Data SQL – Revolve Web

vim ~/revolveweb/data.sql — zsh
📖 README.md 🐍 developer.py 🗄️ data.sql 📦 projects.py 📧 contact.py
            -- ═══════════════════════════════════════════════════════════════
            -- Data Engineering & SQL Expertise
            -- Building pipelines, analytics, and data infrastructure
            -- ═══════════════════════════════════════════════════════════════
            

            -- ═══════════════════════════════════════════════════════════════
            -- [+] DATA ENGINEERING SKILLS
            -- ═══════════════════════════════════════════════════════════════
            

            CREATE TABLE data_skills (
                skill_category VARCHAR(50),
                technologies ARRAY<VARCHAR>,
                experience_level VARCHAR(20)
            );
            

            INSERT INTO data_skills VALUES
                ('AWS Data Services', ARRAY['Athena', 'Redshift', 'Glue', 'Kinesis', 'Firehose'], 'Production'),
                ('SQL Databases', ARRAY['PostgreSQL', 'MySQL', 'Aurora', 'DynamoDB'], 'Expert'),
                ('ETL Pipelines', ARRAY['Data Transformation', 'Incremental Loading', 'Data Quality'], 'Advanced'),
                ('Analytics', ARRAY['Time-Series', 'Aggregations', 'Window Functions'], 'Production');
            

            -- ═══════════════════════════════════════════════════════════════
            -- [+] AWS DATA PLATFORM EXPERTISE
            -- ═══════════════════════════════════════════════════════════════
            

            SELECT
                service,
                use_case,
                capabilities
            FROM aws_data_services
            WHERE experience = 'Production';
            

            -- Athena: Query S3 data lakes, partitioned Parquet/JSON, cost optimization
            -- Redshift: Data warehousing, star schemas, query performance tuning
            -- Glue: ETL jobs, data catalog, schema discovery, job orchestration
            -- Kinesis: Real-time streaming, data ingestion, analytics pipelines
            -- Firehose: Data delivery to S3/Redshift, transformation, buffering
            

            -- ═══════════════════════════════════════════════════════════════
            -- [+] SQL QUERY OPTIMIZATION
            -- ═══════════════════════════════════════════════════════════════
            

            SELECT
                COUNT(*) AS optimization_techniques
            FROM sql_skills
            WHERE category IN (
                'Partition Pruning',
                'Index Optimization',
                'Query Rewriting',
                'Join Strategies',
                'Window Functions',
                'CTEs & Subqueries'
            );
            

            -- ═══════════════════════════════════════════════════════════════
            -- [+] DATA PIPELINE PATTERNS
            -- ═══════════════════════════════════════════════════════════════
            

            CREATE VIEW pipeline_expertise AS
            SELECT
                'Incremental Loading' AS pattern,
                'MERGE/UPSERT operations, change data capture' AS description
            UNION ALL
            SELECT 'Data Transformation', 'Cleaning, enrichment, feature engineering'
            UNION ALL
            SELECT 'Streaming Processing', 'Real-time aggregation, windowing, event processing'
            UNION ALL
            SELECT 'Data Quality', 'Validation, anomaly detection, schema enforcement';
            

            -- ═══════════════════════════════════════════════════════════════
            -- [+] ANALYTICS & REPORTING
            -- ═══════════════════════════════════════════════════════════════
            

            WITH analytics_capabilities AS (
                SELECT 'Time-Series Analysis' AS capability
                UNION ALL SELECT 'Rolling Aggregations'
                UNION ALL SELECT 'Percentile Calculations'
                UNION ALL SELECT 'Cohort Analysis'
                UNION ALL SELECT 'Statistical Features'
            )
            SELECT * FROM analytics_capabilities;
            

            -- ═══════════════════════════════════════════════════════════════
            -- [+] DATABASE TECHNOLOGIES
            -- ═══════════════════════════════════════════════════════════════
            

            SELECT
                database_type,
                ARRAY_AGG(technology) AS technologies
            FROM database_experience
            GROUP BY database_type;
            

            -- Relational: PostgreSQL, MySQL, Aurora, SQL Server
            -- NoSQL: DynamoDB, DocumentDB
            -- Data Warehouses: Redshift, Snowflake
            -- Data Lakes: S3 + Athena, Parquet, JSON, CSV
            

            -- ═══════════════════════════════════════════════════════════════
            -- [+] FEATURE ENGINEERING FOR ML
            -- ═══════════════════════════════════════════════════════════════
            

            SELECT
                AVG(statistical_feature) AS mean,
                STDDEV(statistical_feature) AS stddev,
                PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY statistical_feature) AS median
            FROM ml_feature_engineering
            WHERE feature_type IN (
                'Aggregations',
                'Time-Based Features',
                'Rolling Windows',
                'Cross-Table Joins'
            );
            

            -- ═══════════════════════════════════════════════════════════════
            -- [+] DATA QUALITY & GOVERNANCE
            -- ═══════════════════════════════════════════════════════════════
            

            CREATE TABLE data_quality_practices (
                practice VARCHAR(100),
                implementation TEXT
            );
            

            INSERT INTO data_quality_practices VALUES
                ('Schema Validation', 'Enforcing data types, constraints, referential integrity'),
                ('Anomaly Detection', 'IQR method, statistical outliers, threshold monitoring'),
                ('Data Lineage', 'Tracking transformations, source to destination mapping'),
                ('Monitoring', 'Pipeline health checks, freshness metrics, error alerts');
            

            -- EOF
        
NORMAL data.sql [+]
sql utf-8 ln 1, col 1 100%
: e ~/revolveweb/data.sql