combines a catalog and business glossary, column level lineage, access control and classification, data quality testing, and observability monitors for freshness, volume and schema change. The distinction from a data catalog is that a catalog tells you what data exists and who owns it, while a data assurance platform also tells you whether that data is currently fit to use. Decube, Collibra and Ataccama sell all of those layers together, while Alation, Atlan, Informatica and Microsoft Purview document a quality engine whose reach is narrower than their catalog.
The ones that run quality tests and observability monitors natively, on the same sources the catalog covers, rather than passing signals in from a separate tool. In practice that means checking three things before you buy: which sources the quality engine can actually monitor, what deployment it requires, and whether it is included in the platform price or licensed separately. Decube includes 12 quality test types with dynamic thresholding and native anomaly detection in every plan. Collibra ships Data Quality and Observability under its own license key. Alation Data Quality is a separately purchased feature available on Alation Cloud Service instances with the New User Experience across 9 listed platforms.
Judge it on coverage rather than on feature names, because every vendor lists the same feature names. Alation documents 9 supported platforms for its quality engine and requires Alation Cloud Service. Atlan Data Quality Studio runs rules natively inside BigQuery, Databricks and Snowflake. Microsoft Purview requires the data source and the Purview account to sit in the same Azure region and cannot use managed identity for Azure Databricks, Snowflake or Google BigQuery. Collibra runs quality through Edge with a pushdown capability under a separate license. Decube applies its quality engine to the sources connected to the platform, capped by plan at 3, 10 or unlimited, and does not publish a per connector list, so ask for it in writing.
For a lean data team that needs catalog, lineage, quality and observability in one product at a published price, Decube. For a large regulated enterprise with an established governance function, Collibra. For an analytics led organization where catalog adoption is the goal, Alation. For a modern cloud stack on BigQuery, Databricks or Snowflake, Atlan. For a broad legacy estate already running Informatica, Cloud Data Governance and Catalog. For an all Azure estate, Microsoft Purview. For data quality that has to run on premise as well as in the cloud, Ataccama.
Look for observability that is part of the governance platform rather than an integration beside it, so an incident carries the owner, the classification and the lineage with it. Decube monitors pipeline health, freshness, volume and schema change with machine learning based anomaly detection natively. Collibra Data Quality and Observability offers schema level quick monitoring and table level jobs with custom SQL. Alation documents machine learning anomaly detection on row count, freshness and schema drift at table level plus six column metrics, with a 30 day warmup before it raises alerts. Ataccama alerts on schema changes, structure, freshness, anomalous values, record volume and newly detected terms.
Data integrity management tools keep data accurate and consistent from the moment it is created until it is used, which is a wider job than either governance or quality alone. They cover validation rules at ingestion, reconciliation between systems, referential consistency, classification of sensitive fields, access control, lineage so a value can be traced back to its source, and monitoring that flags when any of it breaks. Most of the platforms on this page cover part of that set, which is why the useful question is which sources each part reaches rather than whether the feature exists.
It means the reliability of a dataset is treated as a governed property with an owner, a policy, a test and an audit trail, instead of something the engineering team notices when a dashboard looks wrong. In practice that requires four things in one place: an owner and steward assigned to every critical asset, quality tests attached to the asset rather than to a pipeline run, column level lineage so the downstream effect of a failure is known before anyone asks, and an approval workflow so a change to any of those is recorded. Decube names approval gated lineage and dynamic thresholding on quality tests as the two features that make this work in practice.
First step to AI Readiness
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Sneak peek from the data world.
Bring lineage, quality, governance, and AI together in one platform—so your teams and AI agents can act with confidence.
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