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Streaming data from Kafka to PostgreSQL is a common requirement for modern event-driven architectures. Kafka enables scalable real-time event processing, while PostgreSQL provides reliable relational storage for applications, analytics, and data synchronization.
This guide covers how Kafka data flows into PostgreSQL, common integration methods, and key practices for building reliable real-time data pipelines.
In a real-time data pipeline, Apache Kafka acts as an event streaming platform, while PostgreSQL serves as the relational database that stores and queries processed data.
Streaming data from Kafka to PostgreSQL means continuously consuming events from Kafka topics and writing them into PostgreSQL tables with low latency.
This integration is commonly used for:
In the Kafka ecosystem, a source connector moves data into Kafka, while a sink connector moves data from Kafka into external systems such as PostgreSQL.
Streaming data from Kafka is different from CDC and database replication, which are often used for database synchronization and availability scenarios.
| Integration Pattern | Kafka→PG Streaming | CDC | DB Replication |
|---|---|---|---|
| Purpose |
Write Kafka event data into PostgreSQL tables. |
Stream database changes into Kafka or other systems. |
Maintain database copies across environments. |
| Data Unit |
Event messages (JSON, Avro, Protobuf). |
Insert/update/delete events. | Transactions or log records. |
| Primary Use Case |
Real-time analytics and data integration. |
Real-time sync, event-driven architectures. | Disaster recovery, HA, read scaling. |
A reliable Kafka-to-PostgreSQL pipeline involves several components that move data from Kafka topics into relational tables. Understanding this data flow helps explain how different integration methods, such as Kafka Connect and custom consumers, work behind the scenes.
A Kafka producer is an application that publishes records to Kafka topics. These records are stored as key-value messages and are typically serialized in formats such as JSON, Apache Avro, or Protocol Buffers.
Kafka topics are divided into partitions to support scalability and parallel processing. When a producer sends a message, Kafka assigns it to a partition based on the message key. Messages with the same key are written to the same partition, which helps maintain their processing order.
On the receiving side, a consumer or integration connector reads messages from Kafka topics and writes them into PostgreSQL tables. The typical workflow includes:
Because PostgreSQL writes usually have higher latency than Kafka reads, batching multiple records before writing them to the database is important for maintaining throughput and reducing overhead.
There are three common approaches to stream data from Kafka to PostgreSQL. The right choice depends on factors such as data volume, transformation requirements, latency expectations, and operational complexity.
Kafka Connect is an open-source framework within the Apache Kafka ecosystem that enables data movement between Kafka and external systems.
The JDBC Sink Connector is a configuration-based connector that consumes records from Kafka topics and writes them into PostgreSQL tables without requiring custom consumer code.
How the JDBC Sink Connector Works
The JDBC Sink Connector runs within a Kafka Connect cluster and continuously consumes records from configured Kafka topics. It converts Kafka records into database operations based on the connector configuration, then writes the data into PostgreSQL using JDBC.
The connector can process data serialized in formats such as JSON, Avro, or other supported formats through Kafka converters. For schema-based formats like Avro, integration with Schema Registry is commonly used to manage schema compatibility.
Sample Connector Configuration
The following example shows a typical JDBC Sink Connector configuration for writing order data from a Kafka topic into PostgreSQL:
{
"name": "postgres-order-sink",
"config": {
"connector.class": "io.confluent.connect.jdbc.JdbcSinkConnector",
"tasks.max": "2",
"topics": "customer_orders",
"connection.url": "jdbc:postgresql://postgres-db.internal:5432/ecommerce",
"connection.user": "app_user",
"connection.password": "secure_db_password_123",
"insert.mode": "upsert",
"pk.mode": "record_key",
"pk.fields": "order_id",
"auto.create": "true",
"auto.evolve": "true"
}
}
auto.evolve can simplify development and testing, it should be used carefully in production environments. Automatic schema changes may introduce unexpected database modifications if upstream data structures change without proper review.Best For
Kafka Connect with a JDBC Sink Connector is suitable for straightforward data ingestion pipelines with relatively stable schemas. It works well for teams that need reliable Kafka-to-PostgreSQL data movement without building and maintaining custom consumers.
When you need full control over data transformations, filtering, routing, or application-specific logic, a custom consumer can be a flexible approach. Instead of using a pre-built connector, you can build a lightweight application with a programming language such as Python or Go to consume Kafka messages and write data to PostgreSQL.
(Kafka Topic) —> [Custom Consumer Application (Python/Go)] —> [PostgreSQL Database]
Consuming and Writing Data with Python
A custom consumer connects directly to the Kafka cluster, polls for new records, processes messages in the application layer, and writes the results to PostgreSQL.
The following example uses the confluent-kafka Python client to consume JSON messages and psycopg2 to write records to PostgreSQL with an upsert operation:
import json
import psycopg2
from confluent_kafka import Consumer, KafkaError
# Kafka consumer configuration
kafka_config = {
'bootstrap.servers': 'kafka.internal:9092',
'group.id': 'postgres-ingest-group',
'auto.offset.reset': 'earliest',
'enable.auto.commit': False
}
# PostgreSQL database connection
db_conn = psycopg2.connect("host=postgres-db.internal dbname=ecommerce user=app_user password=secure_password")
db_cursor = db_conn.cursor()
consumer = Consumer(kafka_config)
consumer.subscribe(['customer_orders'])
try:
while True:
msg = consumer.poll(timeout=1.0)
if msg is None:
continue
if msg.error():
if msg.error().code() != KafkaError._PARTITION_EOF:
print(f"Consumer error: {msg.error()}")
continue
payload = json.loads(msg.value().decode('utf-8'))
insert_query = """
INSERT INTO customer_orders (order_id, customer_id, total_amount)
VALUES (%s, %s, %s)
ON CONFLICT (order_id) DO UPDATE
SET total_amount = EXCLUDED.total_amount;
"""
db_cursor.execute(
insert_query,
(
payload['order_id'],
payload['customer_id'],
payload['total_amount']
)
)
db_conn.commit()
# Commit the Kafka offset after the database write succeeds
consumer.commit(msg, asynchronous=False)
except KeyboardInterrupt:
pass
finally:
db_cursor.close()
db_conn.close()
consumer.close()
Best For
A custom consumer approach works well for pipelines that require custom transformations, data enrichment, filtering, or conditional routing before writing to PostgreSQL.
