Snowflake Performance Automation and Tuning Training

• Resource allocation: Snowflake provides various resources, such as warehouses, virtual warehouses, and clusters, that can be used to allocate computing power and memory to data processing tasks. By properly allocating resources to workloads, data engineers can optimize performance and minimize costs.
• Query optimization: Snowflake provides query optimization capabilities that can help to identify and eliminate inefficient queries, such as those with complex joins, subqueries, or predicates. By optimizing queries, data engineers can improve query response times and reduce the overall workload on Snowflake.
• Indexing: Snowflake supports automatic indexing of data to improve query performance. By creating and using indexes for frequently queried columns, data engineers can speed up query execution and reduce the number of full table scans.
• Compression: Snowflake provides built-in compression capabilities that can help to reduce the storage footprint and improve query performance. By compressing data at the column or table level, data engineers can reduce the amount of data that needs to be read from disk and speed up query execution.
• Monitoring and alerting: Snowflake provides various monitoring and alerting capabilities that can help data engineers to identify and diagnose performance issues. By monitoring key performance metrics and setting up alerts, data engineers can proactively identify and address performance bottlenecks before they impact users.

• Understanding Snowflake's architecture and performance characteristics: Participants will learn about the underlying architecture of Snowflake and how it impacts performance. They will gain an understanding of Snowflake's distributed processing capabilities and how they can be optimized for different workloads.
• Designing and configuring Snowflake for optimal performance: Participants will learn how to design and configure Snowflake for optimal performance. They will explore best practices for allocating resources, optimizing queries, indexing data, and compressing data to improve query performance.
• Automating Snowflake performance management: Participants will learn how to automate the management of Snowflake's performance using tools such as Snowflake's Performance Tuning and Optimization (PTO) service, which can help to automatically identify and resolve performance issues.
• Monitoring and troubleshooting Snowflake performance issues: Participants will learn how to monitor and troubleshoot Snowflake performance issues using Snowflake's built-in monitoring and alerting capabilities. They will explore how to diagnose common performance issues, such as slow queries, and how to optimize query execution plans to improve performance.
• Ensuring Snowflake security and compliance: Participants will learn how to ensure the security and compliance of Snowflake by configuring access controls, managing user privileges, and monitoring for potential security breaches.

• Recorded Videos After Training
• Digital Learning Material
• Course Completion Certificate
• 24x7 After Training Support

• Data engineers: Data engineers who are responsible for designing and managing Snowflake data warehouses may benefit from a performance automation and tuning course. They can learn how to optimize Snowflake's performance and automate performance management to support data analytics requirements.
• Data analysts: Data analysts who work with Snowflake data warehouses may benefit from a performance automation and tuning course. They can learn how to optimize queries and performance, enabling them to work with large data sets efficiently.
• Business intelligence professionals: Business intelligence professionals who use Snowflake data warehouses to generate reports and insights may benefit from a performance automation and tuning course. They can learn how to optimize Snowflake's performance to ensure fast and accurate data retrieval for their analysis.
• IT managers: IT managers who oversee Snowflake data warehouse implementations may benefit from a performance automation and tuning course. They can learn how to ensure the security, compliance, and performance of Snowflake, supporting their organization's data analytics requirements.
• Database administrators: Database administrators who manage Snowflake data warehouses may benefit from a performance automation and tuning course. They can learn how to monitor, troubleshoot, and optimize Snowflake's performance to ensure that it meets the organization's data analytics requirements.

• Experience with Snowflake: You should be familiar with Snowflake's architecture, including the separation of compute and storage, Snowflake virtual warehouses, and how to navigate the Snowflake web interface.

• Basic SQL knowledge: You should have a basic understanding of SQL syntax, querying databases, and manipulating data. You should be able to write simple SQL queries.

• Familiarity with Snowflake performance concepts: You should have a basic understanding of Snowflake performance concepts, such as query optimization, virtual warehouse sizing, and scaling.

• General database knowledge: You should have a basic understanding of database management concepts such as indexing, partitioning, and data modeling.

• Familiarity with Snowflake's monitoring and performance tools: Although not mandatory, it is recommended that you have some experience working with Snowflake's performance and monitoring tools, such as Query History, Query Profile, and Resource Monitor.

• Multisoft Systems provides a training certification after successful completion Snowflake Performance Automation and Tuning Training. Anyone who is involved in the design, management, and use of Snowflake data warehouses may benefit from a performance automation and tuning course.

Performance Built from the Ground Up in Snowflake Architecture

• Automatic micro-partitioning
• Natural data clustering
• Workload segmentation
• Vectors of compute scaling
• What the execution engine can do for you
• What the optimizer can do for you
• Lightning-fast metadata service
• Caching features

Snowflake Performance Diagnostic and Tuning Toolset

• Query profile and explain plan
• Query profile and query scenarios
• Explain plan and query scenarios
• Understanding micro-partitioning and statistics
• Assessing clustering quality
• Using INFORMATION_SCHEMA functions and account usage share database
• Common investigation scenarios

Best Practices Across the Snowflake Cloud Data Platform

• Writing e ective queries
• Scaling up, right-sizing a warehouse, and concurrency
• Metadata operations and optimizations

Best Practices for Raw Data Zone

• Snowflake as a data lake
• Batch data ingestion and DML
• Streaming ingestion

Best Practices for Conformed Data Zone

• Incremental data processing and CDC
• Evaluating data formats and semi-structured data
• Data engineering pipelines
• Data clustering and tuning

Best Practices for Modeled Data Zone

• Materialized views and use cases
• High-performance query features
• Data unloading

Emerging Performance Features

• Highlight preview features and appropriate use cases
• Explore best practices for new features

Monitoring and Optimizing Costs

• Virtual warehouse compute
• Serverless compute
• Cloud services metadata operations
• Storage
• Replication
• Data transfer