Spark Streaming advanced features in NDCG computing practice
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Guest: Wang Fuping
Profile of Wang Fuping:
- He has served as senior engineer of Baidu’s Big Data Department and architect of Yihaodian’s Search and Precision Department. He has been engaged in big data research and development. He has a deep understanding of big data tools and machine learning, rich experience in real-time computing, and in-depth understanding of Storm and Spark Streaming.
- The current focus is on the construction of data analysis platform, aiming to get through data modeling to data analysis. Based on the data model, druid, Kylin and other technologies are used to provide a platform-level data service and create a one-stop solution of “data as a service”.
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FMI live stream
Spark Streaming advanced features in NDCG computing practice
Share introduction:
From Storm to Spark Streaming and then to Flink, streaming computing has made great progress, and Spark Streaming based on the Spark platform has gone its own way. It uses spark batch processing architecture for reference and implements real-time processing framework through batch processing. The advantages are as follows: 1. High throughput 2. It works well with the Spark platform and can directly use frameworks such as Spark SQL and Mlib. This sharing introduces some advanced features and applications of Spark Streaming from the perspective of search evaluation index: NDCG index calculation.
Sharing Outline:
1. The introduction of the spark
2. Search for NDCG indicators
3. Spark Streaming advanced features in NDCG practice
4. Summary and outlook
Live broadcast: Tuesday (March 20) 20:00 — 21:30 PM
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