Learn how to work with Snowflake...
Data Warehouses are the essential part for analysis. But developing or maintaining data warehouses is not an easy task. It requires a lot of sagacity and it is an emaciated task. Over the times, along with the development in technology sector, there is tool launched publicly in 2014 by Bob Mugila i.e. Snowflake. Snowflake solves all the problems of traditional warehouses and provides an ideal data warehouse and analytics solution. It is an analytical data warehouse, which can be used as Software-as-a-service(SaaS). It provides a warehouse which is fast, easy to use, and far more flexible as compared to traditional warehouses.
Upon completion of this course, you should be able to:
- How Snowflake enables businesses to achieve desired results with least effort
- Capabilities of Snowflake
- About Snowflake Architecture
- How to load data batch and real time data into Snowflake?
- How to work with Key Data formats in Snowflake?
- How to Exchange Data without moving it out of Snowflake?
- About Snowflake Security, Billing, Caching etc.
The intended audience for this course
- Data Engineers
Getting Started with Snowflake
- Evolution of Data Warehousing Technologies
- What’s missing in Hive?
- What is Snowflake?
- Why Snowflake?
- Snowflake vs Big Query vs RedShift
- Characteristics of Modern Data Pipeline
- Key Concepts
- Snowflake Architecture
- Setting up Snowflake Trial Account
- Ways to interact with Snowflake
Load and Transform Data
- Bulk Data Loading
- Continuous Auto-ingest
- Applying transformations
- Best Practices
- Hands-on Lab: Batch Data Ingestion
- Hands-on Lab: Streaming Data Ingestion using Snowpipe
- Hands-on Lab: Automating Snowpipe using AWS Lambda (Python)
Evaluate query constructs as well as core DML & DDL operations
- Data Lifecycle
- DDL and extensions
- ANSI SQL compliant
- Advanced DML
- Hands-on Lab: Working with SnowSQL
Review Snowflakes broad SQL support for data analysis
- Integrating disparate data
- Materialized Views
- Cloning and Time Travel
- System-defined Functions
- Hands-on Lab: Materialized views
Extending Snowflake Capabilities
- Custom defined Functions in SQL
- Stored Procedures
- Hands-on Lab: Working with Custom function in SQL
- Hands-on Lab: Defining Stored Procedures
Demonstrate Snowflake best practices for working with semi-structured data
- Loading semi-structured data
- Parsing and transformation
- Hands-on Lab: Working with JSON data
Maintaining, Monitoring and Tuning Snowflake
- Query Profiling
- Data clustering
- Maintenance activities
- Hands-on Lab: Query Profile
Employs Snowflake's Method for continuous data protection
- User and Role management
- Mapping AWS S3 Roles to Snowflake Roles
- Data Encryption
- Best Practices
Describe how user and application access can be easily managed
- Integration Support with External Applications
- Best Practices for building Data Apps on Snowflake
- Best Practices for integration with Tableau
- Hands-on Lab: Connecting Snowflake with Tableau
- Hands-on Lab: Integrating PySpark with Snowflake
- Hands-on Lab: Snowflake with Java
Utilize data sharing to send your data in real-time to customers and partners
- Data Providers and Consumers
- Secured Data Sharing
- Reader Accounts
Explain the various ways you can manage and monitor your Snowflake account
- Resource Monitors
- Object, User and Account-Level Credit and Storage usages
- Data Transfer Billing
- Attendees should have working experience with Datawarehouses.
- Knowledge of Apache Spark, Apache Hadoop, AWS EMR, AWS SNS etc. will be helpful.
- AWS Account