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.
このコースについて
53,771 最近の表示
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における3の3コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
初級レベル
約20時間で修了
英語
字幕：英語
学習内容
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
習得するスキル
Data ManagementDistributed File SystemsCloud StorageBig DataSQL
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における3の3コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
初級レベル
約20時間で修了
英語
字幕：英語
提供：
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.
シラバス - 本コースの学習内容
3時間で修了
Orientation to Data in Clusters and Cloud Storage
3時間で修了
7件のビデオ （合計56分）, 3 readings, 1 quiz
7件のビデオ
Browsing Tables with Hue7 分
Browsing Tables with SQL Utility Statements6 分
Browsing HDFS with the Hue File Browser13 分
Browsing HDFS from the Command Line9 分
Understanding S3 and Other Cloud Storage Platforms6 分
Browsing S3 Buckets from the Command Line8 分
3件の学習用教材
Review and Preparation30 分
Instructions for Downloading and Installing the Exercise Environment30 分
Troubleshooting the VM5 分
1の練習問題
Week 1 Graded Quiz30 分
5時間で修了
Defining Databases, Tables, and Columns
5時間で修了
7件のビデオ （合計33分）, 12 readings, 2 quizzes
7件のビデオ
Introduction to the CREATE TABLE Statement5 分
Using Different Schemas on the Same Data12 分
Specifying TBLPROPERTIES2 分
Examining, Modifying, and Removing Tables1 分
Hive and Impala Interoperability2 分
Impala Metadata Refresh3 分
12件の学習用教材
Creating Databases and Tables with Hue30 分
Creating Databases and Tables with SQL15 分
Permissions to Create Databases and Tables5 分
The ROW FORMAT Clause25 分
The STORED AS Clause15 分
The LOCATION Clause20 分
CREATE TABLE Shortcuts10 分
Using Hive SerDes15 分
Working with Unstructured and Semi-Structured Data15 分
Examining Table Structure10 分
Dropping Databases and Tables5 分
Modifying Existing Tables35 分
2の練習問題
Week 2 Practice Quiz20 分
Week 2 Graded Quiz30 分
3時間で修了
Data Types and File Types
3時間で修了
5件のビデオ （合計14分）, 12 readings, 2 quizzes
5件のビデオ
Overview of Data Types1 分
Choosing the Right Data Types4 分
Overview of File Types3 分
Choosing the Right File Types3 分
12件の学習用教材
Integer Data Types5 分
Decimal Data Types10 分
Character String Data Types10 分
Other Data Types5 分
Examining Data Types10 分
Out-of-Range Values5 分
Text Files5 分
Avro Files5 分
Parquet Files5 分
ORC Files5 分
Other File Types5 分
Creating Tables with Avro and Parquet Files20 分
2の練習問題
Week 3 Practice Quiz20 分
Week 3 Graded Quiz30 分
5時間で修了
Managing Datasets in Clusters and Cloud Storage
5時間で修了
8件のビデオ （合計48分）, 13 readings, 3 quizzes
8件のビデオ
Refresh Impala's Metadata Cache after Loading Data2 分
Loading Files into HDFS with Hue's Table Browser10 分
Loading Files into HDFS with Hue's File Browser6 分
Loading Files into HDFS from the Command Line8 分
Loading Files into S3 from the Command Line10 分
Using Hive and Impala to Load Data into Tables3 分
Conclusion2 分
13件の学習用教材
More about HDFS Shell Commands10 分
Chaining and Scripting with HDFS Commands5 分
HDFS Permissions5 分
Other Ways to Load Files into S35 分
S3 Permissions10 分
Missing Values15 分
Character Sets5 分
Using Sqoop to Import Data15 分
More Sqoop Import Options5 分
Using Sqoop to Export Data5 分
SQL LOAD DATA Statements10 分
SQL INSERT Statements10 分
SQL INSERT ... SELECT and CTAS Statements15 分
2の練習問題
Week 4 Practice Quiz20 分
Week 4 Graded Quiz30 分
レビュー
MANAGING BIG DATA IN CLUSTERS AND CLOUD STORAGE からの人気レビュー
SYにより2020年2月29日
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 ,
AMにより2019年12月15日
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.
MAにより2020年4月13日
This is one of the systematic specializations which makes the harder and otherwise overwhelming subject so easy to navigate, follow and learn.
AZにより2020年5月3日
Although more reading than video, I found readings to be more convenient when you need to get back to a particular lesson and recap something.
Modern Big Data Analysis with SQL専門講座について
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.
よくある質問
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)
さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。
©2020 Coursera Inc.All rights reserved.
