Miao Wang

Engineering Manager, Adobe, Inc.

Miao is an Engineering Manager at Adobe, where he works with a great team on platform engineering with Spark and other open source technologies. He used to be an active Spark contributor before changing to his manager role. His interests span on high speed networks, data center infrastructure, data processing and machine learning. Prior to joining Adobe, he worked at A10 Networks, IBM and Alibaba with various engineering roles. Miao holds a Ph.D in Computer Science from University of Nebraska. Lincoln with a focus on Peer-to-Peer (P2P) Streaming.

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Taming the Search: A Practical Way of Enforcing GDPR and CCPA in Very Large Datasets with Apache SparkSummit 2020

In today's data-driven economy, companies increasingly collect more user data as their valuable assets. By contrast, users have rightfully raised the concern of how to protect their data privacy. In response, there are data privacy laws to protect user's privacy, among which, General Data Protection Regulation (GDPR) by European Union (EU) and California Consumer Privacy Act (CCPA) are two representative laws regulating business conduct in corresponding regions. Common requirements are to access or delete all records in all ever-collected data given a specific user's search key(s) in a timely manner. The size of collected data and the volume of requests for search make enforcing GDPR and CCPA highly inefficient if not resourcefully infeasible.

In this talk, we demonstrate our work for enforcing GDPR and CCPA in Adobe's Experience Platform (AEP) by efficiently solve the search problem above. Specifically, we build Bloom Filters while saving data in Data Lake with minimal resource and maintenance overhead, which reduces nearly 10X searching time for a single search request. Furthermore, we build orchestrated microservices for splitting and scheduling extra-large search jobs into multiple smaller jobs with a balance between resources consumption and processing time. Finally, we walk through a few lessons learnt from our work of handling datasets with a larger number of files and partitions with Spark.