In today's data-driven landscape, real-time analytics play a pivotal role in driving business success. To harness the power of real-time insights, we need a robust data pipeline that captures every change from the source database and makes it available for downstream consumers. In this detailed article, we'll explore a data pipeline that leverages Debezium and S3 to extract change data from Postgres and load it into S3, paving the way for real-time analytics and informed decision-making. Let's dive into the main components of this powerful data pipeline and discover the magic of Change Data Capture (CDC)! 🚀🔍
Understanding the Data Pipeline
Our data pipeline follows a well-defined process:
- Capture Changes with Debezium: We use Debezium to capture all changes made to the "commerce.products" and "commerce.users" tables in the Postgres database. Debezium acts as a powerful connector that reads the transaction log and streams change data to a Kafka queue, with one topic per table. 💼📊
- Push to Kafka Queue: The change data is pushed into a Kafka queue, making it available for downstream consumers. Kafka Connect Cluster plays a vital role in enabling seamless data transfer between Kafka and various systems, ensuring data availability and reliability. 🔄🌐
- S3 Sink for Data Loading: Our downstream consumer leverages an S3 sink connector to extract data from Kafka and load it into an S3 bucket. The data in S3 is stored with table-specific paths, ensuring easy accessibility for analytics. 🗂️💻
- Data Warehousing with duckDB: To ingest and analyze the data stored in S3, we rely on duckDB, an embedded analytical database that allows us to generate Slowly Changing Dimension Type 2 (SCD2) datasets. With SCD2, we can track historical changes in data, enriching our analytical capabilities. 📚📊
Main Components of the Data Pipeline
- Upstream System: The Postgres database serves as the source system, containing the "user" and "product" tables. To mimic real-time changes, a Python script creates fake create, update, and delete operations. 🐍💼
- Kafka Connect Cluster: This separate cluster handles data transfer between Kafka and various systems. Within this cluster, we deploy two open-source connectors:
- Debezium Connector: Extracts data from Postgres and loads it into Kafka, capturing changes with precision.
- S3 Sink Connector: Extracts data from Kafka and efficiently loads it into the designated S3 bucket, paving the way for data warehousing.
- Kafka Cluster: The heart of our data pipeline, Kafka Cluster ensures that change data is readily available for downstream consumption, fueling real-time analytics and insights. 💡🌐
- Data Storage with Minio: We utilize Minio, an S3 OS alternative, as our cloud storage system to hold the data generated by Debezium. Minio offers a scalable and cost-effective solution for storing data in the cloud. ☁️🗄️
- Data Warehousing with duckDB: The final step involves leveraging duckDB to read the data stored in S3. duckDB empowers us to create an SCD2 dataset, allowing us to track historical changes and unlock deeper insights. 🦆📊
Understanding the Change Capture Data Point
Each change captured by Debezium is represented in a JSON format, providing valuable information for downstream processing. Let's explore the key components of this data point:
- schema: Describes the data types of all fields in the payload section.
- payload: Contains the change data and associated metadata.
- before: The data before the change. Null if it's a create operation.
- after: The data after the change. Null if it's a delete operation.
- source: Information about the source system (Postgres in our case).
- ts_ms: The Unix time when the transaction was committed to the DB.
- lsn: Log sequence number, crucial for maintaining transaction order and correctness.
- op: Represents the type of transaction (create, update, delete, or part of a snapshot pull).
- ts_ms: The Unix time when the transaction was read by Debezium.
Conclusion
Extracting change data from Postgres and loading it into S3 paves the way for real-time analytics and data-driven decision-making. The data pipeline, powered by Debezium and duckDB, empowers organizations to harness the full potential of real-time insights. By capturing every change and making it available for downstream consumption, businesses can gain a competitive edge in today's fast-paced world. 🌟🚀
If you're intrigued by the possibilities of real-time analytics and need guidance on implementing Change Data Capture in your data pipeline, reach out to us at hi@itcrats.com. Let's unlock the true potential of your data-driven journey together! 🤝📧
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