Contributor: Alexandra Raibuh (Java Technical Solutions)
Assessment: By combining CDC with Amazon MSK, organizations can create a robust and reliable data processing pipeline that meets real-time data needs.
CDC delivered with Managed Serverless Kafka makes Real-Time streaming resilient
CDC (Change Data Capture) is an integration pattern used very commonly to handle real time data synchronization between platforms.
Unlike traditional ETL solutions that rely on outdated batch data processing, Kafka's streaming capabilities ensure real-time or near-real-time data distribution, making it perfect for time-sensitive decision-making. Kafka solutions excel at detecting anomalies and fraud in real-time, enabling organizations to respond to incidents swiftly.
This feature is especially valuable in industries where rapid action is critical for maintaining security and preventing financial losses. Other data streaming use cases include anomaly and fraud detection, real-time customer experience customization, IoT analytics enhancement, targeted marketing campaigns, personalized real-time interactions, and healthcare and emergency services support.
Amazon MSK (Managed Streaming for Apache Kafka) stands out as a top choice for real-time data integration services for giants like Instagram, Twitter, or Tinder, where a constant data stream is crucial. It plays a key role in industries such as telecommunications and retail, where up-to-the-minute data updates are essential for making informed decisions.
How to get personalized experiences in real-time using Cloud Streaming Technology
Based on our experience with traditional platforms, we understand the importance of having data readily available in digital channels we worked with, such as Internet banking or retail banking. This is where streaming technology comes into play, allowing for real-time, personalized experiences for users. Modern streaming architectures offer a solution that can be implemented in any Cloud system, including hybrid cloud environments. These architectures consist of 5 logical layers, each with purpose-built components:
1. Source: Devices or applications that generate real-time data at high velocity.
2. Stream ingestion: Collects and ingests data from thousands of sources in real time.
3. Stream storage: Stores data in the order received for a specified period, allowing for replay.
4. Stream processing: Enables real-time analytics or streaming ETL by processing records as they are produced.
5. Destination: Data can be stored in a data lake, data warehouse, database, or other event-driven applications like OpenSearch.
Getting the most out of your messaging system: tips for keeping communication smooth and reliable
Ensuring the reliability and scalability of messaging systems is crucial for seamless communication. The process involves organizing messages, making them persistent, integrating them into a system, storing them, and then processing them. Some clients use these messages for personalized experiences or risk analysis. Different streaming architecture patterns are also used, such as Clickstream for analytics, Change Data Capture with Kafka, real-time Fraud Detection Systems, and Microservices Architecture for effective processing. In this sense, Kafka is commonly used in architectures resembling digital channels and is recommended as an alternative to SGs/SMS for its cloud-native capabilities. While SGs/SMS may be more cloud-native, Kafka offers more versatility and is not limited to a specific platform.
At IT Smart Systems we leverage CDC with AWS MSK for seamless data integration
Recently, we presented our findings in an internal Demo here at IT Smart Systems, showcasing the benefits of implementing Change Data Capture (CDC) with AWS Managed Streaming for Apache Kafka (MSK). For this demo, we created an Amazon MSK cluster and configured and deployed a Debezium source connector in MSK Connect to capture real-time data changes in a MySQL database running on Amazon RDS. An EC2 client instance was also set up to connect to the MSK cluster and interact with the Kafka topics. Authentication and authorization from the clients to the MSK cluster were enabled through custom AWS Identity and Access Management (IAM) roles and policies.
The out of the box Kafka ready made available connectors make the implementation of real-time streaming flows really fast and will less code / no code requirements.
Leveraging this technology, we found that organizations can improve data integration processes, enhance data quality, and enable faster decision-making based on real-time data updates. We are excited to continue exploring the possibilities of CDC with AWS MSK and its potential impact on data management strategies.
For more details on these technologies, contact us.
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