Tech Lead of RAT
Dan is a data-intensive computing enthusiast who loves concurrency and parallelism in things he builds and how the team build pipelines. He started his career on a telecommunications company and got his first experience on dealing with large scale of data. Back then he was a software engineer responsible for developing mediation and OLTP systems. Then later on, lead the transformation of the organizations's data pipeline and drove data analytics team to utilize big data tools and technologies. He challenged himself further and joined Rakuten as data engineer. His first critical assignment was to help on launching the new pipeline of RAT which uses Apache Spark, and operationalize Druid to serve as engine for low-latency queries. He is currently the tech lead of RAT, helping it grow and build valuable capabilities and features for Rakuten data users.
DATA ENGINEERING AT SCALE @ RAKUTEN 14:20 PM - 14:40 PM
Rakuten services generate a lot of data. Rakuten Ichiba super-sales have generated logging data as high as 3.8 billion records per day, 120,000 queries per second. Internally, we have developed frameworks and tools to collect that data and make it available to a variety of internal capabilities that benefit our users and customers. In this talk we will share will share our data collection infrastructure and talk about applications that make use of the data we have available.
Based on the user attributes and actions on the Rakuten eco-system, we have built a system that allows displaying users the benefits they will get upon their interactions with other diverse services . We will cover how we can retrieve data as well as how we can process data in real-time to be accurate, especially focusing on the functional and technical challenges this use case presents.
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