Catalog

1. Overview

Purpose of the Catalog

The Catalog in Connecty AI provides a centralized dictionary of your data assets. It acts as a structured directory where you can explore the contents of your connected databases — including tables, columns, schemas and metadata. Whether you’re a data analyst, engineer or business user, the Catalog helps you understand what data is available, how it's organized, and what it means — all without writing a single line of code.

Using the Catalog before diving into query building or analysis gives you clarity and saves time by removing guesswork.

Where to find it

You can access the Catalog by navigating to the Context Engine section in the left-hand menu. Once there, select Catalog from the list of available tools. This will open the Catalog interface, where you can switch between it tabs. Each tab presents a different level of detail, allowing you to explore from the top level (entire databases) down to specific columns.

2. Catalog structure

2.1 Tabs Hierarchy

The Catalog is organized to reflect the logical structure of a database environment, with information grouped into four tabs: Database, Schema, Objects, and Columns. These tabs are interconnected and follow a top-down hierarchy, moving from general to specific.

The structure makes it easy to drill down from a high-level overview (what data sources are available?) to very detailed metadata (what does this specific column contain?). You can also click on any entity in these tabs to reveal deeper information.

2.2 Schema tab

The Schema tab shows the second level in the Catalog hierarchy. A schema is a logical container within a database, grouping together related tables and other data objects.

What you’ll see

Each row in the Schema tab represents one schema and includes:

• Schema Name – The technical name of the schema.

• Database Name – The parent database the schema belongs to.

• Tables Count – Number of tables (objects) stored within that schema.

• Enriched Description – A human-readable explanation of the schema’s purpose and contents.

Think of the Schema tab as a zoomed-out view of your data landscape. Use it to decide which parts of the database you want to investigate further via the Objects and Columns tabs.

2.4 Objects tab (Tables/Views)

The Objects tab brings you to the table level — this is where actual data lives. In most cases, “objects” refer to tables, though other object types (like views) could be included depending on the system setup.

This tab helps you understand what datasets are available, how large they are, and what kind of information they contain.

What you’ll see

Each row in the Objects tab corresponds to one table and includes:

• Name – The name of the table (e.g., customer, orders, lineitem)

• Type – The object type (e.g., table)

• State – Whether the object is currently available

• Database – The database the table belongs to

• Schema – The schema that contains the object

• Enriched Description – A short explanation of what kind of data the table holds

• Row Count – Total number of records

• Size (MB) – Storage size of the table

• Columns Count – How many columns the table contains

How to use it

Use the Objects tab when you want to:

• Quickly find a table based on name or description

• Identify large tables by row count or storage size

• Understand the purpose of a table through its enriched description

• Get a high-level sense of how detailed or complex a table is (via columns count)

If your Catalog contains many tables, use the filter panel on the right side to search or narrow by name, type, state, database, or schema. This is especially useful when you're working in large environments or looking for a specific subset of tables/

Click for more

Clicking on an object opens the object (table) inspector, which includes:

• Full DDL (Data Definition Language) statement — showing how the table is structured

• Column list with names, data types, descriptions, and sample values

• Basic stats such as row count, size, and timestamps

• A field for editing the table’s description

This view is incredibly helpful for both technical users and business users — it gives you a complete picture of the data structure and content, without needing to open a query editor.

2.5 Columns tab

The Columns tab provides the most granular view in the Catalog. It lists all individual columns across all tables and gives detailed metadata about each one, making it easy to understand field-level data even before querying anything. This tab is especially useful for data exploration, validation and governance.

What you’ll see

Each row in the Columns tab represents a single column and includes:

• ID – Internal identifier for the column

• Name – The technical name of the column (e.g., _fivetran_deleted)

• Data Type – The abstracted type (e.g., BOOL, STRING, TIMESTAMP)

• Table – The table the column belongs to

• Schema – The schema where the table resides

• Database – The parent database

• State – Whether the column is currently available

• Enriched Description – A short explanation of what the column represents

• PII – Indicates if the column has been marked as containing Personally Identifiable Information

• AI Suggested PII – Indicates if the system suspects the column may contain PII

• Deleted – Whether the column is marked as deleted in the source

How to use it

Use the Columns tab when you want to:

• Look up a column by name across all tables

• Understand column-level metadata (e.g., data types, purpose, privacy sensitivity)

• Identify fields that may contain sensitive information

• Validate whether a certain attribute exists in your dataset before writing queries

• Explore enriched descriptions for better understanding of field roles

The Columns tab includes advanced filtering options that let you search by column name, data type, table, schema, database, state, etc. This is extremely useful when you're looking for all columns of a certain type.

Clicking on a column opens the column inspector, where you’ll find:

• Full metadata including nullability, position, and data type

• Column statistics (row count, null percentage, sample values)

• Flags like system-defined, hidden, or partitioning column

• Editable enriched description

• PII-related settings (user-defined and AI-suggested)

3. Entity Inspectors

Clicking on any item in the Catalog — whether it’s a database, schema, object or column — opens a detailed Entity Inspector. This inspector reveals additional metadata such as data definitions, sample values, statistics, DDL statements and system flags. This interactive layer helps you drill deeper into the structure and meaning of your data without needing to leave the Catalog view.

3.1 Object Inspector

3.2 Column Inspector

3.3 Editing the description

Moderators can edit and save the AI generated business descriptions by clicking on the pen icon next to the descriptions.

When and why to use the Catalog

You’ll find the Catalog particularly useful when:

• You’re exploring a new dataset and want to quickly understand its structure.

• You need to locate a specific table or column to build queries or validate data definitions.

• You're reviewing metadata, such as row counts, data types, or descriptions, to assess data quality.

• You want to identify columns that may contain Personally Identifiable Information (PII), either manually marked or automatically suggested by the system, so you can handle sensitive data appropriately.

• You’re collaborating across teams and need to align on the meaning and purpose of certain data elements.