Mastering Real-Time Data Pipelines: Implementing CDC with Strimzi Kafka Connect from PostgreSQL to MinIO — Part-1
In this article series, I will take you on an exciting journey into the world of Change Data Capture (CDC) using Kafka within a Kubernetes environment. I’ll dive into the fundamentals of CDC, uncover its compelling benefits, and walk through a practical implementation guide. Along the way, we’ll explore real-world scenarios where Kafka-powered CDC can revolutionize your data architecture. Get ready to transform your approach to real-time data streaming and integration!
The article series is structured into three parts to provide both theoretical insights and practical implementation details.
Part 1: Theoretical Foundation and General Architecture : In this section, we will explore the foundational concepts of Change Data Capture (CDC) and its crucial role in modern data architectures. We will discuss the benefits of using Kafka, specifically with Strimzi, as a central streaming platform to capture real-time data changes from various sources, such as relational and NoSQL databases, object storages, and other data repositories. This part aims to explain CDC concepts, provide an in-depth look at Kafka Connect cluster architecture, and highlight use-case scenarios for CDC. By understanding these theoretical foundations, you will gain insights into how CDC enables efficient and scalable data pipelines, setting the stage for practical implementations in the subsequent sections.
Part 2: Practical Implementation — Ingesting Data into Kafka from PostgreSQL Next, we’ll delve into practical implementation details. You’ll learn how to set up Strimzi resources such as Kafka Connect clusters specifically configured to ingest data changes from PostgreSQL into Kafka. I’ll guide you through the configuration steps, emphasizing the setup of connectors and the deployment of the entire setup on Kubernetes.
Part 3: Practical Implementation — Sinking Data from Kafka to MinIO in Apache Parquet Format The final part focuses on sinking the data into MinIO as Apache Parquet files, ensuring they are ready for real-time processing and analysis. We’ll leverage schema registry to maintain data schema compatibility and integrity throughout the pipeline. This section will include detailed steps on configuring Kafka connectors for sinking data, handling data formats, and deploying the entire solution on Kubernetes.
By the end of this article, you will have a comprehensive understanding and practical experience in implementing a powerful CDC pipeline with Kafka, demonstrating its efficiency and scalability within a Kubernetes environment. Lets continue with Part-1 subjects.
- Introduction to Change Data Capture (CDC) and its Importance with Kafka
- Use Case Scenarios
- What is Strimzi Kafka ?
- What is Kafka Connect ?
- Kafka Connect Architecture
Introduction to Change Data Capture (CDC) and its Importance with Kafka
— What is Change Data Capture(CDC) ?
Change Data Capture (CDC) is a process that identifies and captures changes made to data in a database, ensuring that these changes can be propagated to other systems in real-time.
— Why CDC with Kafka is a Game-Changer ?
- Real-Time Data Processing: CDC with Kafka allows for the continuous capture and streaming of data changes in real-time, enabling immediate data availability for analytics and decision-making.
- Scalability: Kafka’s distributed architecture ensures high throughput and fault tolerance, making it scalable to handle large volumes of data changes across multiple sources.
- Decoupling Data Producers and Consumers: Kafka acts as an intermediary, decoupling data producers (databases) from data consumers (applications and analytics platforms), facilitating independent scaling and development.
- Seamless Integration: Kafka Connect provides a robust framework for integrating with various data sources and sinks, ensuring seamless data flow between heterogeneous systems.
- Data Consistency and Reliability: By capturing changes at the data source level, CDC ensures data consistency and reliability, maintaining accurate and up-to-date data across distributed systems.
- Event-Driven Architectures: CDC with Kafka supports event-driven architectures, where data changes trigger real-time actions and workflows, enhancing responsiveness and automation.
- Reduced Latency: With CDC and Kafka, the latency between data generation and availability is minimized, supporting near real-time analytics and operational intelligence.
- Historical Data Replay: Kafka’s ability to store and replay data changes allows for historical data analysis and recovery, providing valuable insights and enabling back-testing of scenarios.
