Learn to build complete data pipelines that transform raw event data into actionable insights using SQL and Pandas. You'll gain the skills to design efficient star schemas, implement Type-2 slowly changing dimensions for historical tracking, and optimize database performance for analytical workloads.
Data Pipelines and SQL for Product Analytics
Data Pipelines and SQL for Product Analytics
包含在 中
您将学到什么
Build scalable data pipelines using SQL and Pandas to transform 10+ million rows of raw event data into structured analytics datasets.
Design and optimize star schemas with Type-2 slowly changing dimensions to track historical changes in product analytics data.
Compare and implement advanced SQL window functions across different dialects like Presto and Spark for cross-platform compatibility.
Evaluate existing data warehouse schemas and propose performance refinements using aggregation techniques and indexing strategies.
您将获得的技能
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 Product Analytics Unlocked: Metrics to Meaningful Insight 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有11个模块
In this module, you will configure automated ETL pipelines using Apache Airflow to seamlessly ingest real-time event streams from sources like Mixpanel into data warehouses such as Snowflake.
涵盖的内容
2个视频1篇阅读材料1个作业1个非评分实验室
In this module, you will implement systematic validation processes to assess mobile event implementations against predefined tracking specifications, identify compliance gaps, and create actionable remediation workflows.
涵盖的内容
1个视频2篇阅读材料2个作业
You will learn to build scalable, maintainable data transformation pipelines through parameterized SQL scripting techniques.
涵盖的内容
3个视频1篇阅读材料2个作业
You will learn systematic performance analysis techniques to identify and resolve database bottlenecks that impact analytical workflows.
涵盖的内容
2个视频2篇阅读材料3个作业
You will learn systematic approaches to transform complex nested JSON structures into pandas DataFrames, enabling reliable data preprocessing for analytics pipelines.
涵盖的内容
3个视频1篇阅读材料1个作业
You will develop systematic approaches to identify, diagnose, and correct timezone-related data quality issues that fragment user sessions and compromise temporal analytics.
涵盖的内容
2个视频1篇阅读材料3个作业1个非评分实验室
You will learn the critical syntax variations between SQL dialects that can make or break analytical queries in enterprise data environments.
涵盖的内容
2个视频2篇阅读材料1个作业
You will learn advanced techniques for transforming raw event streams into structured analytical datasets using both SQL and Pandas aggregation methods.
涵盖的内容
2个视频1篇阅读材料3个作业1个非评分实验室
You will learn the fundamental concepts and practical implementation of Type-2 slowly changing dimensions to preserve complete historical data records in dimensional models.
涵盖的内容
2个视频2篇阅读材料1个作业
You will learn systematic evaluation techniques to assess star schema effectiveness and develop comprehensive refinement strategies that balance query performance, storage efficiency, and analytical capabilities.
涵盖的内容
2个视频2篇阅读材料3个作业
You will build a complete data pipeline system that automates event data ingestion, transforms complex data structures, and creates optimized analytical datasets. This project integrates skills from automated data ingestion, SQL optimization, JSON transformation, time data correction, advanced aggregation techniques, and dimensional modeling to create a production-ready analytics infrastructure.
涵盖的内容
4篇阅读材料1个作业
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