Stream Processing with Python: A Practical Guide (2026)
Python developers can process streaming data using Faust, Quix Streams, Bytewax, or RisingWave (via psycopg2). The simplest approach: connect Python to a streaming database via PostgreSQL driver and query real-time materialized views — no streaming framework needed.
Python Streaming Options Compared
| Tool | Approach | Learning Curve | Kafka Required | SQL Support |
| RisingWave + psycopg2 | Query streaming views | Very low | Optional | ✅ Full PG SQL |
| Faust | Python stream processor | Medium | ✅ | ❌ |
| Quix Streams | Python stream processor | Medium | ✅ | ❌ |
| Bytewax | Python dataflow | Medium | Optional | ❌ |
| Kafka-python | Raw consumer | Low | ✅ | ❌ |
Simplest: Python + RisingWave
import psycopg2
# Connect to RisingWave (same as PostgreSQL)
conn = psycopg2.connect(host='risingwave', port=4566, dbname='dev', user='root')
cursor = conn.cursor()
# Query real-time streaming views
cursor.execute('SELECT * FROM real_time_metrics WHERE ts > NOW() - INTERVAL %s', ('5 minutes',))
for row in cursor.fetchall():
print(row)
# With pandas
import pandas as pd
from sqlalchemy import create_engine
engine = create_engine('postgresql://root@risingwave:4566/dev')
df = pd.read_sql('SELECT * FROM active_users', engine)
No Kafka consumer code, no streaming framework. The streaming computation happens in RisingWave; Python just queries results.
Frequently Asked Questions
Do I need a streaming framework for Python stream processing?
Not if you use a streaming database. RisingWave handles the streaming computation with SQL. Python simply queries the results via psycopg2 or SQLAlchemy — the same way you'd query PostgreSQL.
What is the best Python streaming library?
For simple use cases: RisingWave + psycopg2 (no streaming library needed). For embedded stream processing: Faust or Quix Streams. For dataflow processing: Bytewax. For raw Kafka consumption: kafka-python or confluent-kafka.
