Snowflake Data Warehouse & Analytics

A virtual Live course conveniently designed for the working professionals to learn Snowflake technology.

Live Class: Tuesday, 31 Aug

Enrolled: 0

Duration: 36 Hours

Offered by: infyni

About Course

Snowflake Data Warehouse & Analytics course covers advanced Data Movement, Performance, Security, Agile Development and Data Sharing design considerations and best practices in the Snowflake Cloud Data Platform. It enables you to build data-intensive applications without operational burden. Trusted by fast growing software companies, Snowflake handles all the infrastructure complexity, so you can focus on innovating your own application.

The data engineer’s role is changing quickly. As new tools and self-service data pipelines eliminate traditional tasks such as manually writing ETL code and cleaning data, companies are asking data engineers to provide guidance on data strategy and pipeline optimization.

Course shall be interactive with lecture, demos, and labs.

Skills You Will Gain

Business Intelligence Data Warehousing Expected Return Sap Hana

Course Offerings

  • Instructor-led interactive classes
  • Clarify your doubts during class
  • Access recordings of the class
  • Attend on mobile or tablet
  • Live projects to practice
  • Case studies to learn from
  • Lifetime mentorship support
  • Industry specific curriculum
  • Certificate of Completion
  • Employability Opportunity
  • Topics
  • Instructor
  • Creating Snowflake Account
  • Testing Snowflake
  • Introduction
  • Understanding the Web UI Components
  • Installing SnowSql
  • Using Command Line Interface
  • In-Dept Snowflake Architecture 2.0
  • DDL Commands Refresher
  • DDL Creating VW
  • DDL Create DB, Schemas
  • DDL Creating Materialized Views
  • DDL Alter, Drop Etc.
  • DML - Select Statements
  • DML - Operators
  • DML - Group By, Order By, Having Clause
  • DML - Joins, Right, Inner & Outer
  • Sub-query with From Clause, Where, Having Clause
  • Case Statements
  • Operators (UNION, UNION ALL, INTERSECT, Minus)
  • ABS, Ceil, Floor, Coalesce
  • String, Date, Time
  • System, Rollback
  • Bulk Loading DATA / Big Data
  • Data Load - Copy - Bulk Load
  • Apply transformation while Loading
  • Loading COMPRESSED file from AWS S3 to Snowflake
  • Additional Data Load and Transformations
  • Internal Stages
  • Types of Internal Stages (User, Table)
  • External Stages
  • Results Cache
  • Local Cache
  • Disk Cache
  • Creating Cluster Tables
  • Cluster Tables with expressions
  • Order By Clause
  • Manual re-clustering
  • Best practices in Micro-Partitioning and Clustering
  • Multi-Cluster Virtual Warehouses
  • Launching New Clusters
  • Using Dedicated Virtual Warehouses
  • Scale Out - Auto Scale
  • Query Management and Optimization
  • Concurrency Control
  • Establishing Secure Connection AWS-Snowflake
  • AWS Roles
  • Loading data to AWS S3 Buckets
  • Creating Table, External Stage Objects
  • File Format Objects
  • Creating INTERGATION Objects
  • CSV Data Loading from AWS to Snowflake
  • Parquet Data Loading from AWS to Snowflake
  • JSON Data Loading from AWS to Snowflake
  • Nested Data Loading from AWS to Snowflake
  • XML Data Loading from AWS to Snowflake
  • SNOWPIPES
  • Configure AWS S3 for Snowpipe
  • AWS Events
  • SQS Queue
  • Permanent Tables
  • Transient Tables
  • Temporary Tables
  • Set Data Retention Timings
  • Query Historical Data
  • Cloning Historical Objects
  • Time Travel for Data Recovery
  • Detailed Overview
  • Features by Snowflake
  • What is a Tree of Tasks?
  • Implement a Standalone Task
  • Stored Procedures using Tasks
  • Clone Database, Pipes, Streams, Tasks& Stages
  • Clone Schema & Tables
  • Cloning Table Storage Metrics
  • Cloning Using Time Travel
  • Swap Tables
  • Secure data Sharing
  • Sharing with other Snowflake users
  • Sharing with non-Snowflake Users
  • Snowflake Approach to Access Control & Key Concepts
  • Role Hierarchy in Snowflake
  • Connect Tableau with Snowflake
  • Connect Power BI with Snowflake
  • Creating Graphs and Chartsl
  • Best practices while doing clone.
  • Best practice while creating database and tables.
  • Best practice while using virtual warehouse.
  • Best practice while using retention period and time travel

...
Yogesh Agrawal
About Instructor

10+ years of experience in Business Intelligence domain with core experience in design and implementation of Data Warehousing and Data Integration Solutions.


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