Building Data Analytics Solutions Using Amazon Redshift
In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads.
You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift.
In this course, you will learn to:
- Compare the features and benefits of data warehouses, data lakes, and modern data architectures
- Design and implement a data warehouse analytics solution
- Identify and apply appropriate techniques, including compression, to optimize data storage
- Select and deploy appropriate options to ingest, transform, and store data
- Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
- Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
- Secure data at rest and in transit
- Monitor analytics workloads to identify and remediate problems
- Apply cost management best practices
- This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.
Students with a minimum one-year experience managing data warehouses will benefit from this course. We recommend that attendees of this course have:
- Completed either AWS Technical Essentials or Architecting on AWS
- Completed Building Data Lakes on AWS
Module A: Overview of Data Analytics and the Data Pipeline
Data analytics use cases
Using the data pipeline for analytics
Module 1: Using Amazon Redshift in the Data Analytics Pipeline
Why Amazon Redshift for data warehousing?
Overview of Amazon Redshift
Module 2: Introduction to Amazon Redshift
Amazon Redshift architecture
Interactive Demo 1: Touring the Amazon Redshift console
Amazon Redshift features
Practice Lab 1: Load and query data in an Amazon Redshift cluster
Module 3: Ingestion and Storage
Ingestion
Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API
Data distribution and storage
Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
Querying data in Amazon Redshift
Practice Lab 2: Data analytics using Amazon Redshift Spectrum
Module 4: Processing and Optimizing Data
Data transformation
Advanced querying
Practice Lab 3: Data transformation and querying in Amazon Redshift
Resource management
Interactive Demo 4: Applying mixed workload management on Amazon Redshift
Automation and optimization
Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster
Module 5: Security and Monitoring of Amazon Redshift Clusters
Securing the Amazon Redshift cluster
Monitoring and troubleshooting Amazon Redshift clusters
Module 6: Designing Data Warehouse Analytics Solutions
Data warehouse use case review
Activity: Designing a data warehouse analytics workflow
Module B: Developing Modern Data Architectures on AWS
Modern data architectures
Class Deliverables
- Amazon Authorised Instructors
- Official AWS Content
- Hands-on labs (*where available)
- Class completion certificates
- Exam Prep sessions
Dates Available - Click on Book Now to proceed
| Virtual | 1 days | All Day | October 11, 2024 | ₹15,000 | |
| Virtual | 1 days | All Day | October 25, 2024 | ₹15,000 | |
| Virtual | 1 days | All Day | November 4, 2024 | ₹15,000 | |
| Virtual | 1 days | All Day | November 18, 2024 | ₹15,000 | |
| Virtual | 1 days | All Day | December 2, 2024 | ₹15,000 | |
| Virtual | 1 days | All Day | December 16, 2024 | ₹15,000 |
Don't see a date that works for you?
Fill in the form below to let us know.
Popular Courses
This course is designed for individuals with little to no experience on the AWS Cloud. The learners will learn about AWS Cloud concepts, AWS services such as Security, AWS Architecture, Pricing and Support to develop their knowledge on the AWS Cloud.
You will learn how to use a combination of DevOps best practices and tools to support your organization’s capability to develop, deliver and maintain applications and services at a high velocity on the AWS cloud.
The course explores the usage of the iterative Machine Learning (ML) pipeline to solve real-world business problems in a project-based environment. You will learn about each phase of the pipeline from an experienced AWS instructor.
FAQs
To enroll in this course, choose the starting date and make an online payment. Once your payment is confirmed, our team will reach out to you.
Wire Transfer, Credit Card, Debit Card, UPI & Purchase Order.
There is no minimum number of candidates required, we are happy to train 1 to 1 . With regards to the maximum number, we can accomodate 30 learners in one batch.
- Training Delivered by an Amazon Authorized Instructor.
- AWS Content E-Kit
- Hands-on-labs for 30 days
- Class attendance certificate
You will get the access to course content & lab on first day of your training session.
The course Completion Certificate will be issued to your email id within 2 weeks of completing your course.
A one-day course could be delivered over two half day sessions (4 hours a day), or a three-day course could be delivered over five days (4 hours a day)
