Snowflake Advanced Training
This three-day course covers advanced Snowflake features related to data movement, performance, authentication, design considerations, and recommended best practices in the Snowflake Data Cloud. This advanced course consists of lectures, demos, and labs.
In this course, you will:
- Evaluate Snowflake’s advanced architectural concepts.
- Design a bulk loading and load troubleshooting strategy.
- Employ strategies and Snowflake-recommended best practices for continuous data processing.
- Develop a methodology for performance tuning for your Snowflake Data Cloud.
- Protect data access at the table, column, and row levels.
- Use data sharing and database replication.
This course is intended for:
- Data Analysts
- Data Engineers
- Data Scientists
- Database Architects
- Database Administrators
We recommend that attendees of this course have:
- Completion of “Snowflake Fundamentals” or equivalent Snowflake knowledge.
Module 1: Architecture Highlights
- Architectural Layers
- Caching
Module 2: Connecting to Snowflake
- What is Integration?
- Security Integration
- Key Pair Authentication
Module 3: Extensibility and Programmability
- External Functions
- API Integrations
- Tools and Utilities
- Developer Framework
- Turn Python Scripts into Web Apps (Streamlit)
Module 4: Working with Semi-Structured Data
- Structured vs. Semi-structured
- Loading and Transforming Semi-structured Data
- Schema Detection
- Schema Evolution
Module 5: Working with Unstructured Data
- Overview
- Concepts
- Workflow
Module 6: Working with Geospatial
- Geospatial Overview
- Geography
- Geometry
Module 7: Data Loading and Troubleshooting
- Data Ingestion Options
- Fixing Load Failures
- Continuous Loading with Snowpipe
- Notification Integration
- Snowpipe Streaming Overview
- Snowpipe Streaming Detail
Module 8: Transformation Pipelines
- Traditional Micro-batch Pipeline
- Alternative Continuous Data Pipeline
- Stream Details
- How Streams Work
- Common Use Cases
- Dynamic Tables
Module 9: Working with Data Lakes
- Data Lake Deployments
- Querying External Data Lakes
- Creating and Querying External Tables
- Partitioning External Tables
Module 10: Data Clustering
- What is Data Clustering?
- Micro-partition Pruning (Elimination)
- Evaluating Clustering
- Implement and Test Cluster Keys
Module 11: Performance Optimization
- Search Optimization
- Materialized Views
- Materialized View Use Cases
- When to Use Materialized Views
- Query Acceleration Services (QAS)
- Join Elimination Using Constraint Support
Module 12: Data Sharing
- Data Access Options
- Direct Data Sharing Workflow
Class Deliverables
- E-Content kit by AWS
- Hands-on labs
- Class completion certificates
- Exam Prep sessions
Class Schedule
Stay ahead with our comprehensive course schedule. Browse upcoming training sessions & reserve your spot to gain hands-on expertise & certification-ready skills.
| Virtual | 3 days | All Day | June 3, 2024 | ₹0 | |
| Virtual | 3 days | All Day | June 17, 2024 | ₹0 | |
| Virtual | 3 days | All Day | July 15, 2024 | ₹0 | |
| Virtual | 3 days | All Day | July 22, 2024 | ₹0 |
FAQs
Find answers to commonly asked questions about our courses, certifications, schedules, and more.
Your cloud learning journey made simple and transparent.
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)
