AWS-DATAWARE: Data Warehousing on AWS

Name: AWS-DATAWARE: Data Warehousing on AWS
Brand: AWS

RM5,400.00

Learn from AWS specialists to improve your Data Analytics skills and understanding.

Amazon Redshift is the most commonly used cloud data warehouse, with tens of thousands of users using it to analyze exabytes of data and conduct complicated analytical queries. Without having to manage your data warehouse architecture, you can run and scale analytics on all of your data in seconds.

Learn how to use Amazon Redshift to create a cloud-based data warehousing system and how to use AWS services to gain insights and value from data by designing and implementing them. Learn about data collection, ingestion, storage, processing, and visualization through real-world scenarios.

Begin your Data Analytics education today.

Training Duration: 3 Days

Certificate Of Completion Available
Group Private Class
VILT Class Available
SBL-Khas Claimable

Download Course Outline

The AWS-DATAWARE: Data Warehousing on AWS course introduces you to concepts, strategies, and best practices for designing a cloud based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS.

This course demonstrates how to collect, store, and prepare data for the data warehouse by using AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3. Additionally, this course demonstrates how to use Amazon QuickSight to perform analysis on your data.

Course level: Intermediate
This course is intended for:

Database Architects

Database Administrators

Database Developers

Data Analysts

Data Scientists
We recommend that attendees of this course have:

Taken AWS Technical Essentials (or equivalent experience with AWS)

Familiarity with relational databases and database design concepts
In this course, you will:

Discuss the core concepts of data warehousing, and the intersection between data warehousing and big data solutions

Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a data warehouse in the cloud

Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3, to contribute to the data warehousing solution

Architect the data warehouse

Identify performance issues, optimize queries, and tune the database for better performance

Use Amazon Redshift Spectrum to analyze data directly from an Amazon S3 bucket

Use Amazon QuickSight to perform data analysis and visualization tasks against the data warehouse
Module 1: Introduction to Data Warehousing

Relational databases

Data warehousing concepts

The intersection of data warehousing and big data

Overview of data management in AWS

Hands-on lab 1: Introduction to Amazon Redshift

Module 2: Introduction to Amazon Redshift

Conceptual overview

Real-world use cases

Hands-on lab 2: Launching an Amazon Redshift cluster

Module 3: Launching clusters

Building the cluster

Connecting to the cluster

Controlling access

Database security

Load data

Hands-on lab 3: Optimizing database schemas

Module 4: Designing the database schema

Schemas and data types

Columnar compression

Data distribution styles

Data sorting methods

Module 5: Identifying data sources

Data sources overview

Amazon S3

Amazon DynamoDB

Amazon EMR

Amazon Kinesis Data Firehose

AWS Lambda Database Loader for Amazon Redshift

Hands-on lab 4: Loading real-time data into an Amazon Redshift database

Module 6: Loading data

Preparing Data

Loading data using COPY

Maintaining tables

Concurrent write operations

Troubleshooting load issues

Hands-on lab 5: Loading data with the COPY command

Module 7: Writing queries and tuning for performance

Amazon Redshift SQL

User-Defined Functions (UDFs)

Factors that affect query performance

The EXPLAIN command and query plans

Workload Management (WLM)

Hands-on lab 6: Configuring workload management

Module 8: Amazon Redshift Spectrum

Amazon Redshift Spectrum

Configuring data for Amazon Redshift Spectrum

Amazon Redshift Spectrum Queries

Hands-on lab 7: Using Amazon Redshift Spectrum

Module 9: Maintaining clusters

Audit logging

Performance monitoring

Events and notifications

Lab 8: Auditing and monitoring clusters

Resizing clusters

Backing up and restoring clusters

Resource tagging and limits and constraints

Hands-on lab 9: Backing up, restoring and resizing clusters

Module 10: Analyzing and visualizing data

Power of visualizations

Building dashboards

Amazon QuickSight editions and features
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AWS-DATAWARE: Data Warehousing on AWS

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