Amazon Redshift Training
Amazon Redshift Training equips users with the skills to effectively use Amazon’s fully-managed, cloud-based data warehouse service. Training focuses on setting up, managing, and optimizing Redshift for analytics and data-driven decision-making.
Why should you choose Nisa For Amazon Rdshift Training?
Nisa Trainings is the best online training platform for conducting one-on-one interactive live sessions with a 1:1 student-teacher ratio. You can gain hands-on experience by working on near-real-time projects under the guidance of our experienced faculty. We support you even after the completion of the course and happy to clarify your doubts anytime. Our teaching style at Nisa Trainings is entirely hands-on. You’ll have access to our desktop screen and will be actively conducting hands-on labs on your desktop.
Job Assistance
If you face any problem while working on Amazon Redshift Course, then Nisa Trainings is simply a Call/Text/Email away to assist you. We offer Online Job Support for professionals to assist them and to solve their problems in real-time.
The Process we follow for our Online Job Support Service:
- We receive your inquiry for Online Job
- We will arrange a telephone call with our consultant to grasp your complete requirement and the tools you’re
- If our consultant is 100% confident in taking up your requirement and when you are also comfortable with our consultant, we will only agree to provide service. And then you have to make the payment to get the service from
- We will fix the timing for Online Job Support as mutually agreed by you and our consultant.
Course Information
Amazon Redshift Training
Duration: 25 Hours
Timings: Weekdays (1-2 Hours per day) [OR] Weekends (2-3 Hours per day)
Training Method: Instructor Led Online One-on-One Live Interactive
Sessions.
COURSE CONTENT :
1. Introduction to Amazon Redshift
- Overview of Data Warehousing and Redshift
- What is Amazon Redshift?
- Redshift architecture and ecosystem
- Key features: columnar storage, massively parallel processing (MPP), and automatic scaling
- Use Cases for Amazon Redshift
- Analytics, reporting, and business intelligence
- Real-time data warehousing and integration with other AWS services
2. Setting Up Amazon Redshift
- Provisioning a Redshift Cluster
- Cluster types (single node vs. multi-node)
- Configuring nodes, regions, and instance types
- VPC and security group configurations
- Connecting to Redshift
- Using SQL clients, JDBC, and ODBC
- Configuring client tools like Tableau, Power BI, and Looker
3. Working with Data
- Loading Data into Redshift
- Data ingestion from S3, DynamoDB, and external databases
- COPY command for bulk loading
- Best practices for data loading
- Data Modeling
- Designing schemas (star vs. snowflake schema)
- Defining tables, keys, and constraints
- Querying Data
- Writing SQL queries in Redshift
- Using Redshift-specific functions and commands
4. Performance Tuning and Optimization
- Query Performance Optimization
- Analyzing and optimizing query execution plans
- Using VACUUM and ANALYZE commands
- Best practices for distribution keys and sort keys
- Workload Management (WLM)
- Configuring WLM for query prioritization
- Managing concurrency and resource allocation
- Compression and Storage Optimization
- Using Redshift’s columnar storage
- Choosing the right compression encodings
5. Data Security
- Redshift Security Features
- Encryption at rest and in transit (SSL, AWS KMS)
- Role-based access control (RBAC)
- Managing Users and Permissions
- Creating users and groups
- Setting permissions and roles
- Auditing and Monitoring
- Enabling logging with AWS CloudTrail and CloudWatch
6. Integration with Other AWS Services
- Integrating with Data Lakes
- Using Redshift Spectrum to query S3 directly
- Data lake architecture with Glue and Athena
- ETL and Data Pipelines
- Integrating with AWS Glue, Lambda, and Step Functions
- Automating data pipelines for ingestion and transformation
- BI and Machine Learning
- Connecting Redshift to SageMaker, QuickSight, and third-party BI tools
7. Managing Redshift Clusters
- Monitoring Cluster Health
- Using the Redshift Console and CloudWatch
- Monitoring query performance and cluster metrics
- Backup and Restore
- Automated snapshots and point-in-time recovery
- Restoring clusters from backups
- Scaling Clusters
- Elastic resize for scaling up and down
- Redshift Serverless for on-demand scaling
8. Advanced Features
- Redshift Serverless
- Setting up and using Redshift Serverless
- Optimizing costs and performance in a serverless setup
- Data Sharing
- Sharing data across Redshift clusters
- Use cases for cross-region and cross-account sharing
- Federated Queries
- Querying external data sources without moving data
- Integration with RDS, Aurora, and other databases