Infosys Snowflake Developer Interview Questions, Process, and Tips

Snowflake Architecture is a cloud-based data warehousing platform that separates storage and compute for increased scalability and performance.

• Snowflake uses a unique architecture with three layers: storage, compute, and services.

• Data is stored in Amazon S3 or Microsoft Azure Blob Storage, separate from the compute resources.

• Compute resources can be scaled up or down independently of storage, allowing for flexibility a...read more

Multi-clustering is a feature in Snowflake that allows data to be stored in multiple clusters for improved performance and scalability.

• Improves query performance by distributing data across multiple clusters

• Increases scalability by allowing more resources to be added as needed

• Helps in workload isolation and resource management

• Example: Storing customer data in one cluster and product data in another for better performan

Snowflake architecture is a cloud-based data warehousing solution that separates storage and compute resources for scalability and performance.

• Snowflake uses a unique architecture with separate storage and compute layers.

• Data is stored in scalable storage units called micro-partitions.

• Compute resources can be scaled up or down independently based on workload demands.

• Snowflake's architecture enables automatic scaling an...read more

Time travel and fail safe are features in Snowflake that allow users to access historical data and ensure data integrity.

• Time travel allows users to access historical data by querying a specific point in time, using the TIMESTAMP parameter in queries.

• Fail safe ensures data integrity by automatically saving a copy of data before any changes are made, allowing users to revert back if needed.

• Both features are important fo...read more

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale.

• Data lakes store raw data in its native format without the need to structure it beforehand

• Data lakes can store a variety of data types such as logs, images, videos, and more

• Data lakes enable data scientists and analysts to explore and analyze data without predefined schemas

Data sharing is the process of allowing multiple users or systems to access and use the same data.

• Data sharing involves granting permissions to users or systems to access specific datasets.

• It can be done through APIs, databases, file sharing, or cloud storage.

• Examples include sharing customer data between different departments in a company or sharing research data with collaborators.

Optimizing SQL queries involves using indexes, minimizing data retrieval, and avoiding unnecessary joins.

• Use indexes on columns frequently used in WHERE clauses

• Minimize data retrieval by selecting only necessary columns

• Avoid unnecessary joins by using subqueries or temporary tables

Time travel is a theoretical concept that involves moving between different points in time.

• Time travel is a popular theme in science fiction literature and movies.

• The concept of time travel raises questions about causality and the nature of time.

• Some theories suggest that time travel could be possible through wormholes or time machines.

The cache will be invalidated and refreshed when the query changes.

• When the query changes, the cache will be invalidated to ensure accurate results.

• The system will refresh the cache with the updated query results.

• This helps prevent outdated or incorrect data from being served from the cache.

I would rate myself 8 out of 10 in SQL. I have strong knowledge and experience in writing complex queries and optimizing database performance.

• Strong understanding of SQL syntax and commands

• Experience in writing complex queries involving multiple tables and joins

• Knowledge of database optimization techniques

• Familiarity with Snowflake specific SQL functions and features

Implemented data pipelines using Snowflake for ETL processes, data warehousing, and analytics.

• Designed and developed Snowflake data models for efficient storage and retrieval.

• Used Snowflake's features like clustering keys and materialized views to optimize query performance.

• Integrated Snowflake with various data sources and BI tools for seamless data processing and visualization.