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Data discoverySpecs buildingData lineage & workflowsTestingData quality

A data catalog for every step of your data projects

Discover your data, define what needs to be built, visualise it, test it, and monitor data quality.

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Find your data

We provide multiple solutions to help you find your data quickly.

Simple search

Search your data easily with our fast and intuitive interface. Ideal for simple queries and daily usage.

Fastest
Simple search illustration

SQL-like search

Use SQL-like queries for precise and advanced searches. Perfect for technical users who want to filter and analyze their data.

Most precise

AI search

Ask your questions in natural language and let our AI find the relevant data for you. A new way to interact with your data.

Smartest

Understand your data

Navigate smoothly between all your data assets, from technical to business assets, in a unified catalog.

Asset Documentation

All data assets in one place

Document all your assets: description, associated dashboards and data products, data lineage, source, criticity, confidentiality, attached medias...

Technical Assets

Systems
Systems
Databases
Databases
Schemas
Schemas
Tables
Tables
Fields
Fields

Business Assets

Data domains
Data domains
Subdomains
Subdomains
Business concepts
Business concepts

Dashboards

Dashboards

Data Products

Data products

Connect your data stack

Depending on the platform, you can import far more than tables and columns, including semantic context, lineage, usage analytics, unstructured assets, database code and integrated workflows.

+See more integrations

Databricks

Technical metadata
Available

The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).

Semantic layer
Available

The business definition of each field, read from the SQL comment attached to it.

Data lineage
Available

How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.

Usage analytics
Available

Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.

List of unstructured data
Available

An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).

Database code
Not available

Code embedded in the database: functions, stored procedures, triggers, views and other code objects.

Workflows
Available

The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.

Two-way sync
Available

Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.

Automated sync

Schedule each connection to sync automatically

Snowflake
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
Databricks
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Not available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
BigQuery
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
PostgreSQL
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
MySQL
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
Amazon Aurora
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Not available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
SQL Server
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
Oracle
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
SAP HANA
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Not available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
ClickHouse
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Not available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Not available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
Redshift
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
Azure Synapse
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
MariaDB
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
Azure SQL
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
dbt Core
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Not available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Not available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Not available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
Google Cloud Storage
  • Technical metadata: Not available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Not available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Not available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Not available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Not available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
Amazon S3
  • Technical metadata: Not available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Not available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Not available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Not available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Not available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
Azure Data Lake Storage
  • Technical metadata: Not available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Not available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Not available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Not available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Not available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
Microsoft OneLake
  • Technical metadata: Not available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Not available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Not available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Not available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Not available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
Teradata
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Not available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Not available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
Vertica
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Not available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
IBM Db2
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Not available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
IBM AS/400
  • Technical metadata: Available. The list of fields, tables, schemas and databases with their names and technical constraints (types, keys, nullability).
  • Semantic layer: Available. The business definition of each field, read from the SQL comment attached to it.
  • Data lineage: Not available. How data flows from one asset to another — which sources feed each table and field, and where they are reused downstream.
  • Usage analytics: Not available. Usage statistics for tables and fields: how often they are queried and how heavily they are relied on.
  • List of unstructured data: Not available. An inventory of file-based and non-tabular assets stored in the platform (objects, files, volumes).
  • Database code: Not available. Code embedded in the database: functions, stored procedures, triggers, views and other code objects.
  • Workflows: Not available. The orchestration layer: jobs, runs and scheduled pipelines defined in the platform.
  • Two-way sync: Not available. Push table and column descriptions from your catalog back to the source as native SQL comments, with a full preview and side-by-side diff before writing.
Power BI
  • Reports & dashboards: Available. The inventory of reports, workbooks, pages, views and dashboards published on the platform.
  • Semantic model: Limited. The datasets, models or query definitions behind reports, plus measures and calculated fields.
  • Source connections: Available. Which databases and data sources a report or dashboard connects to.
  • Field lineage: Limited. Field references extracted from metadata APIs or query parsing, and matched back to catalog fields.
  • Consumption analytics: Not available. Report or view usage metadata, such as opening frequency or view counts.
Tableau
  • Reports & dashboards: Available. The inventory of reports, workbooks, pages, views and dashboards published on the platform.
  • Semantic model: Limited. The datasets, models or query definitions behind reports, plus measures and calculated fields.
  • Source connections: Available. Which databases and data sources a report or dashboard connects to.
  • Field lineage: Limited. Field references extracted from metadata APIs or query parsing, and matched back to catalog fields.
  • Consumption analytics: Limited. Report or view usage metadata, such as opening frequency or view counts.
Looker
  • Reports & dashboards: Available. The inventory of reports, workbooks, pages, views and dashboards published on the platform.
  • Semantic model: Limited. The datasets, models or query definitions behind reports, plus measures and calculated fields.
  • Source connections: Not available. Which databases and data sources a report or dashboard connects to.
  • Field lineage: Limited. Field references extracted from metadata APIs or query parsing, and matched back to catalog fields.
  • Consumption analytics: Not available. Report or view usage metadata, such as opening frequency or view counts.
Qlik Sense
  • Reports & dashboards: Available. The inventory of reports, workbooks, pages, views and dashboards published on the platform.
  • Semantic model: Available. The datasets, models or query definitions behind reports, plus measures and calculated fields.
  • Source connections: Available. Which databases and data sources a report or dashboard connects to.
  • Field lineage: Limited. Field references extracted from metadata APIs or query parsing, and matched back to catalog fields.
  • Consumption analytics: Not available. Report or view usage metadata, such as opening frequency or view counts.
Grafana
  • Reports & dashboards: Available. The inventory of reports, workbooks, pages, views and dashboards published on the platform.
  • Semantic model: Limited. The datasets, models or query definitions behind reports, plus measures and calculated fields.
  • Source connections: Available. Which databases and data sources a report or dashboard connects to.
  • Field lineage: Limited. Field references extracted from metadata APIs or query parsing, and matched back to catalog fields.
  • Consumption analytics: Not available. Report or view usage metadata, such as opening frequency or view counts.
Metabase
  • Reports & dashboards: Available. The inventory of reports, workbooks, pages, views and dashboards published on the platform.
  • Semantic model: Limited. The datasets, models or query definitions behind reports, plus measures and calculated fields.
  • Source connections: Available. Which databases and data sources a report or dashboard connects to.
  • Field lineage: Limited. Field references extracted from metadata APIs or query parsing, and matched back to catalog fields.
  • Consumption analytics: Not available. Report or view usage metadata, such as opening frequency or view counts.

