Step 1: Set up PostgreSQL with Sample Data
Before you can source data from PostgreSQL into Kafka, you need a running instance of PostgreSQL with some data in it. This step involves:
Setting up PostgreSQL: You spin up a PostgreSQL container (using Docker) to simulate a production database. PostgreSQL is a popular relational database, and in this case, it serves as the source of your data.
Create a database and tables: You define a schema with a table (e.g., users) to hold some sample data. The table contains columns like id, name, and email. In a real-world scenario, your tables could be more complex, but this serves as a simple example.
Populate the table with sample data: By inserting some rows into the users table, you simulate real data that will be ingested into Kafka.
Why This Step is Important:
You need the data to be present in PostgreSQL because this is the source that the JDBC Source Connector will pull from. The connector will continuously monitor this database for any changes (using polling mechanisms) and send the changes to Kafka.
Step 2: Set up Docker-based Confluent Platform Kafka Cluster
Apache Kafka is a distributed event streaming platform that you’ll use to transport data between PostgreSQL and S3. Confluent provides a distribution of Kafka with additional tools for easier management and integration
Key Components:
Zookeeper: Kafka uses Zookeeper for managing cluster metadata, which helps in keeping track of broker statuses and configurations.
Kafka Brokers: These are the core of Kafka. They store the actual messages (in topics) and are responsible for receiving and sending data.
Kafka Connect: This is a tool that allows you to easily integrate Kafka with external systems, like databases (PostgreSQL) and cloud storage (S3). It’s used to run source connectors and sink connectors.
By using Docker Compose, you can quickly set up a local Confluent Kafka cluster with Zookeeper, Kafka brokers, and Kafka Connect services all running in containers.
Why This Step is Important:
You need Kafka as an intermediary to store and transfer data from PostgreSQL to S3. Kafka ensures reliable data streaming, and Kafka Connect makes it easy to connect PostgreSQL and S3 to Kafka without writing custom code.
Step 3: Configure JDBC Source Connector
The JDBC Source Connector is the key component that pulls data from PostgreSQL into Kafka.
Key Concepts:
JDBC (Java Database Connectivity): This is a standard Java API for connecting to relational databases. The JDBC Source Connector uses JDBC to connect to PostgreSQL and retrieve data
Kafka Topics: In Kafka, data is organized into topics. The source connector reads from the specified database and writes data to a Kafka topic.
Incremental Mode: The connector is configured to read data in incremental mode (based on the primary key or timestamp column). This means it only picks up new or updated data since the last poll, reducing the load on the database.
Poll Interval: The JDBC Source Connector polls the database for new data at a regular interval (e.g., every 10 seconds). This polling mechanism ensures continuous ingestion of data into Kafka.
Why This Step is Important:
This step configures the connector to continuously monitor PostgreSQL and send data to Kafka. You specify which tables to monitor (users table in this case), how to identify new rows (via id), and where to send that data (to Kafka topics).
Step 4: Configure S3 Sink Connector
The S3 Sink Connector takes data from Kafka topics and writes it to an S3 bucket
Key Concepts:
S3 (Simple Storage Service): S3 is an object storage service provided by AWS, where you can store large volumes of data (like JSON, CSV, or Parquet files). The S3 Sink Connector will take the data flowing through Kafka topics and store it in S3.
Topic Mapping: You map a Kafka topic (e.g., postgres-users) to an S3 location. The connector will consume messages from this topic and write them to S3, either as files or in some format (e.g., JSON).
AWS Credentials: To interact with S3, the connector needs AWS credentials (access key and secret key). These credentials allow the connector to upload data to S3.
Storage Format: The connector allows you to specify the format of the data stored in S3. Common formats are JSON, Avro, and Parquet. In this case, we’re using JSON format, but you could configure it for Avro or Parquet if needed.
Why This Step is Important:
Once the data reaches Kafka, you need a way to persist it to a durable storage location. The S3 Sink Connector handles this by automatically reading from Kafka topics and writing the data to AWS S3, which is highly durable and cost-effective for storage.
Step 5: Verify the Data Flow
After setting up the connectors, it’s important to verify that the data is flowing properly. This involves:
Kafka Topic Verification:
After the JDBC Source Connector has ingested data from PostgreSQL, you should verify that the data has been written to the specified Kafka topic. You can do this using Kafka’s built-in tools or the Kafka UI. The data in Kafka will be in the form of JSON (or the configured format).
S3 Bucket Verification:
After the S3 Sink Connector has processed data from the Kafka topic, you should verify that the data is being written to your S3 bucket. You can check the contents of your S3 bucket via the AWS Management Console. The data should appear as files in the specified format (e.g., JSON) in the bucket.
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