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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-engineering-interview-questions-practice-test-series/
课程评论：没有评论
课程名称：数据工程面试问题实战测试系列 课程概述：本课程旨在提供对数据工程原理和技术的深入理解。涵盖六个关键部分，确保以结构化方式掌握在实际数据工程环境中使用的关键概念、工具和技术。 1. 数据工程基础：学习数据工程的基本方面，包括数据存储类型（结构化、半结构化和非结构化）、数据处理方法（批处理与流处理）以及基本的数据库概念。 2. 数据建模与仓储：理解数据库规范化、实体-关系建模、索引和分区。探索数据仓储概念，包括星型和雪花模式、联机分析处理（OLAP）与联机事务处理（OLTP），以及数据集市在企业分析中的作用。 3. ETL与数据管道：获取关于提取、转换和加载（ETL）过程、数据摄取技术以及工作流编排工具（如Apache Airflow和Prefect）的实用见解。学习如何高效处理实时数据移动和转换。 4. 大数据技术与框架：深入了解分布式计算和大数据处理，使用Hadoop、Spark和Kafka。理解这些工具如何帮助在可扩展的环境中处理、流式传输和管理大型数据集。 5. 云数据工程：探索基于云的数据工程解决方案，包括AWS Redshift、Google BigQuery和Azure Synapse。理解云存储、数据湖的概念，以及管理服务在数据工程工作流中的使用。 6. 性能优化与最佳实践：学习优化数据管道、提高查询性能和有效管理成本的策略。理解现代数据工程中的数据治理、安全和合规最佳实践。
This course is designed to provide an in-depth understanding of data engineering principles and technologies. Covering six critical sections, it ensures a structured approach to mastering key concepts, tools, and techniques used in real-world data engineering environments.1. Foundations of Data EngineeringLearn the fundamental aspects of data engineering, including data storage types (structured, semi-structured, and unstructured), data processing methodologies (batch vs. stream processing), and fundamental database concepts.2. Data Modeling and WarehousingUnderstand database normalization, entity-relationship modeling, indexing, and partitioning. Explore data warehousing concepts, including star and snowflake schemas, OLAP vs. OLTP, and the role of data marts in enterprise analytics.3. ETL and Data PipelinesGain practical insights into Extract, Transform, Load (ETL) processes, data ingestion techniques, and workflow orchestration tools like Apache Airflow and Prefect. Learn how to handle real-time data movement and transformations efficiently.4. Big Data Technologies and FrameworksDive into distributed computing and big data processing using Hadoop, Spark, and Kafka. Understand how these tools help in processing, streaming, and managing large datasets in scalable environments.5. Cloud Data EngineeringExplore cloud-based data engineering solutions, including AWS Redshift, Google BigQuery, and Azure Synapse. Understand cloud storage, data lakes, and the use of managed services for data engineering workflows.6. Performance Optimization and Best PracticesLearn strategies to optimize data pipelines, improve query performance, and manage costs effectively. Understand best practices for data governance, security, and compliance in modern data engineering.
