Advanced Operations Using Amazon Redshift
5 实验练习 5小时 7分钟 33 积分
In this Quest, you will delve deeper into the uses and capabilities of Amazon Redshift. You will use a remote SQL client to create and configure tables, and gain practice loading large data sets into Redshift. You will explore the effects of schema variations and compression. You will explore visualization of Redshift data, and connect Redshift with Amazon Machine Learning to create a predictive data model.
The lab will give you the basic understanding of Amazon Redshift data warehouse service. It will demonstrate the basic steps required to get started with Redshift: creating a cluster, loading data and performing queries against that data.
本实验演示了如何使用 Amazon RedShift 来创建集群、加载数据、运行查询以及监控性能。注意：在本实验中，学员需要下载免费的 SQL 客户端。
In this hands-on lab, you'll learn how to load data from Amazon S3 into an Amazon Redshift cluster and use Tableau Desktop for creating visualizations from that dataset. Note: registration and providing your personal contact information to Tableau is required for access to the trial version of Tableau Desktop needed for this lab. You may be contacted by Tableau as per their license agreements, which are provided during installation.
In this lab, you will experiment with and compare different types of data loading using Amazon Redshift. You will create tables, load data using S3, remote hosts, and practice troubleshooting data loading errors. For the lab to function as written, please DO NOT change the auto assigned region.
In this lab, you will take a close look at different types of table layout and schema design. You will create tables using various methods for data compression and distribution, and analyze which methods work best, including incorporating Amazon Redshift recommendations. You will conclude the lab by building five different versions of the same table, and analyzing how the differences impact storage requirements and query performance. Pre-requisites: To successfully complete this lab, you should be familiar with Redshift concepts. Knowledge of SQL programming is required, although full solution code is provided.