拓竹科技大数据开发工程师

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动画图解、一键运行的数据结构与算法教程

In this course you will learn about algorithms and data structures, two of the fundamental topics in computer science.

Learn about data structures in this comprehensive course. We will be implementing these data structures in C or C++.

Data Structures and Algorithms full course tutorial java

Step by step guide to learn Data Structures and Algorithms in 2025

一本通俗易懂的数据结构与算法入门书，引导读者手脑并用地学习，强烈推荐算法初学者阅读。

A visual guide to the most important patterns and approaches for the coding interview.

大数据处理的主要流程包括数据收集、数据存储、数据处理、数据应用等主要环节。

With all this talk of Big Data, we got Rebecca Tickle to explain just what makes data into Big Data.

In this Apache Kafka fundamentals course, we introduce you to the basic Apache Kafka elements and APIs, as well as the broader Kafka ecosystem.

Apache Flink 是一个分布式计算框架，它能够对有界和无界的数据流进行高效的处理。

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.

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.

Learn about Elasticsearch with this comprehensive course designed for beginners, featuring both theoretical concepts and hands-on applications using Python (though applicable to any programming language). The course is structured in two parts: first covering essential Elasticsearch fundamentals including index management, document storage, text analysis, pipeline creation, search functionality, and advanced features like semantic search and embeddings; followed by a practical section where you'll build a real-world website using Elasticsearch as a search engine, working with the Astronomy Picture of the Day (APOD) dataset to implement features such as data cleaning pipelines, tokenization, pagination, and aggregations.