Building Data Analytics Solutions Using Amazon Redshift Training in US
Learn to build and manage data analytics pipelines
8 hours of training program
In-depth course structure with eight modules
Integrate Amazon Redshift with a data lake
Guided instructive sessions from expert trainers
Includes interactive demos, practice labs and class exercises
Flexible learning options designed for a busy schedule
Hassle-free payment options in instalments
Overview
- Explore real-world uses and applications of data analytics
- Understand how Amazon Redshift is structured for data warehousing
- Get practical experience using the Amazon Redshift console
- Learn how to load data and run queries in Redshift clusters
- Use Amazon Redshift Spectrum to analyse large datasets
- Work with Jupyter notebooks for importing and analysing data
- Learn how to secure and monitor Redshift clusters effectively
Upcoming sessions
Curriculum
Data analytics use cases
Using the data pipeline for analytics
Why Amazon Redshift for data warehousing?
Overview of 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
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
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
Securing the Amazon Redshift cluster
Monitoring and troubleshooting Amazon Redshift clusters
Data warehouse use case review
Activity: Designing a data warehouse analytics workflow
Modern data architectures
Learning Outcomes
After you complete this training, you will be able to:
1
Understand how Amazon Redshift is built and how it fits into data analytics workflows
2
Learn how to work with semi-structured data using the SUPER data type in Amazon Redshift
3
Build skills in using Redshift Spectrum to run queries on large datasets stored in Amazon S3
4
Apply best practices to resize and tune Redshift clusters for better performance
5
Develop advanced knowledge of how data is stored and distributed in Amazon Redshift
Prerequisites
To enrol in this Building Data Analytics Solutions Using Amazon Redshift Training in Sweden, candidates must fulfil these eligibility requirements:
- A minimum of one year of experience managing data warehouses is required
- Must have completed either AWS Technical Essentials or Architecting on AWS
- Must have completed the Building Data Lakes on AWS course
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Frequently asked questions
Our Building Data Analytics Solutions Using Amazon Redshift Training in the US is an interactive, instructor-led course. Professionals learn how to use Amazon Redshift to design, develop, and optimize data analytics solutions. It is a cloud-based data warehouse service offered by AWS. Data engineers, data warehouse developers, cloud architects, and IT specialists will find this course excellent.
You can look for jobs such as:
- Big Data Engineer
- Cloud Data Engineer
- Business Intelligence Analyst
- Data Warehouse Consultant
- AWS Data Specialist
Most hiring companies look for talent that can create and tune Redshift clusters to perform effective analytics.
Yes, this training is commonly offered both in online real-time format and in self-study recorded versions. Numerous academies also offer downloadable materials, lab access, and personalized support to support remote learning.
The professionals must know about fundamental AWS services, SQL, and data warehousing principles. The candidates should have some experience with tools such as S3, IAM, and EC2 so that they can easily understand Redshift concepts and gain hands-on practice while training.
Applicants must have at least one year of data warehouse management experience. They should have completed some of the foundational courses in AWS, such as AWS Technical Essentials or Architecting on AWS, preferably Building Data Lakes on AWS. This provides a solid basis for Redshift-based analytics learning.
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