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The Hidden Cost of Routine SQL Reporting

 
Data teams across organizations face the same recurring challenge: stakeholders require regular reports, but manual SQL reporting consumes valuable time that could be spent on analysis. The process remains consistent regardless of company size — connect to the database, execute queries, format results, and distribute findings to decision-makers.

Data professionals routinely handle reporting tasks that don’t require advanced statistical knowledge or domain expertise, yet they consume significant time through repetitive execution of the same queries and formatting procedures.

This workflow addresses a fundamental efficiency problem: transforming one-time setup into ongoing automated delivery of professional reports directly to stakeholder inboxes.

The Solution: A 4-Node Automated Reporting Pipeline

 
Building on our previous n8n exploration, this workflow tackles a different automation challenge: scheduled SQL reporting. While our first tutorial focused on data quality analysis, this one demonstrates how n8n handles database integration, recurring schedules, and email distribution.

Unlike writing standalone Python scripts for reporting, n8n workflows are visual, reusable, and easy to modify. You can connect databases, perform transformations, run analyses, and deliver results — all without switching between different tools or environments. Each workflow consists of “nodes” that represent different actions, connected together to create an automated pipeline.

Our automated SQL reporter consists of four connected nodes that transform manual reporting into a hands-off process:

1. Schedule Trigger – Runs every Monday at 9 AM
2. PostgreSQL Node – Executes sales query against database
3. Code Node – Transforms raw data into formatted HTML report
4. Send Email Node – Delivers professional report to stakeholders

Building the Workflow: Step-by-Step Implementation

Prerequisites

Step 1: Set Up Your PostgreSQL Database

We’ll create a realistic sales database using Supabase for this tutorial. Supabase is a cloud-based PostgreSQL platform that provides managed databases with built-in APIs and authentication—making it ideal for rapid prototyping and production applications. While this tutorial uses Supabase for convenience, the n8n workflow connects to any PostgreSQL database, including AWS RDS, Google Cloud SQL, or your organization’s existing database infrastructure.

Create Supabase Account:

1. Visit supabase.com and sign up for free
2. Create new project – choose any name and region
3. Wait for setup – takes about 2 minutes for database provisioning
4. View your connection details from the Settings > Database page (or the “connect” button on the main page)

Load Sample Data:

Navigate to the SQL Editor in Supabase and run this setup script to create our sales database tables and populate them with sample data:

-- Create employees table
CREATE TABLE employees (
    emp_id SERIAL PRIMARY KEY,
    first_name VARCHAR(50),
    last_name VARCHAR(50),
    department VARCHAR(50)
);

-- Create sales table
CREATE TABLE sales (
    sale_id SERIAL PRIMARY KEY,
    emp_id INTEGER REFERENCES employees(emp_id),
    sale_amount DECIMAL(10,2),
    sale_date DATE
);

-- Insert sample employees
INSERT INTO employees (first_name, last_name, department) VALUES
('Mike', 'Johnson', 'Sales'),
('John', 'Doe', 'Sales'),
('Tom', 'Wilson', 'Sales'),
('Sarah', 'Chen', 'Marketing');

-- Insert recent sales data
INSERT INTO sales (emp_id, sale_amount, sale_date) VALUES
(1, 2500.00, CURRENT_DATE - 2),
(1, 1550.00, CURRENT_DATE - 5),
(2, 890.00, CURRENT_DATE - 1),
(2, 1500.00, CURRENT_DATE - 4),
(3, 3200.00, CURRENT_DATE - 3),
(4, 1200.00, CURRENT_DATE - 6);

Paste this entire script into the SQL Editor and click the “Run” button in the bottom-right corner. You should see “Success. No rows returned” confirming that your tables and sample data have been created successfully.

Test Your Connection:

Within the same SQL Editor, run a fresh query to verify everything works: SELECT COUNT(*) FROM employees;

You should see 4 employees in the results.

Step 2: Configure Gmail for Automated Sending

Enable App Password:

1. Turn on 2-step verification in your Google Account settings
2. Generate app password – go to Security > App passwords
3. Select “Mail” and “Other” – name it “n8n reporting”
4. Copy the 16-character password – you’ll need this for n8n

Step 3: Import and Configure the Workflow

Import the Template:

1. Download the workflow file
2. Open n8n and click “Import from File”
3. Select the downloaded file – all four nodes appear automatically
4. Save the workflow as “Automated SQL Reporting”

The imported workflow contains four connected nodes with all the complex SQL and formatting code already configured.

Configure Database Connection:

1. Click the PostgreSQL node
2. Get your connection details from Supabase by clicking the “Connect” button on your main page. For n8n integration, use the “Transaction pooler” connection string as it’s optimized for automated workflows:

3. Create new credential with your Supabase details:
   • Host: [your-project].supabase.com
   • Database: postgres
   • User: postgres…..
   • Password: [from Supabase settings]
   • Port: 6543
   • SSL: Enable
4. Test connection – you should see a green success message

Configure Email Settings:

1. Click the Send Email node
2. Create SMTP credential:
   • Host: smtp.gmail.com
   • Port: 587
   • User: your-email@gmail.com
   • Password: [your app password]
   • Secure: Enable STARTTLS
3. Update recipient in the “To Email” field

That’s it! The analysis logic automatically adapts to different database schemas, table names, and data types.

Step 4: Test and Deploy

1. Click “Execute Workflow” in the toolbar
2. Watch each node turn green as it processes
3. Check your email – you should receive the formatted report
4. Toggle to “Active” to enable Monday morning automation

Once the setup is complete, you’ll receive automatic weekly reports without any manual intervention.

