Module 04 · Career Opportunities and Data Engineering in Action职业机会与数据工程实践

Graded Assignment: Peer Reviewed Assignment评分作业：同行评审作业

There are two types of data repositories – Transactional and Analytical. Transactional systems, also known as Online Transaction Processing (or OLTP) systems, are designed for real-time execution of a large number of database transactions that are taking place simultaneously, typically online. Give two examples of the kind of transactional data for which an OLTP system may be used.
数据存储库分为两种类型——事务型和分析型。事务型系统，也称为在线事务处理（或 OLTP）系统，旨在实时执行大量同时发生的数据库事务，通常是在线进行的。请给出两个 OLTP 系统可能用于的事务型数据示例。

OLTP systems handle high-concurrency, real-time transactions. Common examples include e-commerce platforms processing customer orders (instantly checking inventory, generating orders, and deducting stock) and banks handling ATM withdrawals (immediately verifying accounts, updating balances, and recording transactions).
联机事务处理（OLTP）系统处理高并发、实时事务。常见示例包括处理客户订单的电子商务平台（即时检查库存、生成订单和扣除库存）和处理 ATM 取款的银行（立即验证账户、更新余额和记录交易）。

In contrast, analytical systems (OLAP) focus on aggregating and analyzing large volumes of historical data to support business decisions, such as calculating sales trends for a product category in a quarter. OLTP manages concrete operations, while OLAP enables subsequent data insights.
相比之下，分析系统（OLAP）侧重于聚合和分析大量历史数据以支持业务决策，例如计算某个产品类别在一个季度的销售趋势。OLTP 管理具体操作，而 OLAP 则支持后续的数据洞察。

Rubric 评分标准

Transactional, or OLTP, systems are designed for handling high-frequency transactional data being generated in real-time. Examples of the type of transactions these systems would be used for, include:
事务性系统或 OLTP 系统旨在处理实时生成的高频事务数据。这些系统将用于的事务类型示例包括：

Online banking transactions
网上银行交易
Online financial transaction
在线金融交易
ATM transaction ATM 交易
Airline booking 机票预订
Hotel booking 酒店预订
Movie ticket purchases 电影票购买
eCommerce purchase 电子商务购买
In-store purchases 店内购买

Note: It is OK for your peer to answer this in their own words. Their responses could be outside of this list but should be valid examples of high-frequency transactional data being generated in real-time.
注意：你的同行可以用他们自己的话来回答这个问题。他们的回答可能不在此列表中，但应该是实时生成的高频交易数据的有效示例。

According to the course material, the architecture of a data platform consists of five layers, each layer responsible for a specific set of tasks. List at least 2 layers in a data platform.
根据课程材料，数据平台的架构由五层组成，每层负责一组特定的任务。请列出数据平台中至少 2 个层。

Based on the course material you provided, the data platform architecture includes the following layers. Two examples are:
根据您提供的课程材料，数据平台架构包括以下层。以下是两个示例：

Data Ingestion Layer: Responsible for collecting and bringing raw data from various sources (such as databases, logs, sensors) into the platform.
数据摄取层：负责从各种来源（如数据库、日志、传感器）收集原始数据并将其引入平台。
Data Processing Layer: Responsible for transforming, calculating, and processing the stored data to convert it into a format ready for analysis.
数据处理层：负责对存储的数据进行转换、计算和处理，以将其转换为可供分析的格式。

Rubric 评分标准

According to the course material, the architecture of a data platform consists of five layers, each layer responsible for a specific set of tasks. These five layers are
根据课程材料，数据平台的架构由五层组成，每一层负责一组特定的任务。这五层是

Data Ingestion or Data Collection Layer
数据摄取或数据收集层
Data Storage and Integration Layer
数据存储和集成层
Data Processing Layer
数据处理层
Analysis and User Interface Layer
分析和用户界面层
Data Pipeline Layer
数据管道层

Note: It is OK for the learners to answer this in their own words.
注意：学员可以用自己的话回答。

List two factors that you would consider in order to make a decision about the type of data store that meets your organization’s needs for a particular implementation.
列出你在做决定时会考虑的两个因素关于哪种数据存储能够满足您组织特定实施需求的决策。

When selecting a data store for a specific implementation need, two key factors to consider are:
在为特定的实施需求选择数据存储时，需要考虑两个关键因素：

Data Access Patterns: The primary consideration is how the data will be read and written. Clarify whether frequent, small, real-time read/write operations (such as a user placing an order) or complex batch analytical queries (such as generating monthly sales reports) are required. This directly determines whether to choose a transactional (OLTP) database (e.g., PostgreSQL, MySQL) or an analytical (OLAP) data warehouse (e.g., Snowflake, BigQuery). Additionally, factors such as read/write ratio, latency requirements, and query complexity should be taken into account.
数据访问模式：首要考虑的是数据的读写方式。明确是需要频繁、小规模、实时的读写操作（例如用户下订单），还是复杂的批处理分析查询（例如生成月度销售报告）。这直接决定了是选择事务型（OLTP）数据库（例如 PostgreSQL、MySQL）还是分析型（OLAP）数据仓库（例如 Snowflake、BigQuery）。此外，还应考虑读写比、延迟要求和查询复杂性等因素。
Data Model and Consistency Requirements: Next, analyze the structure of the data and consistency needs. Is the data highly structured with clear relationships (suitable for relational databases), or is it flexible, variable, or schema-less (suitable for NoSQL databases, such as document-based MongoDB or key-value based Redis)? At the same time, the business’s strictness regarding data consistency must be evaluated: is strong consistency required (e.g., banking transactions, suitable for databases with ACID properties), or is eventual consistency acceptable (e.g., social media like counts, suitable for databases following the BASE model)?
数据模型和一致性要求：接下来，分析数据结构和一致性需求。数据是高度结构化且关系明确（适用于关系型数据库），还是灵活、可变或无模式（适用于 NoSQL 数据库，例如基于文档的 MongoDB 或基于键值对的 Redis）？同时，必须评估业务对数据一致性的严格程度：是需要强一致性（例如银行交易，适用于具有 ACID 特性的数据库），还是可以接受最终一致性（例如社交媒体点赞数，适用于遵循 BASE 模型的数据库）？

In short, combining how the data will be used with the structure of the data and its consistency requirements is central to selecting the appropriate data store.
简而言之，将数据的使用方式与数据结构及其一致性要求相结合，是选择合适数据存储的关键。

Rubric 评分标准

According to the course material, these are some of the factors you need to consider while selecting a data store that meets the needs of your organization:
根据课程材料，在选择满足组织需求的数据存储时，您需要考虑以下一些因素：

The type of data you need to store.
您需要存储的数据类型。
The volume of data you need to store.
您需要存储的数据量。
Intended use of data. 数据的预期用途。
Storage considerations. 存储注意事项。
Privacy, Security, and Governance considerations.
隐私、安全和治理方面的考虑。

Note: It is OK for the learners to answer this in their own words.
注意：学员可以用自己的话回答。