How I Saved $800+ Daily Using DuckDB & Apache Superset Instead of AWS Redshift for Analytics

The Problem: Skyrocketing AWS Analytics Costs

When managing analytics for our loan management system, we initially turned to the standard AWS stack: Amazon Redshift for data warehousing and AWS Glue for ETL pipelines. The result? A shocking $800 bill for just one day of operation (We obviously asked for waiver and thankfully received it).

For a growing startup or mid-sized company, this translates to potentially $24,000+ monthly just for analytics infrastructure. We knew there had to be a better way.

The Solution: A Cost-Effective Modern Data Stack

After evaluating multiple alternatives, we built a lean analytics pipeline using:

• DuckDB - An in-process analytical database

• Apache Superset - Open-source data visualization platform

• AWS Fargate - Pay-per-use container service

• Amazon S3 - Cost-effective data storage

The result? We reduced our analytics costs by over 95% while maintaining performance and scalability.

Architecture Overview: From RDS to Dashboard

Step 1: Data Export from RDS to S3

Our loan management data resided in Amazon RDS. Instead of continuous replication or expensive real-time sync, we leveraged Export to S3 feature:

• Exports are stored as Parquet files in S3

• Large tables are automatically chunked into multiple Parquet files

• Parquet format provides excellent compression (reducing storage costs)

• Exports can be scheduled during off-peak hours

Cost benefit: S3 storage costs pennies compared to maintaining a live Redshift cluster.

Step 2: Data Transformation with Python and Pandas

While DuckDB can read Parquet files directly, we encountered a critical challenge: data type consistency. Dates and other fields were often stored as strings in the Parquet exports, making aggregate queries impossible.

Here's our transformation approach:

import pandas as pd
import duckdb

# Read Parquet files with proper transformations
def transform_loan_data(parquet_path):
    df = pd.read_parquet(parquet_path)
    
    # Convert date strings to proper datetime
    df['loan_date'] = pd.to_datetime(df['loan_date'])
    df['disbursement_date'] = pd.to_datetime(df['disbursement_date'])
    
    # Transform other fields
    df['loan_amount'] = df['loan_amount'].astype(float)
    df['status'] = df['status'].str.upper().str.strip()
    
    return df

# Load into DuckDB
conn = duckdb.connect('analytics.duckdb')
df_transformed = transform_loan_data('s3://bucket/loans/*.parquet')
conn.execute("CREATE TABLE loans AS SELECT * FROM df_transformed")

Why this approach worked:

• Pandas provided flexible data type conversions

• We could apply business logic during transformation

• Complex nested structures in Parquet could be flattened

• Data quality checks could be implemented in Python

Step 3: Pre-Computing Analytics in DuckDB

To ensure lightning-fast dashboard loading, we pre-computed key metrics and stored them in temporary tables within DuckDB:

-- Daily loan disbursements
CREATE TEMP TABLE daily_disbursements AS
SELECT 
    DATE_TRUNC('day', disbursement_date) as date,
    COUNT(*) as loan_count,
    SUM(loan_amount) as total_amount,
    AVG(interest_rate) as avg_interest_rate
FROM loans
WHERE status = 'DISBURSED'
GROUP BY DATE_TRUNC('day', disbursement_date);

-- Portfolio aging analysis
CREATE TEMP TABLE portfolio_aging AS
SELECT 
    CASE 
        WHEN days_overdue = 0 THEN 'Current'
        WHEN days_overdue <= 30 THEN '1-30 Days'
        WHEN days_overdue <= 60 THEN '31-60 Days'
        ELSE '60+ Days'
    END as aging_bucket,
    COUNT(*) as loan_count,
    SUM(outstanding_amount) as total_outstanding
FROM loans
WHERE status IN ('ACTIVE', 'OVERDUE')
GROUP BY aging_bucket;

Performance advantage: Dashboard queries now fetch pre-aggregated data instead of running complex calculations on-the-fly.

Step 4: Visualization with Apache Superset

Apache Superset connected directly to our DuckDB database using the DuckDB SQLAlchemy driver:

1. Add DuckDB as a data source in Superset

2. Create datasets pointing to our pre-computed tables

3. Build interactive dashboards with various chart types

4. Set up caching for frequently accessed visualizations

The user experience was identical to enterprise BI tools like Tableau or Looker, but at zero licensing cost.

Cost Comparison: The Numbers Don't Lie

Traditional AWS Stack (Redshift + Glue)

• Redshift cluster: $800/day (2 nodes, 24/7)

• AWS Glue: ~$0.44 per DPU-hour

• Monthly estimate: $24,000+

Our DuckDB Solution

• AWS Fargate: ~$30/month (running 8 hours/day)

• S3 storage: ~$5/month (200GB compressed Parquet)

• Fargate for Superset: ~$50/month

• Monthly total: ~$85

Savings: Over $23,900 per month (99.6% reduction)

Additional Benefits Beyond Cost Savings

1. Operational Simplicity

• No cluster management or scaling concerns

• DuckDB is embedded - no separate database server

• Superset runs in a single container

2. Development Speed

• Instant local testing with DuckDB

• SQL-first approach with no complex ETL frameworks

• Easy iteration on analytics queries

3. Flexibility

• Start/stop Fargate tasks when not in use

• Export DuckDB database for local analysis

• Version control your entire analytics stack

4. Performance

• DuckDB's columnar engine excels at analytical queries

• In-process execution eliminates network latency

• Pre-aggregated tables provide sub-second response times

When This Approach Works Best

This architecture is ideal for:

• Batch analytics (not real-time streaming)

• Small to medium datasets (up to 100GB)

• Cost-conscious teams without enterprise budgets

• Infrequent queries (not 24/7 user-facing applications)

When to Consider Alternatives

Stick with Redshift or Snowflake if you need:

• Real-time analytics with sub-second data freshness

• Multi-user concurrency with hundreds of simultaneous queries

• Petabyte-scale data warehousing

• Complex data governance and access controls

Conclusion: Modern Analytics Doesn't Require Big Budgets

By combining open-source tools like DuckDB and Apache Superset with cost-effective AWS services, we proved that sophisticated analytics doesn't require enterprise-level spending.

Our loan management analytics now runs for less than the cost of a nice dinner, compared to the $24,000+ monthly Redshift bills we avoided. More importantly, we maintained full control over our data pipeline and gained the flexibility to iterate quickly.

The modern data stack is democratizing analytics—and your infrastructure costs should reflect that.

Frequently Asked Questions

Q: Can DuckDB handle my production workload?
A: DuckDB excels for analytical workloads up to 100GB. For larger datasets or high concurrency, consider distributed solutions like ClickHouse.

Q: How often should I refresh the data?
A: It depends on your needs. We run daily exports from RDS, but you could schedule hourly or even more frequently.

Q: Is Apache Superset production-ready?
A: Yes, Superset is used by companies like Airbnb, Netflix, and Twitter. Ensure proper security configuration for production use.

Q: What about data security?
A: Use IAM roles for S3 access, VPC networking for Fargate, and Superset's built-in authentication. Encrypt data at rest in S3.

Relevant Keywords

DuckDB, Apache Superset, AWS cost savings, Redshift alternative, analytics on a budget, data visualization, ETL pipeline, AWS Fargate, Parquet files, loan management analytics, modern data stack