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Developing custom transformation in the Kafka connect to minimize data redundancy | Siavash Sedghi, United Health Group

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Developing custom transformation in the Kafka connect to minimize data redundancy | Siavash Sedghi, United Health Group

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Compacted topics grow over time and are often utilizing high performance, low latency and relatively expensive storage solutions. Reducing duplicated data plays a critical role in the size of compacted topics. with less data on the topics, the Kafka cluster consumes less disk space which in turn it leads to lower operation cost.
in this use case-driven talk, we are going to demonstrate how our team at UnitedHealth Group leveraged existing transformers to extract data from the message metadata in the topic as well as how we developed our customized transformers to minimize the amount of duplicated data in each message in the topic.

Compacted topics grow over time and are often utilizing high performance, low latency and relatively expensive storage solutions. Reducing duplicated data plays a critical role in the size of compacted topics. with less data on the topics, the Kafka cluster consumes less disk space which in turn it leads to lower operation cost.
in this use case-driven talk, we are going to demonstrate how our team at UnitedHealth Group leveraged existing transformers to extract data from the message metadata in the topic as well as how we developed our customized transformers to minimize the amount of duplicated data in each message in the topic.

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Developing custom transformation in the Kafka connect to minimize data redundancy | Siavash Sedghi, United Health Group

  1. 1. Optum Technology Provider Data Platform Siavash Sedghi Lead Software Engineer September 2021
  2. 2. Developing Custom Transformations in the Kafka Connect to Minimize Data Redundancy Sep 2021 – Kafka Summit Americas
  3. 3. Our Mission To help people live healthier lives and to help make the health system work better for everyone.
  4. 4. Optum by numbers o Optum serves 127 million individual consumers o Optum serves 9 out of 10 U.S. hospitals o Optum serves ~9 out of 10 Fortune 100 employers o Optum serves more than 90,000 physicians, practices and other health care facilities as well as non-profit associations and organizations o Optum serves a network of over 67,000 pharmacies 4
  5. 5. Architecture 5
  6. 6. The Journey Begins Data ingestion from multiple resources (Kafka Connect) Data enrichment (KTable Joins & Streaming API) Aggregation and metrics calculation (Kafka Streaming API) Sinking data to database (Kafka Connect) Near real-time APIs to serve the data 6
  7. 7. Broker Instance Fee Broker Storage Fee Data Transfer Fee Cost of Operating a Kafka Cluster 7
  8. 8. Broker Instance Fee Broker Storage Fee Data Transfer Fee Cost of Operating a Kafka Cluster 8 Reducing Broker Storage Cost by Minimizing Data Redundancy in Topics
  9. 9. Build-in Transformations 9
  10. 10. Example: 10
  11. 11. Example: 11
  12. 12. Example: 12
  13. 13. Example: 13
  14. 14. Example: 14
  15. 15. Implementing a Transformation 15
  16. 16. Implementing a Transformation 16 Add your Jar file to the base docker image
  17. 17. Implementing a Transformation 17 docker logs -f kafka-connect cat /var/log/kafka/connect.log
  18. 18. DEMO
  19. 19. Siavash Sedghi 19
  20. 20. Optum® is a registered trademark of Optum, Inc. in the U.S. and other jurisdictions. All other brand or product names are the property of their respective owners. Because we are continuously improving our products and services, Optum reserves the right to change specifications without prior notice. Optum is an equal opportunity employer. © 2021 Optum, Inc. All rights reserved.

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