- Cost Efficiency: By leveraging Kafka’s efficient data streaming capabilities, organizations can reduce the overhead and complexity associated with traditional batch processing and ETL jobs.
Use Case Scenarios
Using CDC with Kafka in below scenarios provides real-time data integration, improved efficiency, and scalability, ensuring that critical data is always current and accessible across various systems and applications.
1-) Real-Time Inventory Management System: A retail company needs to keep its inventory updated in real-time across multiple warehouses and stores.
— Benefits of CDC with Kafka:
- Real-Time Updates: Ensures inventory data is continuously updated across all locations, preventing stockouts or overstock situations.
- Scalability: Kafka’s ability to handle large volumes of data efficiently supports the high transaction rates typical in retail environments.
- Integration: Seamlessly integrates with various databases (relational and NoSQL) and applications, ensuring data consistency and accuracy across the entire system.
2-) Customer Activity Tracking System: An e-commerce platform wants to track customer activities (such as page views, product clicks, and purchases) in real-time to provide personalized recommendations.
— Benefits of CDC with Kafka:
- Immediate Insights: Captures and processes customer activities in real-time, enabling timely and relevant recommendations.
- Enhanced User Experience: By leveraging real-time data, the platform can provide a personalized shopping experience, increasing customer satisfaction and engagement.
- Flexibility: Kafka’s integration with processing frameworks like Apache Flink allows for complex event processing and analytics.
3-) Financial Transactions Monitoring: A bank wants to monitor and analyze financial transactions in real-time to detect fraud and provide insights.
— Benefits of CDC with Kafka:
- Fraud Detection: Real-time monitoring allows for the immediate detection of suspicious activities, reducing the risk of fraudulent transactions.
- Regulatory Compliance: Ensures that all transaction data is accurately and promptly recorded, helping banks meet regulatory requirements.
- Scalability and Reliability: Kafka’s fault-tolerant architecture ensures continuous data flow and processing, even under high load conditions.
4-) Order Processing System: An online store wants to streamline its order processing by integrating multiple services that need to react to order changes in real-time.
— Benefits of CDC with Kafka:
- Efficiency: Real-time data flow ensures that order-related services (inventory updates, notifications, shipping) are promptly executed, reducing processing time.
- Coordination: Kafka enables different microservices to consume order changes and act independently yet coherently, improving overall system efficiency.
- Consistency: Maintains data consistency across all services, ensuring accurate order tracking and fulfillment.
5-) User Profile Synchronization: A social media platform needs to synchronize user profiles across multiple systems in real-time.
— Benefits of CDC with Kafka:
- Consistency: Ensures that user profile changes are propagated immediately across all systems, maintaining a consistent user experience.
- Integration: Kafka’s versatility allows for seamless data synchronization with various downstream systems like search indexes, recommendation engines, and caching layers.
- Scalability: Can handle the high volume of profile changes typical in social media platforms, ensuring reliable and efficient data synchronization.
What is Strimzi Kafka
Strimzi Kafka is an open-source project that simplifies the deployment, management, and operation of Apache Kafka on Kubernetes. Strimzi provides Kubernetes-native resources and tools for creating and managing Kafka clusters, making it easier to run Kafka in a cloud-native environment. Key features include automated deployment, scaling, rolling updates, and monitoring, allowing users to leverage the powerful messaging and streaming capabilities of Kafka with the scalability and resilience of Kubernetes. By using Strimzi, organizations can efficiently integrate Kafka into their existing Kubernetes infrastructure, enhancing their data streaming and processing capabilities.
What is Kafka Connect and Connect Cluster Architecture
Kafka Connect is an integration toolkit for streaming data between Kafka brokers and external systems, such as databases. It uses a plugin architecture to implement connectors, which allow connections to other systems and provide configuration options to manipulate data. Plugins include connectors, data converters, and transforms. A connector operates with a specific type of external system and defines a schema for its configuration. You supply the configuration to Kafka Connect to create a connector instance, which then defines tasks for moving data between systems.
