In this course, you'll get an in-depth look at the SQL SELECT statement and its main clauses. The course focuses on big data SQL engines Apache Hive and Apache Impala, but most of the information is applicable to SQL with traditional RDBMs as well; the instructor explicitly addresses differences for MySQL and PostgreSQL.
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课程信息
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第 2 门课程（共 3 门）
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初级
完成时间大约为18 小时
英语（English）
字幕：英语（English）
您将学到的内容有
Understand the basics of SELECT statements
Understand how and why to filter results
Explore grouping and aggregation to answer analytic questions
Work with sorting and limiting results
您将获得的技能
Apache HiveApache ImpalaData AnalysisBig DataSQL
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 3 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
初级
完成时间大约为18 小时
英语（English）
字幕：英语（English）
提供方
Cloudera
At Cloudera, we believe that data can make what is impossible today, possible tomorrow. We empower people to transform complex data into clear and actionable insights. Cloudera delivers an enterprise data cloud for any data, anywhere, from the Edge to AI. Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world’s largest enterprises.
教学大纲 - 您将从这门课程中学到什么
完成时间为 3 小时
Orientation to SQL on Big Data
完成时间为 3 小时
9 个视频 （总计 47 分钟）, 5 个阅读材料, 2 个测验
9 个视频
Review and Preparation4分钟
Using the Hue Query Editors7分钟
Running SQL Utility Statements6分钟
Running SQL SELECT Statements5分钟
Understanding Different SQL Interfaces4分钟
Overview of Beeline and Impala Shell2分钟
Using Beeline8分钟
Using Impala Shell3分钟
5 个阅读材料
Instructions for Downloading and Installing the Exercise Environment30分钟
Troubleshooting the VM5分钟
(Optional) What about Spark SQL?10分钟
Expectations for Learners10分钟
(Optional) Using Other SQL Engines10分钟
2 个练习
Week 1 Core Quiz30分钟
Week 1 Honors Quiz5分钟
完成时间为 3 小时
SQL SELECT Essentials
完成时间为 3 小时
16 个视频 （总计 83 分钟）, 4 个阅读材料, 2 个测验
16 个视频
Introduction2分钟
SQL SELECT Building Blocks2分钟
Introduction to the SELECT List7分钟
Expressions and Operators7分钟
Data Types6分钟
Column Aliases5分钟
Built-In Functions7分钟
Data Type Conversion5分钟
The DISTINCT Keyword5分钟
Introduction to the FROM Clause3分钟
Identifiers7分钟
Formatting SELECT Statements4分钟
Using Beeline in Non-Interactive Mode5分钟
Using Impala Shell in Non-Interactive Mode4分钟
Formatting the Output of Beeline and Impala Shell4分钟
Saving Hive and Impala Query Results to a File5分钟
4 个阅读材料
Order of Operations5分钟
Division and Modulo Operators15分钟
Common String Functions15分钟
Case (In)Sensitivity in SQL10分钟
2 个练习
Week 2 Core Quiz30分钟
Week 2 Honors Quiz5分钟
完成时间为 3 小时
Filtering Data
完成时间为 3 小时
14 个视频 （总计 85 分钟）, 6 个阅读材料, 2 个测验
14 个视频
Introduction1分钟
About the Datasets4分钟
Introduction to the WHERE Clause2分钟
Using Expressions in the WHERE Clause9分钟
Comparison Operators9分钟
Data Types and Precision4分钟
Logical Operators7分钟
Other Relational Operators4分钟
Understanding Missing Values8分钟
Handling Missing Values6分钟
Conditional Functions9分钟
Using Variables with Beeline and Impala Shell7分钟
Calling Beeline and Impala Shell from Scripts6分钟
Querying Hive and Impala in Scripts and Applications2分钟
6 个阅读材料
Data Reference5分钟
(Optional) Unicode Characters10分钟
Working with Literal Strings15分钟
Missing Values with Logical Operators10分钟
Missing Values in String Columns5分钟
(Optional Exercise) Change VM Desktop Color30分钟
2 个练习
Week 3 Core Quiz30分钟
Week 3 Honors Quiz5分钟
完成时间为 3 小时
Grouping and Aggregating Data
完成时间为 3 小时
15 个视频 （总计 82 分钟）, 6 个阅读材料, 2 个测验
15 个视频
Introduction1分钟
Introduction to Aggregation2分钟
