Why Python Data Engineers Should Know Kafka and Flink

Excellent integrations make these frameworks seamlessly accessible to Python developers, allowing them to use these powerful tools without deep Java knowledge.

Oct 1st, 2025 8:00am by Diptiman Raichaudhuri

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Modern data platforms demand real-time context to extract meaningful insights. With AI agents becoming increasingly prevalent, this contextual accuracy is critical for minimizing hallucinations and ensuring reliable results. Data engineers who use Python, one of the most popular languages in the world, increasingly need to work with Apache Kafka and Apache Flink for streaming data processing.

While Python dominates data engineering (holding the No. 1 spot in both TIOBE and PYPL rankings), Apache Kafka and Apache Flink are both written in Java. However, excellent Python integrations make these frameworks seamlessly accessible to Python developers, allowing them to leverage these powerful tools without needing deep Java knowledge.

Why Python Dominates Data Engineering

Python’s popularity in data engineering isn’t accidental; there are Python ports offered for virtually every major data framework, including:

Stream processing: PyFlink, Kafka Python SDKs
Batch processing: PySpark, Apache Airflow, Dagster
Data manipulation: PyArrow, Python SDK for DuckDB
Workflow orchestration: Apache Airflow, Prefect

This extensive ecosystem allows data engineers to build end-to-end pipelines while staying within Python’s familiar syntax and patterns. If you need to process real-time data streams — for user behavior analysis, anomaly detection or predictive maintenance, for example — Python provides the tools without forcing you to switch languages.

Apache Kafka: Stream Storage Made ‘Pythonic’

Apache Kafka has become the de facto standard for data streaming platforms, offering easy-to-use APIs, crucial replayability features, schema support and exceptional performance. While Apache Kafka is written in Java, Python developers access it through librdkafka, a high-performance C implementation that provides production-ready reliability.

The confluent-kafka-python library serves as the primary interface, offering thread-safe Producer, Consumer, and AdminClient classes compatible with Apache Kafka brokers version 0.8 and later, including Confluent Cloud and Confluent Platform. Installation is straightforward: pip install confluent-kafka.

Producer Implementation

Here’s how simple it is to publish messages to Kafka:

from confluent_kafka import Producer

p = Producer({'bootstrap.servers': 'mybroker1,mybroker2'})

def delivery_report(err, msg):
    """Called once for each message produced to indicate delivery result."""
    if err is not None:
        print('Message delivery failed: {}'.format(err))
    else:
        print('Message delivered to {} [{}]'.format(msg.topic(), msg.partition()))

for data in some_data_source:
    # Trigger delivery report callbacks from previous produce() calls
    p.poll(0)
    
    # Asynchronously produce a message
    p.produce('user_clicks', data.encode('utf-8'), callback=delivery_report)

# Ensure all messages are delivered
p.flush()

from confluent_kafka import Producer

p = Producer({'bootstrap.servers': 'mybroker1,mybroker2'})

def delivery_report(err, msg):

"""Called once for each message produced to indicate delivery result."""

if err is not None:

print('Message delivery failed: {}'.format(err))

else:

print('Message delivered to {} [{}]'.format(msg.topic(), msg.partition()))

for data in some_data_source:

# Trigger delivery report callbacks from previous produce() calls

p.poll(0)

# Asynchronously produce a message

p.produce('user_clicks', data.encode('utf-8'), callback=delivery_report)

# Ensure all messages are delivered

p.flush()

Consumer Implementation

Consuming messages is equally straightforward:

from confluent_kafka import Consumer

c = Consumer({
    'bootstrap.servers': 'mybroker',
    'group.id': 'mygroup',
    'auto.offset.reset': 'earliest'
})

c.subscribe(['user_clicks'])

while True:
    msg = c.poll(1.0)
    
    if msg is None:
        continue
    if msg.error():
        print("Consumer error: {}".format(msg.error()))
        continue
        
    print('Received message: {}'.format(msg.value().decode('utf-8')))

c.close()

from confluent_kafka import Consumer

c = Consumer({

'bootstrap.servers': 'mybroker',

'group.id': 'mygroup',

'auto.offset.reset': 'earliest'

})

c.subscribe(['user_clicks'])

while True:

msg = c.poll(1.0)

if msg is None:

continue

if msg.error():

print("Consumer error: {}".format(msg.error()))

continue

print('Received message: {}'.format(msg.value().decode('utf-8')))

c.close()

The confluent-kafka-python client maintains feature parity with the Java SDK while providing maximum throughput performance. Since it’s maintained by Confluent (which was founded by Kafka’s creator), it remains future-proof and production-ready.

