Implementing a Robust Real-Time Data Streaming Solution

Developed a real-time data streaming solution using Apache Kafka, Debezium PostgreSQL Connector, ksqlDB, and AWS S3. This system captures, processes, and stores data changes from PostgreSQL and sends real-time and aggregated data to third-party services using Python. This resulted in improved data accuracy, scalability, and efficiency.

  • Data Processing and Analytics
  • Apache Kafka, Apache Beam, Apache Flink, Debezium PostgreSQL Connector, ksqlDB, PostgreSQL, AWS S3, Python, Parquet format
  • South Africa

The Client

The client is a company seeking to implement a robust real-time data streaming solution to enhance their data processing capabilities. They require a system that can efficiently capture, process, and store data changes from their PostgreSQL database, as well as send real-time and aggregated data to third-party services.

The challenge icon

The Challenge

The client faced several challenges:
  • Efficiently capturing real-time data changes from a PostgreSQL database.
  • Processing and storing this data in a scalable and reliable manner.
  • Ensuring data is archived in a cost-effective and easily accessible format.
  • Sending both real-time and aggregated data to third-party services promptly.
  • Integrating various tools and technologies to create a seamless data pipeline.
The solution icon

The Solution

To address these challenges, the following solution was implemented:
  • Kafka: Used for real-time data streaming to handle high throughput and ensure data durability.
  • Debezium PostgreSQL Connector: Captured data changes from PostgreSQL in real-time and streamed them into Kafka.
  • ksqlDB: Used to process and store the streaming data efficiently.
  • PostgreSQL: Served as the source of data, with changes captured by Debezium.
  • AWS S3: Used for archiving data in Parquet format, ensuring cost-effective storage and easy accessibility.
  • Python: Scripts were developed to send data to third-party services as soon as it was captured and to send aggregated data based on business requirements.

Tools and Technologies

The following tools and technologies were utilized to build the solution:

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Amazon S3

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PostgreSQL

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Python

The result icon

The Result

The implementation of this solution provided several key benefits:
  • Real-Time Data Processing: Enabled the client to capture and process data changes in real-time, improving data accuracy and timeliness.
  • Scalable and Reliable Storage: Ensured data durability and scalability using Kafka and AWS S3.
  • Efficient Data Archiving: Stored data in Parquet format on AWS S3, optimizing storage costs and accessibility.
  • Improved Data Delivery: Automated the delivery of both real-time and aggregated data to third-party services using Python scripts, enhancing the client's data utilization capabilities.
  • Enhanced Integration: Successfully integrated various tools and technologies to create a seamless data pipeline, improving overall system efficiency.

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We are in web development since 1999. In 2004 we developed a PHP MVC framework. Worked on almost all popular web technologies. We implemented many innovative ideas to maximize the client's revenue.

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5 We are in web development since 1999. In 2004 we developed a PHP MVC framework. Worked on almost all popular web technologies. We implemented many innovative ideas to maximize the client's revenue.

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We are in web development since 1999. In 2004 we developed a PHP MVC framework. Worked on almost all popular web technologies. We implemented many innovative ideas to maximize the client's revenue.

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