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BigData ImmersionDay

  • Lab1 存储内的轻量级 ETL – Amazon S3 Select

  • Lab2 无服务器 ETL - AWS Lambda

  • Lab3 Hadoop(Hive&Spark) - Amazon EMR

  • Lab4 数据仓库 - Amazon Redshift 入门

  • Lab5 启动深度学习 EC2 环境 Amazon Deep Learning AMI

前置准备

  1. 本机安装 Python3 以运行实验中的 Python 示例

  2. 本机安装 boto3，安装参考命令

    pip3 install boto3 --user

  3. 本地进行 AWS Credentials 配置

    aws configure

配置可以访问 AWS 的 access key，如果之前使用过命令行则重用CLI的配置即可。

  1. 下载以下2个样例数据文件，并上传到你账户的 S3 某个 Bucket 中

    pagecounts-20100212-050000.gz

    users-data.json

  2. 安装 数据仓库客户端

安装以下其中一个客户端，或其他可以访问 PostgreSQL 的工具，例如：navicat

  • 安装 SQL Workbench (C/S)

https://docs.aws.amazon.com/zh_cn/redshift/latest/mgmt/connecting-using-workbench.html

** 下载 JDBC 驱动程序 下载驱动

  • 或安装 pgweb (B/S)

https://docs.aws.amazon.com/zh_cn/redshift/latest/mgmt/connecting-from-psql.html

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