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Amazon RedShift Classroom and Online Training from Hyderabad

RKSOFT is a Best of Best Amazon Redshift Training institute providing customer project-based Training and Placements assurance in Hyderabad.

Amazon Redshift is a data warehouse service that makes it simple to efficiently analyse all your data using your existing business intelligence tools.

Redshift Course Content:-

Introduction to Amazon Web services

Ø Amazon Web Services Stack

Ø Introduction to Amazon DATABASE

Introduction to AWS Redshift

Ø  AWS Redshift – Data Warehouse-as-a-Service

Ø  Features 

Ø  Pricing

Walkthrough the AWS Management Console. – Hands On

Redshift Architecture Overview

Ø  Clusters 

Ø  Leader and Compute Nodes 

Ø  Node Slices 

Ø  Columnar Storage for performance

Ø  Economics of Redshift 

Ø  Key differentiators

Ø  Common Use cases

Up and Running with AWS Redshift – Hands On

Ø  Launch a new Redshift Cluster 

Ø  Modifying a Cluster – resize, showdown, delete, reboot.

Ø  Security Groups.

Ø  Parameter groups.

Ø  Database Encryption. 

Ø  Backup and recovery – creating manual snapshot and automatic snapshots.

Ø  Authorize access to Cluster 

Ø  Getting Information about Cluster Configuration. 

Ø  Database Audit Logging

Accessing Amazon Redshift cluster – Hands On

Ø  JDBC and ODBC interfaces

Ø  Install and configure client SQL tools using JDBC and/or ODBC drivers

Ø  Create Database, Users, user groups, permissions and access controls.

Ø  Connect to Redshift Cluster

Ø  Load sample data into cluster

Ø  Create and test queries against the data

Monitor cluster performance – Hands On

Ø  Analyzing cluster Performance data

Ø  Analyze query execution 

Ø  Creating Alarm and working with performance metrics

Designing tables – Deep dive – Hands On

Ø  DDL SQL – Creating Tables, Alter tables, Drop tables.

Ø  LIMITATIONS and what is implemented differently. 

Ø  Selecting distribution Style and distribution keys. 

Ø  Selecting Sort Key. 

Ø  Choose best Distribution key covering various use cases

Ø  Choosing best sort keys covering various use cases.

Ø  Choosing a column compression type.

Ø  Define constraints

Loading data – Deep Dive – Hands On

Ø  Using Copy to Load data

Ø  Loading data from S3

Ø  Using a Manifest to Specify Data Files

Ø  Loading Compressed Files

Ø  Loading Fixed-Width Data

Ø  Loading Multi-byte Data

Ø  Loading Encrypted Data Files

Ø  Loading from JSON files.

DML Operations

Ø  Insert, Select, Update, Delete 

Ø  Deep Copy 

Ø  LIMITATIONS Troubleshooting

Ø  S3ServiceException Errors

Ø  System Tables for Troubleshooting Data Loads 

Ø  Multi-byte Character Load Errors 

Ø  Error Reference

Unloading Data

Ø  Unloading Data to Amazon S3 

Ø  Unloading Encrypted Data Files

Ø  Unloading Data in Delimited or Fixed-Width Format 

Ø  Reloading Unloaded Data

Performance Tuning – Hands On

Ø  Query Processing

Ø  Query Planning And Execution Workflow 

Ø  Reviewing Query Plan Steps

Ø  Query Plan 

Ø  Factors Affecting Query Performance 

Ø  Analyzing and Improving Queries 

Ø  Query Analysis Workflow

Ø  Reviewing Query Alerts 

Ø  Analyzing the Query Plan 

Ø  Analyzing the Query Summary 

Ø  Improving Query Performance

Ø  Diagnostic Queries for Query Tuning

Ø  Implementing Workload Management

Ø  Defining Query Queues

Ø  Modifying the WLM Configuration 

Ø  WLM Queue Assignment Rules 

Ø  Assigning Queries to Queues

Ø  Dynamic and Static Properties 

Ø  Monitoring Workload Management 

Ø  Configuring WLM Queues to Improve Query Processing

Ø  Troubleshooting Queries

Ø  Building Admin queries from system tables to analyze performance.

Ø  Migration of Existing BI Systems to Redshift

Ø  Data Loading from OLTP databases to Redshift and Limitations. 

Ø  ETL and ELT on Redshift. 

Ø  Limitations from industry standard Integration and BI tools w.r.t Redshift. 

Ø  Build custom ETL and/or ELT framework from an OLTP db to Redshift

Ø  Use Powershell /Python AWS SDK’s for loading data from SQL Server/ORACLE/Postgres to Redshift through S3.

Ø  Limitations and Best Practices for Redshift Data warehouse implementation.


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