Strimzi operates Kafka Connect in distributed mode, managing data streaming tasks across multiple worker pods. Each connector runs on a single worker and its tasks are distributed across the worker group, enabling scalable pipelines. Each worker runs as a separate pod, enhancing fault tolerance. If tasks outnumber workers, multiple tasks are assigned to each worker. If a worker fails, its tasks are automatically reassigned to active workers, ensuring continuous operation.
Workers convert data from one format to another suitable for the source or target system. Depending on the connector configuration, workers might also apply transforms to adjust messages, such as filtering data, before conversion. Kafka Connect has built-in transforms, but additional transformations can be provided by plugins if necessary.
Now lets examine the source Kafka Connect cluster architecture.
1-) Plugin Implementation:
- Role: A plugin provides the necessary implementation artifacts for the source connector.
- Detail: The plugin architecture of Kafka Connect allows for flexibility and extensibility. Plugins include not only connectors but also converters and transformations. These components enable Kafka Connect to interact with various external systems and manipulate the data as needed.
2-) Worker and Connector Initialization:
- Role: A single worker initiates the source connector instance.
- Detail: Workers are the backbone of the Kafka Connect cluster. Each worker pod runs independently, ensuring the distributed nature of the setup. When a connector is instantiated, one worker takes responsibility for initializing it, thus ensuring the connector starts correctly and is monitored throughout its lifecycle.
3-) Task Creation and Distribution:
- Role: The source connector creates tasks to stream data.
- Detail: After initialization, the connector defines a set of tasks. These tasks are units of work that handle the actual data streaming. Tasks are distributed across the available workers to balance the load and maximize throughput. The number of tasks is typically defined based on the workload and the capacity of the cluster.
4-) Parallel Task Execution:
- Role: Tasks run in parallel to poll the external data system and return records.
- Detail: Each task operates independently, polling data from the external source system. This parallel execution ensures that data is ingested efficiently and in real-time. Tasks continuously poll the external system, ensuring that new data changes are promptly captured and processed.
5-) Data Transformation:
- Role: Transforms adjust the records, such as filtering or relabeling them.
- Detail: Before data is converted and sent to Kafka, it may need to be transformed. Transforms can be used to filter unwanted data, relabel fields, or apply other modifications. Kafka Connect supports built-in transforms, but custom transforms can also be added via plugins, providing flexibility in how data is processed.
6-) Data Conversion:
- Role: Converters put the records into a format suitable for Kafka.
- Detail: Converters are responsible for serializing and deserializing data between Kafka and the external system. They ensure that data is in a compatible format for Kafka topics. Common converters include JSONConverter, AvroConverter, and StringConverter. These converters handle the translation of data formats, making it seamless for Kafka to store and manage the records.
7-) Connector Management:
- Role: The source connector is managed using KafkaConnectors or the Kafka Connect API.
- Detail: Management of Kafka Connectors can be done using Kubernetes custom resources provided by Strimzi, known as KafkaConnectors, or through the Kafka Connect REST API. These management interfaces allow administrators to create, configure, and monitor connectors and tasks, providing full control over the data integration processes.
By leveraging these components and processes, Strimzi Kafka Connect enables robust and scalable data streaming pipelines, ensuring real-time data integration and processing across diverse systems.
If everything is okay until now lets move forward with Sink Kafka Connect cluster architecture :)
Actually most of the components do same thing like in the source connect cluster, the difference in here tasks poll data from relevant Kafka Topic and after the Converting and Transforming process sink data to external data system.
With the theoretical groundwork laid, we’re now poised to dive into the practical side of things. In the upcoming sections, we’ll roll up our sleeves and get hands-on with setting up Kafka Connect clusters. First, we’ll create a source Kafka Connect cluster to seamlessly ingest data from PostgreSQL. Then, we’ll set up a sink Kafka Connect cluster to read data from Kafka topics and store it in MinIO as Apache Parquet format. Excited to see this in action? Keep reading to embark on this implementation journey :)
