Gudu SQL Omni - Data Lineage & Impact Analysis for VS Code

Track data flow, analyze impact, and understand dependencies across your SQL pipelines

Transform your data analysis workflow with powerful SQL lineage visualization, impact analysis, and dependency tracking—all within VS Code. Designed for data engineers and analysts who need to understand how data flows through complex SQL transformations.

🎯 Built for Data Teams

For Data Engineers

Impact Analysis: See what breaks when you change a table or column
Pipeline Visualization: Understand data flow through ETL processes
Dependency Tracking: Identify upstream and downstream dependencies
Migration Safety: Validate schema changes before deployment

For Data Analysts

Data Tracing: Track where data comes from and where it goes
Transformation Understanding: Visualize how data is transformed through queries
Column-Level Lineage: See exactly which columns feed into calculations
Cross-Query Analysis: Understand relationships across multiple SQL files

🔍 Core Features for Data Analysis

1. Data Lineage Visualization

Data Lineage

Track Every Data Movement

Visualize complete data flow from source to destination
Column-level lineage showing exact transformations
Support for CTEs, subqueries, and complex joins
Interactive diagrams with zoom and pan
Export lineage diagrams as PNG for documentation

Real-World Use Cases:

Trace a KPI back to its source tables
Understand impact of changing a column data type
Document data pipelines for compliance
Identify redundant transformations

2. Impact Analysis

Know What Changes Affect

Identify all downstream queries affected by table changes
Understand column dependencies across views and CTEs
Predict migration risks before execution
Generate impact reports for stakeholders

3. Schema Extraction & Documentation

Automatic Schema Discovery

Extract all tables and columns from complex queries
Identify data types and relationships
Document undocumented queries automatically
Build schema documentation from SQL code

4. ER Diagram Generation

ER Diagram

Understand Data Relationships

Generate diagrams from DDL statements
Visualize foreign key relationships
Identify orphaned tables
Document database structure

💡 Common Data Engineering Scenarios

Scenario 1: Data Pipeline Impact Analysis

-- You need to modify a source table
-- The extension immediately shows:
-- ✓ 15 transformation queries affected
-- ✓ 3 downstream dashboards impacted
-- ✓ 2 data marts need rebuilding

Scenario 2: Column-Level Data Tracing

-- Track where customer_lifetime_value comes from:
-- Source: transactions.amount (SUM)
-- → staging.customer_metrics (aggregation)
-- → analytics.customer_360 (final calculation)
-- → dashboard.kpi_metrics (presentation)

Scenario 3: Migration Risk Assessment

-- Before changing data types or dropping columns:
-- ✓ See all affected transformations
-- ✓ Identify breaking changes
-- ✓ Generate migration checklist

🚀 Quick Start for Data Teams

Install: Search "Gudu SQL Omni" in VS Code Extensions
Open: Your SQL files (queries, DDL, stored procedures)
Select: The SQL you want to analyze
Analyze: Right-click → "Analyze Data Lineage"

Essential Commands

Command	Shortcut	Use When You Need To
Data Lineage	`Ctrl+Alt+L`	Trace data flow and transformations
Extract Schema	`Ctrl+Shift+E`	Document tables and columns used
ER Diagram	`Ctrl+Alt+E`	Visualize table relationships
Validate SQL	`Ctrl+Shift+V`	Check syntax before deployment

📊 Supported SQL Dialects

Works with your existing data stack:

Cloud Data Warehouses

✅ Snowflake
✅ BigQuery
✅ Redshift
✅ Databricks
✅ Azure Synapse

Traditional Databases

✅ PostgreSQL
✅ MySQL
✅ Oracle
✅ SQL Server
✅ DB2

Big Data Platforms

✅ Spark SQL
✅ Hive
✅ Presto/Trino
✅ Impala
✅ AWS Athena

🔧 Configuration for Data Workflows

{
  // Set your primary data warehouse
  "guduSQLOmni.general.defaultDbVendor": "snowflake",

  // Include temporary tables in lineage
  "guduSQLOmni.lineage.showTemporaryResult": true,

  // Show all data relationships
  "guduSQLOmni.lineage.showRestrictGroupsRelation": true,

  // Debug complex transformations
  "guduSQLOmni.general.logLevel": "debug"
}

💼 Why Data Teams Choose Gudu SQL Omni

✅ Offline Analysis

All processing happens locally
No data leaves your machine
No cloud dependencies
Works with sensitive data

✅ Complex SQL Support

Multi-CTE queries
Recursive CTEs
Window functions
Stored procedures
Dynamic SQL

✅ Integration Ready

Works with existing SQL files
No code changes required
Supports version control workflows
Export diagrams for documentation

