Snowflake
Snowflake Description
Snowflake is a cloud-based data warehousing platform designed to handle large volumes of data and enable organizations to analyze and derive insights from their data effectively. It is built on a unique architecture that separates storage and compute resources, allowing for scalable and flexible data processing in a highly efficient manner.
Key features of Snowflake include:
- Architecture:
Snowflake uses a multi-cluster, shared data architecture, where data storage and compute resources are decoupled. This separation allows for independent scaling of storage and compute, providing flexibility and cost efficiency. - Data Storage:
Snowflake utilizes a columnar storage format optimized for analytical queries, enabling efficient storage and retrieval of data. It supports structured and semi-structured data formats, including JSON, Avro, Parquet, and more. - Compute Resources:
Snowflake offers compute resources on-demand, allowing users to scale compute power based on workload requirements. This elasticity ensures optimal performance for data processing and analytics tasks, without the need for managing infrastructure. - Concurrency:
Snowflake provides built-in support for concurrent data processing and analytics workloads. It dynamically allocates compute resources to queries, ensuring consistent performance even during peak usage periods. - Data Sharing:
Snowflake enables secure and controlled sharing of data across different organizations, departments, or teams. Users can easily share data sets with external parties while maintaining data governance and access controls.
- Snowflake Overview
- Architecture
- How to use the Snowflake UI & ecosystem
- What is Cloud
- Different Cloud Vendors
- Advantages of Cloud over On-Premise
- What is a Data Warehouse, and Why do we need a Data Warehouse?
- Database Vs Data Warehouse.
- Data Warehouse Architecture
- OLTP Vs OLAP
- What is ETL
- How different from traditional DB
- Quick start to the snowflake and accessing trial account
- Creating warehouse, DB, Schema, and tables
- Accessing different roles and using it
- Working with worksheets
- Understanding different type of accounts
- AWS and understanding S3 storage
- Snowflake architecture and caching
- AZURE and understanding blob storage
- GCP and understanding Bucket storage
- File formats
- Internal and external storage
- Internal and external stage
- Copy into usage
- Snowflake internal storage
- Accessing Cloud storage data into Snowflake (GCP, AZURE and AWS)
- Data unloading
- Accessing Snowpipe
- PUT and GET commands
- Bulk loading from cloud storage
- Continuous loading
- Snowflake Connector and use cases Python
- BI connectors use cases
- Other connectors hands-on
- Variant Data Type
- File format options
- Creating stages
- Loading JSON semi-structured data into SF tables
- Accessing JSON with select statement
- Creating Tasks
- Streams
- Accessing procedures with tasks
- Scheduling as per time with Different time zones
- Automate loading process Daily and Weekly
- Usage of sharing data
- Sharing data with different accounts
- Sharing data with non-SF accounts using reader accounts
- Importance of reader accounts
- Privileges in data sharing
- Challenges with cross-region sharing and understanding replication
- Connecting shared objects with BI tools
- Limitations with Data sharing
- Limitations with Data sharing
- Access Control Privileges for Cloned Objects
- Cloning and Snowflake Objects
- Impact of DDL on Cloning
- Impact of DML and Data Retention on Cloning
- Introduction to Time Travel
- Querying Historical Data
- Enabling and Disabling Time Travel
- Data Retention Period
- Cloning Using Time Travel (Databases, Schemas, and Tables Only)
- Creating multi-users on large tables
- Performance techniques
- Result set cache
- Metadata cache
- Query data cache
- Best practices of using caching for performance and cost optimization
- Error Handling and Validations
- Snowflake Pricing model and selecting best Edition and Calculation of Credits usage
- Resource Monitoring
- Data Masking
- Partitioning and Clustering in snowflake
- Materialized View and Normal View
- Integration with Python
- Integration with AWS, Azure and Google Cloud
- Best Practices to follow
- You will never miss a class at Makeinternship! You can choose either of the two options:
- You can go through the recorded session of the missed class and the class presentation that are available for online viewing through the LMS.
- You can attend the missed session, in any other live batch. Please note, access to the course material will be available for a lifetime once you have enrolled in the course.
- Makeinternship is committed to providing you with an awesome learning experience through world-class content and best-in-class instructors.
- We will create an ecosystem through this training, which will enable you to convert opportunities into job offers by presenting your skills at the time of an interview. We can assist you in resume building and also share important interview questions once you are done with the training. However, please understand that we are not into job placements.
- We have a limited number of participants in a live session to maintain Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording, and it would give you a clear insight into how the classes are conducted, the quality of instructors, and the level of interaction in the class.
- Trainer has 15+ years of experience in IT industry.