Snowflake Data Engineer Training

• Familiarize participants with the Snowflake cloud data warehousing platform and its key features and capabilities.
• Teach participants how to design and implement effective data models that support their organization's data analytics requirements.
• Train participants on how to load data into Snowflake, transform and integrate data, and manage data quality.
• Provide participants with practical experience in building data pipelines, including designing, implementing, and scheduling ETL processes. Teach participants how to optimize performance, improve scalability, and ensure data security and compliance.
• Enable participants to integrate Snowflake with other data analytics tools and technologies, such as Python, R, and business intelligence (BI) platforms.

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

• Data Engineers
• Data Architects
• IT Professionals
• Business Intelligence (BI) Professionals

• Experience with SQL: You should be familiar with writing SQL queries to retrieve and manipulate data. This includes knowledge of SQL syntax, querying databases, and manipulating data.

• Basic understanding of database management concepts: You should have a basic understanding of database management concepts such as database design, normalization, indexing, and data modeling.

• Cloud computing knowledge: You should have a basic understanding of cloud computing concepts, such as the different cloud service models (SaaS, PaaS, and IaaS) and the benefits of using cloud platforms.

• Familiarity with Snowflake: Although not mandatory, it is recommended that you have some experience working with Snowflake's data warehousing platform before taking this course.

• General programming skills: Familiarity with a programming language such as Python or Java would be beneficial, but not essential.

• Multisoft Systems provides a training certification after successful completion Snowflake Data Engineer Training.

Snowflake Architecture and Overview

• Snowflake Architecture
• Cloud Services Layer
• Data Storage Layer
• Compute Layer
• Sample Data
• Web User Interface
• SnowSQL

Time Travel and Cloning

• Time Travel
• Cloning and Agile Development

Snowflake Structure and SQL

• Snowflake Structure
• Data Definition Language (DDL)
• Data Manipulation Language (DML)

Data Loading

• Data Loading Concepts
• Bulk Loading

Semi-structured Data

• Querying
• Load and Unload

Data Loading Considerations

• Snowflake Best Practices
• Data Loading, Transformations, and Validations

Performance

• Query Profile

Explain Plan

• SQL Performance Tips
• Performance Bottleneck Scenarios

Caching

• Metadata Cache
• Query Result Cache
• Data Cache

Clustering

• Natural Clustering