# 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:

```python
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**:

```pgsql
-- 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