📈 ROI for Data Teams

Save Hours Weekly

🕐 -75% time understanding unfamiliar queries
📊 -60% time documenting data pipelines
🔍 -80% time tracing data issues
⚠️ -90% migration-related incidents

🎓 Real User Testimonials

"As a data engineer managing 500+ ETL queries, the lineage visualization helps me quickly assess impact before making changes. It's prevented multiple production issues." — Senior Data Engineer, FinTech

"I use it daily to trace metrics back to source data. When stakeholders ask 'where does this number come from?', I can show them the exact path in seconds." — Lead Data Analyst, E-commerce

"The column-level lineage is incredible. We can finally see which PII columns flow into which reports for compliance tracking." — Data Governance Manager, Healthcare

🚦 Getting Started Examples

Example 1: Trace a Business Metric (90‑day LTV with currency conversion)

WITH completed_orders AS (
  SELECT
    o.order_id,
    o.customer_id,
    o.order_date,
    o.amount,
    o.currency
  FROM raw.orders o
  WHERE o.status = 'COMPLETED'
),
fx AS (
  SELECT
    f.currency_code,
    f.rate_to_usd,
    f.valid_on
  FROM dim.exchange_rates f
),
orders_usd AS (
  SELECT
    o.customer_id,
    o.order_date,
    o.amount * COALESCE(f.rate_to_usd, 1) AS amount_usd
  FROM completed_orders o
  LEFT JOIN fx f
    ON f.currency_code = o.currency
   AND f.valid_on = DATE_TRUNC('day', o.order_date)
),
recent AS (
  SELECT
    customer_id,
    order_date,
    amount_usd
  FROM orders_usd
  WHERE order_date >= CURRENT_DATE - INTERVAL '90' DAY
)
SELECT
  customer_id,
  SUM(amount_usd) AS ltv_90d
FROM recent
GROUP BY customer_id;

Right‑click → Analyze Data Lineage
The diagram highlights:
- Sources: raw.orders.amount, dim.exchange_rates.rate_to_usd
- Transform: amount * rate_to_usd → orders_usd.amount_usd (value change)
- Filter: last 90 days window applied in recent
- Aggregate: SUM(amount_usd) per customer_id → final ltv_90d
Use this to validate assumptions, check currency/join logic, and trace exactly which upstream columns feed the KPI.

Example 2: Assess Migration Impact

-- Planning to change this table?
ALTER TABLE dim_customer
  ADD COLUMN customer_segment VARCHAR(50);

-- The extension shows:
-- • 12 downstream queries need updates
-- • 5 views will be affected
-- • 3 dashboards may break

💰 Pricing & Editions

Free Trial: Full features for evaluation (limited to 10 tables per analysis)

Professional Edition: Unlimited analysis for individuals

✅ Unlimited table analysis
✅ All visualization features
✅ Export capabilities
✅ Priority support

Team Edition: For data teams (Coming Soon)

✅ Everything in Professional
✅ Shared lineage repository
✅ Team collaboration features
✅ API access

🔮 Roadmap for Data Teams

Coming Soon:

🔄 Incremental Lineage: Track changes over time
📁 Project-Wide Analysis: Analyze entire SQL repositories
🔗 dbt Integration: Native dbt model lineage
📊 Lineage API: Integrate with data catalogs
🤖 Smart Suggestions: AI-powered optimization hints
📈 Impact Scoring: Quantify change risks

❓ FAQ for Data Professionals

Q: Can it handle our complex 1000+ line queries? A: Yes, the parser handles complex queries including CTEs, window functions, and nested subqueries.

Q: Does it work with our proprietary SQL extensions? A: The parser understands standard SQL plus vendor-specific syntax. Try with the closest dialect.

Q: Can we use it in our CI/CD pipeline? A: The VS Code extension is for development. Contact us for CI/CD integration options.

Q: How accurate is the lineage analysis? A: Column-level accuracy without database connection. With metadata (coming soon), 100% accuracy.

🛠️ Troubleshooting

Large queries are slow?

Set log level to "error" for better performance
Consider breaking into smaller chunks for analysis

Lineage not showing all tables?

Check if CTEs are being recognized
Ensure correct SQL dialect is selected

Need help?

🏢 About Gudu Software

Gudu Software specializes in SQL parsing and data lineage tools, serving Fortune 500 companies' data governance needs. Our SQLFlow technology powers enterprise data catalogs and governance platforms worldwide.

Ready to take control of your data pipelines?

Install Gudu SQL Omni now and see your data flow like never before!

Quick Actions:

⭐ Rate us if this helps your data workflow
📢 Share with your data team
💬 Send feedback on data-specific features you need
🐛 Report issues with your SQL dialect

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