京东数据开发工程师
社招全职3年以上数据开发岗地点:上海状态:招聘
任职要求
1.计算机相关专业,3年以上数仓开发实施经验,能够根据业务设计数据仓库模型,并对数据模型进行管理,保证数据的产出和质量; 2.精通sql开发,有较丰富的Hive sql性能调优经验,能进行udf开发者优先; 3.有Spark Streaming/Fl…
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工作职责
1.参与京东外卖&秒送PB级数据仓库的建设,为各业务方提供完整、高效的数据支撑; 2.基于简单、易用、高效、可靠等原则建设离线数据仓库,支撑上层数据产品和分析师; 3.构建实时数据仓库,满足实时业务场景; 4.深入参与数据产品建设,为公司内外提供完善的数据解决方案; 5.满足公司各部门日常的数据需求。
包括英文材料
数据仓库+
https://www.youtube.com/watch?v=9GVqKuTVANE
From Zero to Data Warehouse Hero: A Full SQL Project Walkthrough and Real Industry Experience!
https://www.youtube.com/watch?v=k4tK2ttdSDg
SQL+
https://liaoxuefeng.com/books/sql/introduction/index.html
什么是SQL?简单地说,SQL就是访问和处理关系数据库的计算机标准语言。
https://sqlbolt.com/
Learn SQL with simple, interactive exercises.
https://www.youtube.com/watch?v=p3qvj9hO_Bo
In this video we will cover everything you need to know about SQL in only 60 minutes.
Hive+
[英文] Hive Tutorial
https://www.tutorialspoint.com/hive/index.htm
Hive is a data warehouse infrastructure tool to process structured data in Hadoop. It resides on top of Hadoop to summarize Big Data, and makes querying and analyzing easy.
https://www.youtube.com/watch?v=D4HqQ8-Ja9Y
性能调优+
https://goperf.dev/
The Go App Optimization Guide is a series of in-depth, technical articles for developers who want to get more performance out of their Go code without relying on guesswork or cargo cult patterns.
https://web.dev/learn/performance
This course is designed for those new to web performance, a vital aspect of the user experience.
https://www.ibm.com/think/insights/application-performance-optimization
Application performance is not just a simple concern for most organizations; it’s a critical factor in their business’s success.
https://www.oreilly.com/library/view/optimizing-java/9781492039259/
Performance tuning is an experimental science, but that doesn’t mean engineers should resort to guesswork and folklore to get the job done.
Spark+
[英文] Learning Spark Book
https://pages.databricks.com/rs/094-YMS-629/images/LearningSpark2.0.pdf
This new edition has been updated to reflect Apache Spark’s evolution through Spark 2.x and Spark 3.0, including its expanded ecosystem of built-in and external data sources, machine learning, and streaming technologies with which Spark is tightly integrated.
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