Trace the lineage of your data

Follow every transformation of your data, from source systems to reports, on an interactive canvas, and even design future pipelines on it.

How to read the board
Table-level lineage, with the transformation type (union, join...)
Field-level lineage, from column to column

Origins and transformations

Understand where each table comes from and what transformation is applied at every step: union, join, aggregation, exact copy.

Field and table level

Drill from table flows down to individual columns. Each field keeps its own lineage.

Up and down navigation

Climb up to the source systems or down to the dashboards.

Also a design tool

The same canvas is a collaborative board: sketch future pipelines and enrich your catalog before development even starts.

Let AI do the tedious work

Let our AI analyze your metadata and generate lineage, business concepts, and more, in just a few clicks.

0%25%50%75%85%015min30min45min1hCatalog completenessTime spent12345
AI-powered workflow
5x
faster documentation
>80%
accuracy rate
1
Data integration
≈ 5min4%
The metadata from your databases is added to the data catalog.
2
Automated lineage
≈ 16min14%
The data lineage is inferred by AI when it has not been retrieved from the integration.
Indicates the type of the transformationBoth field and table levelsLearn more about automated lineage
3
Automated business concepts
≈ 29min30%
The business definitions of the fields are suggested by AI.
Link to existing business conceptsSuggestion of new business conceptsCan be saved as draftLearn more about automated business concepts
4
Notifications
≈ 41min52%
If business concepts are saved in draft mode, data owners receive an email notification asking them to review and validate them.
Data owners notified by emailOne-click access from the email linkReview, edit and validate
5
Validate
≈ 56min85%
The data owner reviews, validates or edits with one click.

Track data quality

Bring your quality checks into your data catalog, by connecting it to your databases.

customers
  • customers48,210 rows
  • iduuid8f3a…c21e
  • emailvarcharana@acme.io
  • countryvarcharFR
  • orders312,480 rows
  • order_iduuid51b7…09ad
  • amountdecimal129.90
  • created_attimestamp2026-09-24 09:30
Analyzing…0/4

Refresh your existing check results on schedule or on demand.

Explore data quality

Talk to your data with AI

Need a specific piece of information? Ask the AI assistant built into the data catalog.

  • Natural language questions

    Ask your questions in plain language, without writing a query or knowing the technical model.

  • Contextual recommendations

    Get recommendations based on what is documented in your catalog.

Ask a question about your data to an AI

How can I help you today?

Give your AI agents a context layer

Connect your AI agents to your data catalog, so they work from the same documented context as your teams.

04Agents and applications

AI agentsAny MCP client, such as AI assistants
Your applicationsScripts and integrations
HerusContext layer

03Context access

MCP serverFor AI agents
REST APIAlso open to your code
Read API/MCP documentation

02Data catalog

  • Descriptions
  • Tags
  • Technical metadata
  • Lineage
  • Quality
  • Governance
  • And more

01Data sources

  • Snowflake
  • Databricks
  • BigQuery
  • PostgreSQL
  • Oracle
  • SQL Server
See more integrations

Monitor your data governance

Real-time dashboards to follow your data governance key metrics.

Total assets
1,247+12.8%
Active users
278+7.3%
Applied tags
3,542+15.2%

Created and modified assets over time

CreatedModified

User activity

Active users engaging with your data catalog, month over month.

Tags usage

How tags are distributed across your assets