Scalable Data Architectures: Introduction

Scalable Data Architectures: Introduction

• Course Overview
• recognize the need to scale architectures to keep up with the needs for storage and processing of big data
• identify the characteristics of data warehouses that make them ideally suited to the task of big data analysis and processing
• distinguish between relational databases and data warehouses
• recognize the specific characteristics of systems meant for online transaction processing and online analytical processing and how data warehouses are an example of OLAP systems
• identify the various components of data warehouses that enable them to work with varied sources, extract and transform big data, and generate reports of analysis operations efficiently
• recall the features of Amazon Redshift that enable big data to be processed at scale
• list the features of data warehouses and contrast them with those of relational databases, and contrast the two options available to scale compute capacity

Explore theoretical foundations of the need for and characteristics of scalable data architectures in this 8-video course. Learn to use data warehouses to store, process, and analyze big data. Key concepts covered here include how to recognize the need to scale architectures to keep up with needs for storage and processing of big data; how to identify characteristics of data warehouses ideally suiting them to tasks of big data analysis and processing; and how to distinguish between relational databases and data warehouses. Next, learn to recognize specific characteristics of systems meant for online transaction processing and online analytical processing, and how data warehouses are an example of online analytical processing (OLAP) systems. Then, learn to identify various components of data warehouses enabling them to work with varied sources, extract and transform big data, and generate reports of analysis operations efficiently. Finally, study features of Amazon Redshift enabling big data to be processed at scale; features of data warehouses, contrasted with those of relational databases; and two options available to scale compute capacity.

Scalable Data Architectures: Introduction to Amazon Redshift

Scalable Data Architectures: Introduction to Amazon Redshift

• Course Overview
• use the Amazon Redshift Quick Launch feature to provision a data warehouse on Amazon Web Services
• define additional configuration options when provisioning a Redshift cluster by using the default cluster
• recognize the various tool configuration options available for a Redshift cluster and use the metrics available to optimize a cluster configuration
• create an IAM role on AWS that includes the necessary permissions to interact with the Redshift and S3 services
• provision an IAM user that can be used to connect to and interact with AWS using the CLI
• install the AWS command line interface and use it to create and delete Redshift clusters
• use the Redshift Query Editor to create tables, load data, and run queries
• recall the features of Amazon Redshift and the commands and configurations needed to work with Redshift using the CLI

Using a hands-on lab approach, explore how to use Amazon Redshift to set up and configure a data warehouse on the cloud in this 9-video course. Discover how to interact with Redshift service with both the console and Amazon Web Services (AWS) Command Line Interface (CLI). Key concepts covered here include how to use the Amazon Redshift Quick Launch feature to provision a data warehouse; provisioning a Redshift cluster with the default cluster; and tool configuration options for a Redshift cluster, and metrics available to optimize a cluster configuration. Next, learn how to create Identity and Access Management (IAM) roles on AWS that include necessary permissions to interact with Redshift and S3 services; to provision an IAM user that can connect to and interact with AWS using the CLI; and to install the AWS command-line interface to create and delete Redshift clusters. Then learn to use Redshift Query Editor to create tables, load data, and run queries; and learn features of Amazon Redshift and commands and configurations needed to work with Redshift by using the CLI.

Scalable Data Architectures: Working with Amazon Redshift & QuickSight

Scalable Data Architectures: Working with Amazon Redshift & QuickSight

• Course Overview
• use the AWS console to load datasets to Amazon S3 and then load that data into a table provisioned on a Redshift cluster
• run queries on data in a Redshift cluster and use the query evaluation feature to analyze the query execution metrics
• work with the SQL Workbench client to connect to and query data in a Redshift cluster
• disable automated snapshots for a Redshift cluster and configure a table to be excluded from snapshots
• recover an individual table from the snapshot of an entire cluster
• add more nodes to a Redshift cluster
• scale up each individual node of a Redshift cluster and scale down the number of nodes
• create a security group rule to enable access from Amazon's QuickSight servers to a Redshift cluster
• configure Amazon QuickSight to load data from a table in a Redshift cluster for analysis
• use the QuickSight dashboard to generate a time series plot to visualize sales at a retailer over time
• configure snapshots of Redshift clusters and recall the steps involved in analyzing data in Redshift using QuickSight

In this 12-video course, explore the loading of data from an external source such as Amazon S3 into a Redshift cluster, as well as configuration of snapshots and resizing of clusters. Discover how to use Amazon QuickSight to visualize data. Key concepts covered in this course include using the AWS console to load data sets to Amazon S3 and then into a table provisioned on a Redshift cluster; running queries on data in a Redshift cluster with the query evaluation feature; and working with SQL Workbench to connect to and query data in a Redshift cluster. Learn how to disable automated snapshots for a Redshift cluster and configure a table to be excluded from snapshots; recover an individual table from the snapshot of an entire cluster; and create a security group rule enabling access from Amazon's QuickSight servers to a Redshift cluster. Next, configure Amazon QuickSight to load data from a table in a Redshift cluster for analysis; and use the QuickSight dashboard to generate a time series plot to visualize sales at a retailer over time.