Understanding Your Automated Report

Here’s what your stakeholders will receive every Monday:

Email Subject: 📊 Weekly Sales Report – June 27, 2025

Report Content:

• Clean HTML table with proper styling and borders
• Summary statistics calculated automatically from SQL results
• Professional formatting suitable for executive stakeholders
• Timestamp and metadata for audit trails

Here’s what the final report looks like:

The workflow automatically handles all the complex formatting and calculations behind this professional output. Notice how the report includes proper currency formatting, calculated averages, and clean table styling—all generated directly from raw SQL results without any manual intervention. The email arrives with a timestamp, making it easy for stakeholders to track reporting periods and maintain audit trails for decision-making processes.

Technical Deep Dive: Understanding the Implementation

 
Schedule Trigger Configuration:

The workflow runs every Monday at 9:00 AM using n8n’s interval scheduling. This timing ensures reports arrive before weekly team meetings.

SQL Query Logic:

The PostgreSQL node executes a sophisticated query with JOINs, date filtering, aggregations, and proper numeric formatting. It automatically:

• Joins employee and sales tables for complete records
• Filters data to last 7 days using CURRENT_DATE - INTERVAL '7 days'
• Calculates total sales, revenue, and averages per person
• Orders results by revenue for business prioritization

HTML Generation Logic:

The Code node transforms SQL results into professional HTML using JavaScript. It iterates through query results, builds styled HTML tables with consistent formatting, calculates summary statistics, and adds professional touches like emojis and timestamps.

Email Delivery:

The Send Email node uses Gmail’s SMTP service with proper authentication and HTML rendering support.

Testing with Different Scenarios

 
To see how the workflow handles varying data patterns, try these modifications:

1. Different Time Periods: Change INTERVAL '7 days' to INTERVAL '30 days' for monthly reports
2. Department Filtering: Add WHERE e.department="Sales" for team-specific reports
3. Different Metrics: Modify SELECT clause to include product categories or customer segments

Based on your business needs, you can determine next steps: weekly reports work well for operational teams, monthly reports suit strategic planning, quarterly reports serve executive dashboards, and daily reports help with real-time monitoring. The workflow adapts automatically to any SQL structure, allowing you to quickly create multiple reporting pipelines for different stakeholders.

Next Steps

1. Multi-Database Support

Replace the PostgreSQL node with MySQL, SQL Server, or any supported database. The workflow logic remains identical while connecting to different data sources. This flexibility makes the solution valuable across diverse technology stacks.

2. Advanced Scheduling

Modify the Schedule Trigger for different frequencies. Set up daily reports for operational metrics, monthly reports for strategic planning, or quarterly reports for board meetings. Each schedule can target different recipient groups with tailored content.

3. Enhanced Formatting

Extend the Code node to include charts and visualizations using Chart.js, conditional formatting based on performance thresholds, or executive summaries with key insights. The HTML output supports rich formatting and embedded graphics.

4. Multi-Recipient Distribution

Add logic to send different reports to different stakeholders. Sales managers receive individual team reports, executives receive high-level summaries, and finance teams receive revenue-focused metrics. This targeted approach ensures each audience gets relevant information.

Conclusion

 
This automated SQL reporting workflow demonstrates how n8n bridges the gap between data science expertise and operational efficiency. By combining database integration, scheduling, and email automation, you can eliminate routine reporting tasks while delivering professional results to stakeholders.

The workflow’s modular design makes it particularly valuable for data teams managing multiple reporting requirements. You can duplicate the workflow for different databases, modify the SQL queries for various metrics, and adjust the formatting for different audiences—all without writing custom scripts or managing server infrastructure.

Unlike traditional ETL tools that require extensive configuration, n8n’s visual interface makes complex data workflows accessible to both technical and non-technical team members. Your SQL expertise remains the core value, while n8n handles the automation infrastructure, scheduling reliability, and delivery mechanisms.

Most importantly, this approach scales with your organization’s needs. Start with simple weekly reports, then expand to include data visualizations, multi-database queries, or integration with business intelligence platforms. The foundation you build today becomes the automated reporting infrastructure that supports your team’s growth tomorrow.
 
 

Born in India and raised in Japan, Vinod brings a global perspective to data science and machine learning education. He bridges the gap between emerging AI technologies and practical implementation for working professionals. Vinod focuses on creating accessible learning pathways for complex topics like agentic AI, performance optimization, and AI engineering. He focuses on practical machine learning implementations and mentoring the next generation of data professionals through live sessions and personalized guidance.

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“You can cut & paste your entire source code file into the query entry box on grok.com and Grok 4 will fix it for you!” he said in a post on X. “This is what everyone at xAI does. Works better than Cursor,” he added. 

As announced by Musk, the company also plans to launch a coding model focused on being “both fast and smart,” expected to be released in a few weeks.

Both models are available immediately and come bundled with access to Super Grok tiers, where users can direct a network of Grok agents to assist with research and productivity.

Grok 4 is also accessible via API, and xAI claims the model leads key reasoning benchmarks, including ARC-AGI-2.

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Users claim that Cursor’s Pro plan, which once promised unlimited usage, has effectively been capped. “Cursor constantly changing their pricing without any proper announcement or documentation recently is a pretty bad and frustrating move for users,” said one user.

Cursor has issued a clarification, admitting they “missed the mark” with the updated pricing and are fully refunding affected users. But that hasn’t quelled the criticism, as Reddit threads are filled with complaints that go beyond pricing alone.