Common Aggregate Functions2分钟
Using Aggregate Functions in the SELECT Statement8分钟
Introduction to the GROUP BY Clause6分钟
Choosing an Aggregate Function and Grouping Column4分钟
Grouping Expressions6分钟
Grouping and Aggregation, Together and Separately5分钟
NULL Values in Grouping and Aggregation4分钟
The COUNT Function7分钟
Tips for Applying Grouping and Aggregation7分钟
Filtering on Aggregates2分钟
The HAVING Clause8分钟
Understanding Hive and Impala Version Differences10分钟
Understanding Hue Version Differences2分钟
6 个阅读材料
COUNT(*) and SUM(1)5分钟
Interpreting Aggregates: Populations and Samples10分钟
The least and greatest Functions5分钟
Why Aggregate Expressions Ignore NULL Values5分钟
(Optional) Shortcuts for Grouping10分钟
How Grouping and Aggregation Can Mislead10分钟
2 个练习
Week 4 Core Quiz30分钟
Week 4 Honors Quiz10分钟
审阅
来自ANALYZING BIG DATA WITH SQL的热门评论
由 HD 提供Jun 12, 2020
It is no common for me to put 5 star score to courses, but this courser was literally comprehensive. Thanks to all staff and instructor.
由 RJ 提供Oct 25, 2019
Good course to understand the need of SQL in data analysis/big data using good examples and real life data and problem sets.
由 JC 提供May 4, 2020
This course helped me a great deal for a job interview as well as for all future querying with Impala and Hive
由 TP 提供Oct 8, 2019
I have used many platforms to get started with SQL but this has been the best by far. Thank you Cloudera.
关于 Modern Big Data Analysis with SQL 专项课程
This Specialization teaches the essential skills for working with large-scale data using SQL.
Maybe you are new to SQL and you want to learn the basics. Or maybe you already have some experience using SQL to query smaller-scale data with relational databases. Either way, if you are interested in gaining the skills necessary to query big data with modern distributed SQL engines, this Specialization is for you.
Most courses that teach SQL focus on traditional relational databases, but today, more and more of the data that’s being generated is too big to be stored there, and it’s growing too quickly to be efficiently stored in commercial data warehouses. Instead, it’s increasingly stored in distributed clusters and cloud storage. These data stores are cost-efficient and infinitely scalable.
To query these huge datasets in clusters and cloud storage, you need a newer breed of SQL engine: distributed query engines, like Hive, Impala, Presto, and Drill. These are open source SQL engines capable of querying enormous datasets. This Specialization focuses on Hive and Impala, the most widely deployed of these query engines.
This Specialization is designed to provide excellent preparation for the Cloudera Certified Associate (CCA) Data Analyst certification exam. You can earn this certification credential by taking a hands-on practical exam using the same SQL engines that this Specialization teaches—Hive and Impala.
常见问题
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
What are the hardware and software requirements for the exercise environment?
• Windows, macOS, or Linux operating system (iPads and Android tablets will not work) • 64-bit operating system (32-bit operating systems will not work) • 8 GB RAM or more • 25GB free disk space or more • Intel VT-x or AMD-V virtualization support enabled (on Mac computers with Intel processors, this is always enabled; on Windows and Linux computers, you might need to enable it in the BIOS) • For Windows XP computers only: You must have an unzip utility such as 7-Zip or WinZip installed (Windows XP’s built-in unzip utility will not work)
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