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Day Zero Semantic Layer

AI generated semantic layer on day zero, right after data source is connected - building the foundation of automated and trusted data governance. No manual modeling or Yaml/Json upload required.

How to setup

Overview

The DayZero Semantic Layer (DayZero SL) feature automatically builds a semantic model the moment data connection is setup. It discovers datasets, relationships, metrics, and joins and initializes Connecty’s Autonomous Semantic Graph (ASG) without any manual configuration.

DayZero SL uses existing metadata, query history, and data samples to form a holistic understanding of your data’s logical and business context, and allows admins to validate (human-in-the-loop).

What this means for your business: Teams can start extracting insights based on your custom definitions from day one, instead of waiting for manual upload of business definitions.

Key Capabilities

1. Auto Semantic Layer Generation

During DayZero sync, Connecty runs a Query-to-Semantic Layer and Semantic Layer Reconciliation process:

  • Converts discovered relationships into an internal grammar representation

  • Merges inferred logic with existing verified grammar (if any)

  • Ensures consistency and removes ambiguous mappings

Business impact: Analysts get an immediately usable data reasoning layer. The AI understands metric relationships and naming conventions without needing model scripts or dbt packages.

2. Query History Analysis

Connecty analyzes past query history (from tools like Athena or Snowflake) to identify:

  • Frequently used joins and filters

  • Metrics and business expressions used in previous SQL

  • Naming patterns that indicate business intent (e.g., “total_sales”, “monthly_active_users”)

Business impact: The system learns the organization's natural data language automatically, so the first AI answers already align with how your team talks about KPIs.

3. Automatic Schema Discovery

When a new data connection is added (e.g., Snowflake, Databricks, BigQuery, PostgreSQL, Athena), Connecty automatically scans:

  • Tables, columns, and joins

  • Data types and primary keys

  • Relationships between entities

This information is used to populate the ASG (Autonomous Semantic Graph).

Business impact: Reduces dependency on engineers to define structures manually. The semantic model starts forming immediately after connection, accelerating the time to first insight.

4. Column-Level Data Statistics

Connecty automatically computes data quality metrics during sync, including:

  • Null counts and percentages

  • Unique value counts

  • Minimum, maximum, and average (for numeric fields)

Business impact: Analysts and AI agents can evaluate data completeness and reliability instantly, improving the accuracy of generated queries and recommendations.

5. Day-Zero Query History Sync Workflow

Connecty executes a chained sync process:

  1. DE Query History Sync – collects past query usage.

  2. DW Query History Sync – builds semantic clusters from those queries.

  3. Grammar Reconciliation – updates the ASG with query-derived logic.

  4. Completion Event – marks the environment as ready for semantic exploration.

Business impact: Connecty’s AI doesn’t start from zero — it starts with a pre-trained understanding of your organization’s analytical behavior.

6. Parallel Semantic Reasoning

The DayZero process runs multiple semantic inference steps in parallel:

  • Entity extraction

  • Join and key relationship detection

  • Metric and aggregation classification

Business impact: The semantic graph is ready within minutes, even for large warehouses with hundreds of tables.

7. Context Graph

Every generated DWQuery now includes a chart specification, describing how results should be visualized (e.g., line chart, bar chart, trend).

Business impact: The semantic layer doesn’t just interpret queries — it also encodes visualization intent, allowing Connecty to produce instant charts and summaries directly in chat.

8. Verified-Entity Enforcement

When “verified-only” mode is enabled, Connecty’s DayZero-generated semantic layer:

  • Flags unverified entities

  • Restricts reasoning and query generation to approved definitions

Business impact: Ensures business consistency from day zero — only trusted metrics and dimensions are used in AI responses.

Technical Flow Summary

  1. Connect data source (Snowflake, BigQuery, Databricks, PostgreSQL, Athena).

  2. Schema sync → captures tables, joins, and data types.

  3. Query history sync → learns past business usage.

  4. Grammar generation → maps semantics and metrics.

  5. ASG initialization → builds autonomous semantic relationships.

  6. Data stats collection → captures data completeness and quality indicators.

  7. Ready state → Connecty is now fully semantic and query-ready.

Business Value Summary

Feature
Business Outcome

Automatic semantic generation

Reduce modeling time from weeks to minutes

Query history learning

AI understands your organization’s data language

Built-in data stats

Confident decisions based on data quality awareness

Verified-only mode

Governance and consistency from the start

Chart intent recognition

Answers appear as readable insights, not just tables

What Happens Next

Once the DayZero Semantic Layer is initialized:

  • Business users can start asking natural language questions immediately.

  • Data stewards can begin verifying and refining entities.

  • Connecty continues to learn and reconcile new relationships automatically.

DayZero establishes a foundation that combines speed, accuracy, and trust — giving organizations an operational semantic layer before their first manual edit.

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