5 Steps 小时 51 积分
Objectives:This quest is designed to teach you how to work with AWS services to perform big data analytics on the cloud.
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。
This lab demonstrates how to launch an Amazon Elastic MapReduce (EMR) cluster for Big Data processing and use Hive with SQL-style queries to analyze data. You will create a Hadoop cluster using Amazon EMR which will allow to run interactive Hive queries against data stored in Amazon S3. You will use Hive to normalize the data in a more useful way, and you will run queries to analyze the data.
In this lab, you will deploy a fully functional Hadoop cluster, ready to analyze log data in just a few minutes. You will start by launching an Amazon EMR cluster and then use a HiveQL script to process sample log data stored in an Amazon S3 bucket. HiveQL is a SQL-like scripting language for data warehousing and analysis. You can then use a similar setup to analyze your own log files.
warning Advanced Amazon Redshift: Table Layout and Schema Design
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.
warning Advanced Amazon Redshift: Data Loading
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.