Stream Processing Pocket Reference: Real-Time Any-Scale Data Processing
暫譯: 流處理口袋參考：即時任意規模數據處理

Tyler Akidau is principal software engineer at Snowflake. Previously senior staff software engineer at Google, he was the technical lead for the Data Processing Languages & Systems group, responsible for Google's Apache Beam efforts, Google Cloud Dataflow, and internal data processing tools like Google Flume, MapReduce, and MillWheel. His also a founding member of the Apache Beam PMC. Though deeply passionate and vocal about the capabilities and importance of stream processing, he is also a firm believer in batch and streaming as two sides of the same coin, with the real endgame for data processing systems the seamless merging between the two. He is the author of the 2015 Dataflow Model paper and the Streaming 101 and Streaming 102 articles on the O'Reilly website. His preferred mode of transportation is by cargo bike, with his two young daughters in tow.

Slava Chernyak is a senior software engineer at Google Seattle. Slava spent over five years working on Google's internal massive-scale streaming data processing systems and has since become involved with designing and building Windmill, Google Cloud Dataflow's next-generation streaming backend, from the ground up. Slava is passionate about making massive-scale stream processing available and useful to a broader audience. When he is not working on streaming systems, Slava is out enjoying the natural beauty of the Pacific Northwest.

Reuven Lax is a senior staff software engineer at Google Seattle, and has spent the past nine years helping to shape Google's data processing and analysis strategy. For much of that time he has focused on Google's low-latency, streaming data processing efforts, first as a long-time member and lead of the MillWheel team, and more recently founding and leading the team responsible for Windmill, the next-generation stream processing engine powering Google Cloud Dataflow. He's very excited to bring Google's data-processing experience to the world at large, and proud to have been a part of publishing both the MillWheel paper in 2013 and the Dataflow Model paper in 2015. When not at work, Reuven enjoys swing dancing, rock climbing, and exploring new parts of the world.

Austin Bennett designs data systems to help move, share, gather insights and develop data products efficiently.

Tyler Akidau 是 Snowflake 的首席軟體工程師。之前他是 Google 的高級員工軟體工程師，擔任數據處理語言與系統小組的技術負責人，負責 Google 的 Apache Beam 工作、Google Cloud Dataflow 以及內部數據處理工具，如 Google Flume、MapReduce 和 MillWheel。他也是 Apache Beam PMC 的創始成員。雖然他對流處理的能力和重要性充滿熱情並且表達明確，但他也堅信批次處理和流處理是同一枚硬幣的兩面，數據處理系統的真正終極目標是兩者之間的無縫融合。他是 2015 年 Dataflow Model 論文的作者，以及 O'Reilly 網站上 Streaming 101 和 Streaming 102 文章的作者。他的主要交通方式是貨運自行車，並帶著他的兩個小女兒。

Slava Chernyak 是 Google Seattle 的高級軟體工程師。Slava 在 Google 的內部大規模流數據處理系統工作了超過五年，並且自此開始參與 Windmill 的設計和建設，這是 Google Cloud Dataflow 的下一代流後端，從零開始開發。Slava 熱衷於讓大規模流處理對更廣泛的受眾可用且有用。當他不在處理流系統時，Slava 喜歡享受太平洋西北地區的自然美景。

Reuven Lax 是 Google Seattle 的高級員工軟體工程師，過去九年來一直幫助塑造 Google 的數據處理和分析策略。在這段時間裡，他專注於 Google 的低延遲流數據處理工作，最初是 MillWheel 團隊的長期成員和負責人，最近則創立並領導負責 Windmill 的團隊，這是驅動 Google Cloud Dataflow 的下一代流處理引擎。他非常興奮能將 Google 的數據處理經驗帶給全世界，並且為能參與 2013 年的 MillWheel 論文和 2015 年的 Dataflow Model 論文的發表而感到自豪。當不在工作時，Reuven 喜歡搖擺舞、攀岩和探索世界的新地方。

Austin Bennett 設計數據系統，以幫助高效地移動、共享、收集見解和開發數據產品。