In this course, you'll learn how to manage big datasets, how to load them into clusters and cloud storage, and how to apply structure to the data so that you can run queries on it using distributed SQL engines like Apache Hive and Apache Impala. You’ll learn how to choose the right data types, storage systems, and file formats based on which tools you’ll use and what performance you need.
本课程是 Modern Big Data Analysis with SQL 专项课程 专项课程的一部分
Managing Big Data in Clusters and Cloud Storage
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课程信息
22,199 次近期查看
您将学到的内容有
Use different tools to browse existing databases and tables in big data systems
Use different tools to explore files in distributed big data filesystems and cloud storage
Create and manage big data databases and tables using Apache Hive and Apache Impala
Describe and choose among different data types and file formats for big data systems
您将获得的技能
Data ManagementDistributed File SystemsCloud StorageBig DataSQL
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第 3 门课程（共 3 门）
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初级
完成时间大约为21 小时
建议：6 hours/week
英语（English）
字幕：英语（English）
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 Data in Clusters and Cloud Storage
完成时间为 3 小时
7 个视频 （总计 56 分钟）, 3 个阅读材料, 1 个测验
7 个视频
Browsing Tables with Hue7分钟
Browsing Tables with SQL Utility Statements6分钟
Browsing HDFS with the Hue File Browser13分钟
Browsing HDFS from the Command Line9分钟
Understanding S3 and Other Cloud Storage Platforms6分钟
Browsing S3 Buckets from the Command Line8分钟
3 个阅读材料
Review and Preparation30分钟
Instructions for Downloading and Installing the Exercise Environment30分钟
Troubleshooting the VM5分钟
1 个练习
Week 1 Graded Quiz30分钟
完成时间为 5 小时
Defining Databases, Tables, and Columns
完成时间为 5 小时
7 个视频 （总计 33 分钟）, 12 个阅读材料, 2 个测验
7 个视频
Introduction to the CREATE TABLE Statement5分钟
Using Different Schemas on the Same Data12分钟
Specifying TBLPROPERTIES2分钟
Examining, Modifying, and Removing Tables1分钟
Hive and Impala Interoperability2分钟
Impala Metadata Refresh3分钟
12 个阅读材料
Creating Databases and Tables with Hue30分钟
Creating Databases and Tables with SQL15分钟
Permissions to Create Databases and Tables5分钟
The ROW FORMAT Clause25分钟
The STORED AS Clause15分钟
The LOCATION Clause20分钟
CREATE TABLE Shortcuts10分钟
Using Hive SerDes15分钟
Working with Unstructured and Semi-Structured Data15分钟
Examining Table Structure10分钟
Dropping Databases and Tables5分钟
Modifying Existing Tables35分钟
2 个练习
Week 2 Practice Quiz20分钟
Week 2 Graded Quiz30分钟
完成时间为 3 小时
Data Types and File Types
完成时间为 3 小时
5 个视频 （总计 14 分钟）, 12 个阅读材料, 2 个测验
5 个视频
Overview of Data Types1分钟
Choosing the Right Data Types4分钟
Overview of File Types3分钟
Choosing the Right File Types3分钟
12 个阅读材料
Integer Data Types5分钟
Decimal Data Types10分钟
Character String Data Types10分钟
Other Data Types5分钟
Examining Data Types10分钟
Out-of-Range Values5分钟
Text Files5分钟
Avro Files5分钟
Parquet Files5分钟
ORC Files5分钟
Other File Types5分钟
Creating Tables with Avro and Parquet Files20分钟
2 个练习
Week 3 Practice Quiz20分钟
Week 3 Graded Quiz30分钟
完成时间为 5 小时
Managing Datasets in Clusters and Cloud Storage
完成时间为 5 小时
8 个视频 （总计 48 分钟）, 13 个阅读材料, 3 个测验
8 个视频
Refresh Impala's Metadata Cache after Loading Data2分钟
Loading Files into HDFS with Hue's Table Browser10分钟
Loading Files into HDFS with Hue's File Browser6分钟
Loading Files into HDFS from the Command Line8分钟
Loading Files into S3 from the Command Line10分钟
Using Hive and Impala to Load Data into Tables3分钟
Conclusion2分钟
13 个阅读材料
More about HDFS Shell Commands10分钟
Chaining and Scripting with HDFS Commands5分钟
HDFS Permissions5分钟
Other Ways to Load Files into S35分钟
S3 Permissions10分钟
Missing Values15分钟
Character Sets5分钟
Using Sqoop to Import Data15分钟
More Sqoop Import Options5分钟
Using Sqoop to Export Data5分钟
SQL LOAD DATA Statements10分钟
SQL INSERT Statements10分钟
SQL INSERT ... SELECT and CTAS Statements15分钟
2 个练习
Week 4 Practice Quiz20分钟
Week 4 Graded Quiz30分钟
4.7
15 条评论来自Managing Big Data in Clusters and Cloud Storage的热门评论
This is Very good course for a beginners, it gives you lots of exercises to practice in vm and course material is Really really good but only thing is you have to read a lot ,
It would have been nice if videos would have been present instead of reading. Also a more deep diving would have been done in concepts like bucketing and indexing.
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.
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.
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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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