Apache Flink: Stream Processing With PyFlink

While Kafka excels at storing data streams, processing and enriching those streams requires additional tools. Apache Flink serves as a distributed processing engine for stateful computations over unbounded and bounded data streams.

PyFlink provides a Python API that enables data engineers to build scalable batch and streaming workloads, from real-time processing pipelines to large-scale exploratory analysis, machine learning (ML) pipelines, and extract, transform, load (ETL) processes. Data engineers familiar with Pandas will find PyFlink’s Table API intuitive and powerful.

PyFlink APIs: Choosing Your Complexity Level

PyFlink offers two primary APIs:

Table API: High-level, SQL-like operations perfect for most use cases
DataStream API: Low-level control for fine-grained transformations

A common pattern involves applying aggregations and time-window operations (Tumbling or Hopping Windows) to Kafka topics, then outputting results to downstream topics. For example, transforming a ‘user_clicks’ topic into a ‘top_users’ summary.

Real-Time Transformations in Action

Here’s a PyFlink Table API job that processes streaming data with windowed aggregations:

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import EnvironmentSettings, StreamTableEnvironment

def main():
    env = StreamExecutionEnvironment.get_execution_environment()
    settings = EnvironmentSettings.in_streaming_mode()
    tenv = StreamTableEnvironment.create(env, settings)

    # Add Kafka connector
    env.add_jars("flink-sql-connector-kafka-4.0.0-2.0.jar")
    
    # Define windowed aggregation
    top_users_sql = """
                    SELECT
                        user_id,
                        COUNT(cURL) as cnt,
                        window_start,
  window_end
                    FROM TABLE(
                        TUMBLE(TABLE user_clicks, DESCRIPTOR(proctime), INTERVAL '30' MINUTE)
                    )
                    GROUP BY
                        window_start,
                        window_end,
                        user_id
                """
    
    result = tenv.sql_query(top_users_sql)
    # Execute and sink results
    tenv.execute_sql(sink_ddl)

from pyflink.datastream import StreamExecutionEnvironment

from pyflink.table import EnvironmentSettings, StreamTableEnvironment

def main():

env = StreamExecutionEnvironment.get_execution_environment()

settings = EnvironmentSettings.in_streaming_mode()

tenv = StreamTableEnvironment.create(env, settings)

# Add Kafka connector

env.add_jars("flink-sql-connector-kafka-4.0.0-2.0.jar")

# Define windowed aggregation

top_users_sql = """

SELECT

user_id,

COUNT(cURL) as cnt,

window_start,

window_end

FROM TABLE(

TUMBLE(TABLE user_clicks, DESCRIPTOR(proctime), INTERVAL '30' MINUTE)

)

GROUP BY

window_start,

window_end,

user_id

"""

result = tenv.sql_query(top_users_sql)

# Execute and sink results

tenv.execute_sql(sink_ddl)

This approach enables complex use cases like:

User behavior analysis from clickstream data
Anomaly detection in manufacturing processes
Predictive maintenance alerts from Internet of Things (IoT) telemetry

The Python Advantage in Modern Data Streaming

The combination of PyFlink and Python Kafka clients creates a powerful toolkit for Python-trained data engineers. You can contribute to data platform modernization without learning Java, leveraging existing Python expertise while accessing enterprise-grade streaming capabilities.

Key benefits include:

Familiar syntax: Stay within Python’s ecosystem
Production performance: librdkafka and Flink’s Java engine provide enterprise speed
Full feature access: No compromise on Kafka or Flink capabilities
Ecosystem integration: Seamless connection with other Python data tools

Getting started requires just two pip installs: pip install confluent-kafka and pip install apache-flink. From there, you can build sophisticated real-time data pipelines that rival any Java implementation.

As AI and real-time analytics continue driving data platform evolution, Python data engineers equipped with Kafka and Flink skills are positioned to lead this transformation. The barriers between Python productivity and Java performance have effectively disappeared, making this an ideal time to expand your streaming data expertise.

Diptiman Raichaudhuri is a staff developer advocate at Confluent. Raichaudhuri is an IT industry veteran with more than two decades of experience working at global product and software service delivery organizations. At Confluent, he works closely with developers around the...