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Overview

Perfect for data teams, researchers, and analysts who need to programmatically analyze onchain data, build custom reports, and create automated analytics pipelines.

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What You’ll Learn

  • Execute SQL queries via API
  • Retrieve and process results
  • Handle query parameters
  • Build analytics pipelines
  • Filter and transform data

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Prerequisites

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Quick Start

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1. Execute a Simple Query

Let’s start by executing a query to analyze recent DEX trading volume: 
from dune_client.client import DuneClient
import pandas as pd

# Initialize client
dune = DuneClient(api_key="YOUR_API_KEY")

# Execute SQL query
sql = """
SELECT 
    blockchain,
    DATE_TRUNC('hour', block_time) as hour,
    SUM(amount_usd) as volume_usd,
    COUNT(*) as trade_count
FROM dex.trades
WHERE block_time > now() - interval '24' hour
GROUP BY 1, 2
ORDER BY 2 DESC
"""

results = dune.run_sql(query_sql=sql)

# Convert to pandas DataFrame for analysis
df = pd.DataFrame(results.result.rows)
print(df.head())
The run_sql() function in our Python SDK automatically handles query execution and polling for completion. With cURL, you’ll need to poll the execution status endpoint and then fetch results manually.

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2. Use Parameterized Queries

For reusable analytics, create queries with parameters: 
from dune_client.client import DuneClient
from dune_client.query import QueryBase
from datetime import datetime, timedelta

dune = DuneClient(api_key="YOUR_API_KEY")

# Execute a saved query with parameters
query_id = 3493826  # Example: Popular DEX analysis query

query = QueryBase(query_id=query_id)
results = dune.run_query(query=query)

# Process results
for row in results.result.rows:
    protocol = row.get('protocol') or row.get('project', 'Unknown')
    volume = row.get('volume') or row.get('volume_usd', 0)
    print(f"{protocol}: ${volume:,.2f}")

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3. Build an Analytics Pipeline

Create a pipeline that runs multiple queries and combines results: 
from dune_client.client import DuneClient
import pandas as pd
from datetime import datetime

dune = DuneClient(api_key="YOUR_API_KEY")

def daily_dex_analysis():
    """Run daily DEX analytics pipeline"""
    
    # 1. Get overall volume
    volume_query = """
    SELECT 
        SUM(amount_usd) as total_volume,
        COUNT(DISTINCT taker) as unique_traders
    FROM dex.trades
    WHERE block_time > now() - interval '24' hour
    """
    volume_data = dune.run_sql(query_sql=volume_query)
    
    # 2. Get top protocols
    protocol_query = """
    SELECT 
        project as protocol,
        SUM(amount_usd) as volume,
        COUNT(*) as trades
    FROM dex.trades
    WHERE block_time > now() - interval '24' hour
    GROUP BY 1
    ORDER BY 2 DESC
    LIMIT 10
    """
    protocol_data = dune.run_sql(query_sql=protocol_query)
    
    # 3. Get chain breakdown
    chain_query = """
    SELECT 
        blockchain,
        SUM(amount_usd) as volume
    FROM dex.trades
    WHERE block_time > now() - interval '24' hour
    GROUP BY 1
    ORDER BY 2 DESC
    """
    chain_data = dune.run_sql(query_sql=chain_query)
    
    # Combine and process results
    report = {
        'timestamp': datetime.now(),
        'total_volume': volume_data.result.rows[0]['total_volume'],
        'unique_traders': volume_data.result.rows[0]['unique_traders'],
        'top_protocols': pd.DataFrame(protocol_data.result.rows),
        'chain_breakdown': pd.DataFrame(chain_data.result.rows)
    }
    
    return report

# Run pipeline
report = daily_dex_analysis()
print(f"24h Volume: ${report['total_volume']:,.2f}")
print(f"Unique Traders: {report['unique_traders']:,}")
print("\nTop Protocols:")
print(report['top_protocols'])

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4. Filter and Transform Results

Use Dune’s powerful filtering to refine results: 
from dune_client.client import DuneClient

dune = DuneClient(api_key="YOUR_API_KEY")

# Execute query with SQL-based filtering
sql = """
SELECT 
    blockchain,
    project,
    SUM(amount_usd) as volume_usd
FROM dex.trades
WHERE block_time > now() - interval '24' hour
    AND blockchain = 'ethereum'
    AND amount_usd > 1000000
GROUP BY 1, 2
ORDER BY 3 DESC
"""

results = dune.run_sql(query_sql=sql)

# Results are already filtered server-side
for row in results.result.rows:
    print(row)
Server-side filtering with Dune’s SQL-like WHERE clause syntax is unique to Dune’s API! It saves bandwidth and processing time compared to filtering client-side.

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Common Patterns

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Scheduled Analytics

Run analytics on a schedule (e.g., with cron, Airflow, or cloud scheduler): 
import schedule
import time
from dune_client.client import DuneClient

dune = DuneClient(api_key="YOUR_API_KEY")

def hourly_report():
    """Generate hourly DEX report"""
    sql = """
    SELECT 
        project,
        SUM(amount_usd) as hourly_volume
    FROM dex.trades
    WHERE block_time > now() - interval '1' hour
    GROUP BY 1
    ORDER BY 2 DESC
    """
    
    results = dune.run_sql(query_sql=sql)
    
    # Send to your dashboard/database/Slack
    send_to_dashboard(results.result.rows)

# Run every hour
schedule.every().hour.at(":00").do(hourly_report)

while True:
    schedule.run_pending()
    time.sleep(60)

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Onchain Monitoring

Monitor specific metrics and trigger alerts: 
from dune_client.client import DuneClient
import time

dune = DuneClient(api_key="YOUR_API_KEY")

def monitor_large_trades(threshold=1000000):
    """Alert on large trades"""
    sql = f"""
    SELECT *
    FROM dex.trades
    WHERE block_time > now() - interval '5' minute
    AND amount_usd > {threshold}
    ORDER BY block_time DESC
    """
    
    results = dune.run_sql(query_sql=sql)
    
    if results.result.rows:
        for trade in results.result.rows:
            send_alert(f"Large trade: ${trade['amount_usd']:,.2f} on {trade['blockchain']}")

# Run continuously
while True:
    monitor_large_trades()
    time.sleep(300)  # Check every 5 minutes

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Next Steps

Execute Queries API

Full API reference for query execution

Result Filtering

Advanced filtering techniques

Materialized Views

Create and manage materialized views

DuneSQL Functions

Learn DuneSQL functions and operators

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Example Use Cases

  • Research Reports: Generate daily/weekly reports on DeFi protocols, NFT markets, or chain activity
  • Automated Dashboards: Feed data to custom dashboards or BI tools
  • Alert Systems: Monitor metrics and trigger alerts when thresholds are met
  • Comparative Analysis: Compare metrics across chains, protocols, or time periods