It provides maximum flexibility but requires teams to manage application code, error handling, scaling, and operational monitoring.
For large-scale pipelines that require complex event processing, window-based calculations, stateful operations, or stream joins, simple connectors and custom consumers may not provide enough processing capabilities.
In these scenarios, stream processing frameworks such as Apache Flink and Apache Spark Structured Streaming are commonly used.
(Kafka Topic) —> [Flink / Spark Processing Engine] —> [PostgreSQL Database]
Using Flink with a JDBC Sink
Apache Flink is designed for low-latency stream processing and supports stateful computations over continuous data streams. With Flink’s DataStream API, teams can filter, transform, and enrich Kafka events before writing processed results into PostgreSQL.
The following Java example shows how Flink’s JdbcSink can write processed stream data into a PostgreSQL table:
import org.apache.flink.connector.jdbc.JdbcConnectionOptions;
import org.apache.flink.connector.jdbc.JdbcExecutionOptions;
import org.apache.flink.connector.jdbc.JdbcSink;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
public class FlinkPostgresStreamingJob {
public static void main(String[] args) throws Exception {
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// Consume and process stream from Kafka
DataStream processedStream = env.fromSource(...)
.filter(event -> event.getAmount() > 10.0);
// Write processed data to PostgreSQL
processedStream.addSink(JdbcSink.sink(
"INSERT INTO processed_orders (order_id, amount) VALUES (?, ?) " +
"ON CONFLICT (order_id) DO UPDATE SET amount = EXCLUDED.amount",
(statement, order) -> {
statement.setString(1, order.getOrderId());
statement.setDouble(2, order.getAmount());
},
JdbcExecutionOptions.builder()
.withBatchSize(1000)
.withBatchIntervalMs(200)
.withMaxRetries(5)
.build(),
new JdbcConnectionOptions.JdbcConnectionOptionsBuilder()
.withUrl("jdbc:postgresql://postgres-db.internal:5432/ecommerce")
.withDriverName("org.postgresql.Driver")
.withUsername("app_user")
.withPassword("secure_password")
.build()
));
env.execute("Flink-to-PostgreSQL-Sink");
}
}
When Spark Structured Streaming Fits Better
Apache Spark Structured Streaming uses a micro-batch execution model by default, making it a good choice for teams already working with the Spark ecosystem.
Spark may be a better fit when:
Best For
Stream processing frameworks are suitable for large-scale pipelines that require advanced transformations, stateful processing, data enrichment, or combining multiple streaming sources before loading results into PostgreSQL.
Running a Kafka-to-PostgreSQL pipeline in production requires careful attention to reliability, performance, and data consistency. Proper monitoring, error handling, and recovery strategies help keep the pipeline stable as data volume and processing complexity increase.
Consumer lag measures the difference between the latest offset available in a Kafka partition and the offset that a consumer group has processed. When lag continues to grow, the consumer cannot keep up with incoming events, causing delays before data reaches PostgreSQL.
To reduce and manage consumer lag:
Streaming pipelines must handle unexpected failures, including malformed messages, schema issues, and temporary database connection problems. Without proper error handling, a single problematic record can interrupt message processing.
Recommended practices include:
Kafka’s message retention capability allows teams to replay historical events when recovering from application bugs, processing failures, or data synchronization issues.
To support safe recovery and replay:
Each method covered here solves a different problem. Kafka Connect works well when your schema is stable and you just need data flowing with minimal code. A custom consumer makes sense when you need fine-grained control over message processing. Flink or Spark earns its complexity when the pipeline needs real transformations, joins, or aggregations.
All three assume Kafka already has clean, timely event data. Getting that data into Kafka reliably from a live production database, without lag, schema mismatches, or silent drift, is a separate problem. Info2soft’s i2Stream is built for that earlier stage.
i2Stream comes with several features relevant to Kafka-adjacent pipelines:
For teams whose real bottleneck is getting reliable, low-latency data out of a production database in the first place, i2Stream handles that layer so the Kafka Connect, custom consumer, or Flink/Spark job downstream has something trustworthy to work with. In addition, Info2soft also offers i2CDP for continuous, byte-level data protection when the goal shifts from streaming to disaster recovery.
Streaming data from Kafka to PostgreSQL isn’t a one-size-fits-all decision. Kafka Connect gets you there fastest when your schema is stable, a custom consumer gives you control when the logic gets specific, and Flink or Spark earns its place when transformation is part of the job.
Whichever method you choose, the pipeline is only as reliable as the data feeding into Kafka in the first place. That’s where tools like Info2soft‘s i2Stream come in, keeping the source data accurate and current so downstream streaming doesn’t inherit upstream problems.
Start with the method that matches your team’s current needs, and revisit the choice as your pipeline’s complexity grows.
