Data Streaming Solution for Publishing Company

A prominent educational content publisher encountered difficulties in providing real-time content updates, tailored experiences, and actionable insights to sales, publishers, products, and returns data for stakeholders. To overcome these obstacles, they collaborated with Intersoft to implement a robust data streaming solution.

Intersoft thoroughly reviewed the business use cases observed several critical issues:

• Real-time Data Processing: The existing system failed to provide real-time content updates and analytics, leading to outdated information and delayed insights.
• Scalability: The system needed to efficiently manage the increasing volume of content updates and user interactions as the user base expanded.
• Integration: The solution needed to seamlessly integrate with diverse data sources and downstream applications for analytics and content delivery.
• Reliability: Ensuring continuous data processing with high availability and fault tolerance was crucial for uninterrupted access to educational materials.

Intersoft implemented a data streaming solution using Apache Kafka, tailored to address specific needs.

• Infrastructure:
  • Setup Kafka on multiple brokers to ensure load balancing and Zookeeper on three nodes to ensure high availability and fault tolerance.
  • Configured Kafka topics for different data streams, such as content updates, user interactions, and analytics events.
  • Zookeeper to manage Kafka brokers and maintain configuration information for consistent state maintenance across all nodes.
• Data Ingestion:
  • Producers: Intersoft developed Kafka Connect producers to ingest data from different Databases/Files or API sources. Producers written in Python to get data from these external sources and convert into AVRO format file which are consumed by Kafka Connector
  • Kafka Karapace schema registry is used to store the schema of each file.
• Data Processing:
  • Stream Processing: Intersoft developed a dedup logic in KSQL to remove the duplicates records from the stream. There are multiple legacy sources which have lot of duplicate records and client wants to remove the duplicate data before insertion.
  • Implemented algorithms to provide real-time analytics on content usage, user engagement, and learning outcomes.
• Data Consumption:
  • Consumers: Intersoft developed Kafka Connect consumers to deliver processed data to multiple downstream storage like MongoDB and MYSQL data lake house.
  • Created consumers to update real-time dashboards for educators and administrators, providing insights into content usage.
  • Configured consumers to feed data into machine learning models for personalized content recommendations and adaptive learning pathways.
• Monitoring:
  • Intersoft implemented monitoring tools to ensure the health and performance of the Kafka cluster.
  • Deployed Prometheus and Grafana to monitor Kafka metrics, such as message throughput, latency, and broker health.
  • Used Kafka Manager to manage Kafka brokers, topics, and partitions.
• Security:
  • Intersoft implemented security measures to protect data in transit and at rest.
  • Configured SSL/TLS for encrypted communication between Kafka brokers, producers, and consumers.
  • Implemented SASL for authentication and authorization of clients accessing the Kafka cluster.

By partnering with Intersoft and implementing a data streaming solution using Apache Kafka, customer transformed its digital platform. The solution provided a scalable, reliable, and secure platform for real-time content updates, personalized learning experiences, and actionable insights, ultimately enhancing the educational experience for all business stake holders. This case study highlights Intersoft’s expertise in leveraging Kafka for data streaming to drive significant improvements in the delivery and management of educational content.

• Real-time Content Updates: Customer achieved real-time content updates, ensuring that educators and students always had access to the latest educational materials.
• Enhanced User Engagement: Real-time data processing enabled personalized learning experiences, increasing student engagement and improving learning outcomes.
• Scalability: The Kafka-based solution scaled efficiently to handle the increasing volume of content updates and user interactions without performance degradation.
• Operational Efficiency: Real-time analytics and reporting improved decision-making for content creation and curation, optimizing the overall educational material delivery process.