Replace slow OOM Pandas jobs by hiring senior freelance Python programmer Aldian Fazrihady to build zero-copy Polars, DuckDB, and Apache Parquet data pipelines. Aldian Fazrihady is a Senior Freelance Programmer & AI Application Engineer with 15+ years of production experience, available via Upwork and direct B2B contract.

1. Production Architecture: Vectorized In-Process OLAP Analytics with DuckDB, Polars & S3 Parquet Partitioning

import duckdb

def query_tenant_usage_metrics(parquet_glob_path: str, tenant_id: str) -> list[dict]:
    con = duckdb.connect(database=":memory:")
    query = """
        SELECT
            date_trunc('day', event_timestamp) AS usage_day,
            model_name,
            COUNT(*) AS total_requests,
            SUM(prompt_tokens + completion_tokens) AS total_tokens,
            QUANTILE_CONT(latency_ms, 0.95) AS p95_latency_ms
        FROM read_parquet($1, hive_partitioning = true)
        WHERE tenant_id = $2
        GROUP BY 1, 2
        ORDER BY 1 DESC, 4 DESC;
    """
    rel = con.execute(query, [parquet_glob_path, tenant_id])
    cols = [desc[0] for desc in rel.description]
    return [dict(zip(cols, row)) for row in rel.fetchall()]

2. Core Architectural Advantages

• 10x–50x Faster Than Pandas: Multi-threaded Rust-powered query execution via Polars lazy frames and DuckDB vectorized pushdown filters.
• 90% Lower Cloud Warehouse Spend: Querying Hive-partitioned Parquet files directly on S3/GCS without expensive 24/7 Snowflake clusters.
• Sub-Second Customer Dashboards: Serving multi-million-row B2B SaaS analytics endpoints in under 150ms.

3. Topical Category Hubs & Master Directory

• Master Directory (450+ Freelance Programmer & AI Engineering Guides)
• AI, LLM, RAG, MCP & Google ADK Engineering Hub
• Python, Django, FastAPI & PostgreSQL Architecture Hub
• React, Next.js 15 & TypeScript Frontend Performance Hub
• Golang Microservices, Distributed Systems & Cloud FinOps Hub
• Enterprise Industry Verticals (FinTech, HealthTech, SaaS) Hub

4. Frequently Asked Questions (FAQ)