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Data Explorer

Purpose and Overview​

The Data Explorer is a sophisticated interface within the Nodus platform designed to provide comprehensive visibility and interactive access to your organizational data assets. This module facilitates database navigation, schema inspection, and data analysis through an intuitive visual interface, eliminating the need for complex query writing for initial data exploration.

Core Capabilities​

  • Database Navigation: Hierarchical browsing of database structures
  • Schema Analysis: Detailed inspection of table relationships and column definitions
  • Data Preview: Interactive examination of dataset contents with intelligent sampling
  • Metadata Management: Annotation and documentation of data assets
  • Search Functionality: Advanced search capabilities across all data objects
  • Integration with Analytics: Seamless transition to SQL Runner for advanced analysis

Interface Components​

The Data Explorer interface consists of several key components, each serving a specific function in the data discovery process:

Database Navigator​

The left panel presents a hierarchical tree view of your database resources:

  • Database Selection: Switch between different connected databases
  • Schema Organization: View logical groupings of related tables
  • Folder Structure: Navigate custom-defined organizational folders
  • Favorites: Quick access to frequently used tables and views
  • Search: Locate specific database objects quickly

Schema Inspector and Data Preview​

The central panel provides detailed metadata about selected database objects:

  • Table Information: Overview of selected table properties
  • Column Definitions: Comprehensive list of columns with data types
  • Primary Keys: Identification of unique identifiers
  • Foreign Keys: Visualization of relationships between tables
  • Indexes: Performance optimization structures
  • Constraints: Business rules enforced at the database level
  • Tabular View: Grid representation of data rows and columns
  • Pagination: Navigation through large datasets
  • Sorting: Order data by any column for quick pattern identification

Key Functionality​

  1. Select the appropriate database from the database selector dropdown
  2. Expand schema nodes to view contained tables and views
  3. Use the search functionality to locate specific objects

Inspecting Schema​

  1. Select a table or view from the navigator to display its structure
  2. Review general table properties (row count, size, creation date)
  3. Examine column attributes:
    • Name
    • Data type with precision/scale
    • Nullability
    • Default values
    • Constraints
  4. Identify primary and foreign keys

Schema Summarization​

The summarization feature provides statistical insights into table structure:

  • Column Count: Total number of fields
  • Data Type Distribution: Breakdown of column types
  • Dimension vs. Metric Analysis: Classification of columns by usage pattern
  • Relationship Density: Assessment of connections to other tables
  • Update Frequency: Analysis of data modification patterns

Inspecting Tables​

  1. Select a table from the navigator
  2. View table metadata including storage parameters and distribution keys
  3. Examine column definitions and constraints
  4. Access the data preview tab to examine actual content

Table Schema Analysis​

The schema analysis provides detailed information about the table structure:

  • Column Name: The identifier used in queries
  • Data Type: The specific data type with any parameters
  • Description: Documentation associated with the column

Data Preview​

The data preview functionality enables direct examination of table contents:

  1. Automatically displays a sample of rows (default limit: 100)
  2. Sort by clicking on column headers
  3. Identify patterns and anomalies visually

Labeling and Documentation​

The Data Explorer enables annotation of data assets to improve discoverability and understanding:

  1. Add descriptive labels to tables and columns
  2. Categorize tables by business domain or functional area
  3. Tag tables with relevant business terms
  4. Document table purpose and usage guidelines
  5. Define column-level business definitions

These annotations enhance both human discovery and AI-powered assistance capabilities within the platform.

Integration with Other Nodus Components​

The Data Explorer is designed to work seamlessly with other Nodus platform components:

  • SQL Runner: Transition directly to query execution for advanced analysis
  • DataFlow Canvas: Identify source and target tables for data pipeline design
  • Data Catalog: Contribute and consume metadata for enterprise data governance
  • AI Assistant: Leverage enhanced context for natural language interactions

Best Practices​

Efficient Navigation​

  • Utilize search functionality for large database environments
  • Create logical folder structures for related tables

Effective Metadata Management​

  • Maintain comprehensive descriptions for all important tables
  • Label columns with business terminology
  • Document data lineage and update processes
  • Identify sensitive data fields with appropriate tags

Optimized Performance​

  • Use filters when previewing large tables
  • Close unused table previews to conserve resources
  • Schedule intensive exploration during off-peak hours
  • Export large datasets rather than previewing in the interface