ANALYTICS SOLUTION

Transform Your
PostgreSQL Analytics

Enhance your PostgreSQL workflows with AI-powered analytics. Get instant insights, automated optimization, and intelligent analytics.

88%
Performance
65%
Cost Reduction
92%
Query Speed
78%
Automation

Key Features for PostgreSQL

Advanced Full-Text Search

Enhance your data operations with advanced full-text search

Learn more →

Custom Extensions

Enhance your data operations with custom extensions

Learn more →

JSON/JSONB Operations

Enhance your data operations with json/jsonb operations

Learn more →

Materialized Views

Enhance your data operations with materialized views

Learn more →

Table Partitioning

Enhance your data operations with table partitioning

Learn more →

Parallel Query Processing

Enhance your data operations with parallel query processing

Learn more →

Real-World Examples

Use Case:

"Create an intelligent search and analytics system"

Solution:

                        
-- Create necessary extensions and indexes
CREATE EXTENSION IF NOT EXISTS pg_trgm;
CREATE EXTENSION IF NOT EXISTS unaccent;

CREATE MATERIALIZED VIEW product_search_index AS
WITH product_details AS (
    SELECT 
        p.product_id,
        p.product_name,
        p.description,
        p.price,
        c.category_name,
        ARRAY_AGG(t.tag_name) as tags,
        AVG(r.rating) as avg_rating,
        COUNT(r.rating) as review_count,
        SUM(s.quantity) as total_sales
    FROM products p
    LEFT JOIN categories c ON p.category_id = c.category_id
    LEFT JOIN product_tags pt ON p.product_id = pt.product_id
    LEFT JOIN tags t ON pt.tag_id = t.tag_id
    LEFT JOIN reviews r ON p.product_id = r.product_id
    LEFT JOIN sales s ON p.product_id = s.product_id
    GROUP BY p.product_id, p.product_name, p.description, p.price, c.category_name
)
SELECT 
    product_id,
    product_name,
    description,
    price,
    category_name,
    tags,
    avg_rating,
    review_count,
    total_sales,
    setweight(to_tsvector('english', product_name), 'A') ||
    setweight(to_tsvector('english', category_name), 'B') ||
    setweight(to_tsvector('english', COALESCE(array_to_string(tags, ' '), '')), 'C') ||
    setweight(to_tsvector('english', description), 'D') as document_vector
FROM product_details;

-- Create GIN index for fast text search
CREATE INDEX idx_product_search ON product_search_index USING gin(document_vector);

-- Intelligent search function
CREATE OR REPLACE FUNCTION search_products(
    search_query TEXT,
    min_price DECIMAL = NULL,
    max_price DECIMAL = NULL,
    min_rating DECIMAL = NULL,
    category_filter TEXT = NULL
) RETURNS TABLE (
    product_id INT,
    product_name TEXT,
    category_name TEXT,
    price DECIMAL,
    avg_rating DECIMAL,
    review_count INT,
    search_rank DECIMAL,
    matched_terms TEXT[]
) AS $$
BEGIN
    RETURN QUERY
    SELECT 
        psi.product_id,
        psi.product_name,
        psi.category_name,
        psi.price,
        psi.avg_rating,
        psi.review_count,
        ts_rank(document_vector, query) * (1 + ln(COALESCE(review_count, 0) + 1)) as search_rank,
        ARRAY(
            SELECT token 
            FROM ts_debug(search_query) 
            WHERE alias = 'word'
        ) as matched_terms
    FROM 
        product_search_index psi,
        to_tsquery('english', regexp_replace(search_query, '\s+', ' & ', 'g')) query
    WHERE 
        document_vector @@ query
        AND (min_price IS NULL OR price >= min_price)
        AND (max_price IS NULL OR price <= max_price)
        AND (min_rating IS NULL OR avg_rating >= min_rating)
        AND (category_filter IS NULL OR category_name = category_filter)
    ORDER BY search_rank DESC
    LIMIT 50;
END;
$$ LANGUAGE plpgsql;

-- Example usage
SELECT * FROM search_products(
    'organic sustainable food',
    min_price := 10,
    max_price := 100,
    min_rating := 4.0,
    category_filter := 'Organic Foods'
);
                    

Explanation:

PostgreSQL advanced search features: • Full-text search with weights • Trigram similarity matching • Custom function creation • Materialized views for performance • Complex ranking algorithm Implementation highlights: 1. Multi-field search vectors 2. Weighted relevance scoring 3. Price and rating filtering 4. Category-based filtering 5. Review count boosting Perfect for: - E-commerce platforms - Content management systems - Product catalogs - Search optimization

Common Use Cases

Real-time Data Analysis

Optimize your PostgreSQL analytics with AI-powered automation

Custom Dashboard Creation

Optimize your PostgreSQL analytics with AI-powered automation

Automated Reporting

Optimize your PostgreSQL analytics with AI-powered automation

Predictive Analytics

Optimize your PostgreSQL analytics with AI-powered automation

Performance Monitoring

Optimize your PostgreSQL analytics with AI-powered automation

Trend Analysis

Optimize your PostgreSQL analytics with AI-powered automation

Why Choose AI-Powered PostgreSQL?

Improved Performance

Optimize your PostgreSQL queries automatically for better performance and reduced resource usage.

Cost Reduction

Lower operational costs through intelligent resource management and automated optimization.

Time Savings

Automate routine analytics tasks and focus on strategic initiatives.

Enhanced Security

Built-in security best practices and automated compliance monitoring.

Easy Integration

Simple Setup

Connect your PostgreSQL instance with just a few clicks

Secure Connection

Enterprise-grade encryption and security measures

Instant Results

Start seeing improvements immediately after integration

Simple, Transparent Pricing

Starter

Free
  • Basic Analytics
  • 5 Queries
  • Community Support
Get Started

Professional

$49/month
  • Advanced Analytics
  • 500 Queries
  • Priority Support
Get Started

Enterprise

Custom
  • Custom Solutions
  • Dedicated Support
  • SLA Guarantee
Get Started

Ready to Transform Your PostgreSQL Analytics?

Frequently Asked Questions

How does AI improve PostgreSQL Analytics?

Our AI technology automatically optimizes PostgreSQL queries, provides intelligent insights, and automates routine tasks, improving performance and reducing manual work.

Is it secure?

Yes, we implement enterprise-grade security measures including encryption, access controls, and compliance with industry standards.

How long does implementation take?

Most customers are up and running within a few hours, with full integration typically completed within a week.