滴滴国际事业群IBG-后端研发实习生（8）
实习兼职地点：北京状态：招聘♡ 收藏
工作描述
岗位职责（具体工作内容） 1.负责国际化外卖搜广推数据流转体系建设，基于 MQ、Flink、Spark、Redis、Go 等技术，打通曝光、点击、转化、商家、商品、用户行为、特征、样本、标签等核心数据链路，支撑召回、排序、广告与策略优化闭环。 2.负责国际化外卖实时数据研发，构建面向搜索、推荐、广告、运营等场景的实时数据处理与分发能力，涵盖消息接入、流式 ETL、维表关联、实时特征计算、缓存服务、数据回流及监控治理，提升数据时效性、稳定性与可用性。 3.负责国际化外卖底层数据开发，搭建离线实时一体化数据链路、特征仓库、指标体系、…
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包括英文材料
消息队列+
https://www.youtube.com/watch?v=xErwDaOc-Gs
Flink+
https://nightlies.apache.org/flink/flink-docs-release-2.0/docs/learn-flink/overview/
This training presents an introduction to Apache Flink that includes just enough to get you started writing scalable streaming ETL, analytics, and event-driven applications, while leaving out a lot of (ultimately important) details.
https://www.youtube.com/watch?v=WajYe9iA2Uk&list=PLa7VYi0yPIH2GTo3vRtX8w9tgNTTyYSux
Today’s businesses are increasingly software-defined, and their business processes are being automated. Whether it’s orders and shipments, or downloads and clicks, business events can always be streamed. Flink can be used to manipulate, process, and react to these streaming events as they occur.
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.
Redis+
[英文] Developer Hub
https://redis.io/dev/
Get all the tutorials, learning paths, and more you need to start building—fast.
https://www.runoob.com/redis/redis-tutorial.html
REmote DIctionary Server(Redis) 是一个由 Salvatore Sanfilippo 写的 key-value 存储系统，是跨平台的非关系型数据库。
https://www.youtube.com/watch?v=jgpVdJB2sKQ
In this video I will be covering Redis in depth from how to install it, what commands you can use, all the way to how to use it in a real world project.
Go+
https://www.youtube.com/watch?v=8uiZC0l4Ajw
学习Golang的完整教程！从开始到结束不到一个小时，包括如何在Go中构建API的完整演示。没有多余的内容，只有你需要知道的知识。
ETL+
https://www.ibm.com/think/topics/etl
ETL—meaning extract, transform, load—is a data integration process that combines, cleans and organizes data from multiple sources into a single, consistent data set for storage in a data warehouse, data lake or other target system.
https://www.youtube.com/watch?v=OW5OgsLpDCQ
It explains what ETL is and what it can do for you to improve your data analysis and productivity.
缓存+
https://hackernoon.com/the-system-design-cheat-sheet-cache
The cache is a layer that stores a subset of data, typically the most frequently accessed or essential information, in a location quicker to access than its primary storage location.
https://www.youtube.com/watch?v=bP4BeUjNkXc
Caching strategies, Distributed Caching, Eviction Policies, Write-Through Cache and Least Recently Used (LRU) cache are all important terms when it comes to designing an efficient system with a caching layer.
https://www.youtube.com/watch?v=dGAgxozNWFE
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
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