Snowflake

Snowflake is a cloud-based data warehouse and analytical tool. With so many SaaS Data warehouse solutions available competing against one another, Snowflake stands out among the crowd for its...

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Course Code INNO-SNOWFLAKE
Duration 4 days
Available Formats Classroom
6119 Reviews star_rate star_rate star_rate star_rate star_half
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Snowflake is a cloud-based data warehouse and analytical tool. With so many SaaS Data warehouse solutions available competing against one another, Snowflake stands out among the crowd for its uniqueness in design and approach. Unlike other data warehouse systems, Snowflake is not built on Big Data platforms; instead, it works on a new SQL engine best suited for the cloud. Snowflake has one of the best ACID (atomicity, consistency, isolation, and durability) compliant solutions. One of Snowflake’s unique capabilities is its table structures wherein Micro–Partitions and Data–Clustering is adopted. Snowflake allows users to “time travel,” i.e., track data changes over time and view changes made in the past 90–days. Some of the other features of Snowflake are:

  • Cloning – the clone feature creates an instant copy of any Snowflake object
  • Undrop – any dropped object (Databases, schemas, tables, etc.) can be undropped
  • Fail-safe – In the event of any disaster like hardware or disk failures, Snowflake engineers can recover the data for up to seven days.

Skills Gained

Attendees will leave understanding the Snowflake architecture, how to load and transform data, and how to evaluate query constructs, DDL and DML Operations. They will also learn how to manage application and user access, learn best practices for working with semi-structured data, and employ Snowflake's method for continuous data protection.

Who Can Benefit

The audience for this class is Data Analysts, Data Engineers, Data Scientists, Data Architects, and Database Administrators.

Prerequisites

Attendees should have Data Warehouse knowledge

Course Details

Outline

Data Warehousing Overview

  • Data warehousing evolution
  • Cloud data warehousing
  • Adapting to increasing demands for data access and analytics
  • Adjusting to how data is created and used today

Architecture and Overview

  • Technical Overview
  • Cloud Services Layer
  • Compute Layer
  • Storage Layer

Data Movement

  • Data Loading
  • Data Unloading
  • Best Practices
  • Data Sharing

Objects and Commands

  • Query Constructs
  • Data Description Language (DDL)
  • Data Manipulation Language (DML)
  • Local only resources

SQL Support for Data Analysis

  • SQL Support and Query Best Practices
  • SQL Analytic Functions
  • High Performing Estimation Functions
  • UDF and Stored Procedure
  • Demo Query Profile

Managing Security

  • Data Encryption
  • Authentication
  • Role-Based Access Control

Semi-structured data

  • Working with semi-structured data
  • Queries
  • Data Optimization

Caching

  • Caching Features
  • Performance Improvements
  • Cost Optimization

Clients and Ecosystem

  • Clients
  • Connectors
  • SnowSQL

Security

  • Continuous Data Protection
  • Time Travel
  • Cloning

Performance and Concurrency

  • Query Profile
  • Micro-Partitions
  • Data Clustering
  • Scaling a Virtual Warehouse

Account and Resources Management and Monitoring

  • System Resource Usage and Billing
  • Managing Virtual Warehouses
  • Workload Independence and Segmentation
  • Resource Monitors
  • Information Schema and Account Usage
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