10+ Snowflake Data Engineer Interview Questions and Answers

Performance tuning options in Snowflake include clustering, materialized views, query profiling, and resource monitoring.

• Use clustering keys to organize data for faster query performance

• Create materialized views to pre-aggregate data and improve query speed

• Utilize query profiling to identify and optimize slow queries

• Monitor resource usage to ensure efficient query execution

Snowflake offers unique architecture with separation of storage and compute, automatic scaling, and support for diverse workloads.

• Snowflake's architecture separates storage and compute, allowing for independent scaling and cost optimization.

• Snowflake automatically handles infrastructure management, reducing the need for manual tuning and maintenance.

• Snowflake supports diverse workloads, including data warehousing, data lakes, and real-time analytics.

• Snowflake's unique multi-c...read more

Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity.

• Normalization involves breaking down a table into smaller tables and defining relationships between them.

• It helps in reducing data redundancy and ensures data integrity.

• Denormalized tables are preferred for read-heavy applications where performance is a priority.

• Denormalization can improve query performance by reducing the number of joins needed.

• Denormalized tables ar...read more

Star schema has a single fact table connected to multiple dimension tables, while snowflake schema has normalized dimension tables.

• Star schema denormalizes data for faster query performance.

• Snowflake schema normalizes data to reduce redundancy.

• Star schema is easier to understand and query, but snowflake schema saves storage space.

• Example: A star schema for a sales database would have a fact table for sales transactions connected to dimension tables for products, customers, an...read more

Snowflake is a cloud-based data warehousing platform with unique features like separation of storage and compute, automatic scaling, and data sharing capabilities.

• Snowflake uses a unique architecture with separate layers for storage, compute, and services, allowing for independent scaling of each component.

• It uses virtual warehouses to separate compute resources, enabling users to scale up or down based on workload requirements.

• Snowflake's data sharing feature allows users to...read more

Time Travel in Snowflake allows users to query historical data as it existed at a specific point in time.

• Time Travel in Snowflake enables users to access and query data as it existed at a specific point in the past.

• It helps in auditing, compliance, and debugging by allowing users to track changes and view historical data.

• Users can specify a timestamp or a statement ID to retrieve data from a specific point in time.

• Time Travel is useful for comparing data at different points i...read more

To set data retention time as 0 in Snowflake, you can use the ALTER TABLE command with the SET option.

• Use ALTER TABLE command with SET option to modify the data retention time to 0.

• Syntax: ALTER TABLE table_name SET DATA_RETENTION_TIME = 0;

• Example: ALTER TABLE sales SET DATA_RETENTION_TIME = 0;

Connectors in Snowflake are used to integrate with various data sources and tools for seamless data loading and querying.

• Snowflake JDBC connector for connecting to Snowflake using Java applications

• Snowflake ODBC connector for connecting to Snowflake using ODBC-compliant applications

• Snowflake Python connector for connecting to Snowflake using Python scripts

• Snowflake Spark connector for integrating Snowflake with Apache Spark for data processing

• Snowflake Kafka connector for str...read more

Migration in Snowflake involves moving data and objects from one environment to another using various methods.

• Use Snowflake's built-in tools like SnowSQL, Snowpipe, and Snowflake Data Migration Service for seamless migration

• Consider factors like data volume, complexity, and downtime requirements when planning migration

• Test the migration process thoroughly to ensure data integrity and consistency

• Monitor the migration progress and performance to identify any issues and optimize...read more

Snowpipe is configured using a Snowflake account, specifying the source data location and the target table.

• Configure a stage in Snowflake to specify the source data location.

• Create a pipe in Snowflake to define the target table and the stage.

• Set up notifications for the pipe to trigger loading data automatically.

• Monitor the pipe for any errors or issues in data loading.

• Example: CREATE STAGE my_stage URL = 's3://my_bucket/data'

• Example: CREATE PIPE my_pipe AS COPY INTO my_table...read more

Ingestion in Snowflake involves loading data into the platform for analysis and processing.

• Use Snowflake's COPY INTO command to load data from external sources like S3, Azure Blob Storage, or Google Cloud Storage.

• Consider using Snowpipe for continuous data ingestion from streaming sources.

• Utilize Snowflake's Snowpark for data ingestion and processing using programming languages like Scala or Java.

Various data modelling techniques like dimensional modelling, ER modelling, and data vault are used.

• Dimensional modelling is used for data warehousing and involves organizing data into facts and dimensions.

• ER modelling is used to visualize the data relationships in an entity-relationship diagram.

• Data vault modelling is used for agile data warehousing and involves creating a flexible and scalable data model.

Snowflake is a cloud-based data warehousing platform that offers scalability, flexibility, and security.

• Snowflake separates compute and storage, allowing for independent scaling of each

• It supports multiple data types and integrates with various data sources

• Snowflake provides automatic optimization and tuning for query performance

• It offers advanced security features such as multi-factor authentication and data encryption

Snowflake Data Engineers are responsible for designing, building, and maintaining data pipelines within the Snowflake platform.

• Designing and implementing data pipelines in Snowflake

• Optimizing data storage and retrieval processes

• Collaborating with data scientists and analysts to ensure data accuracy and integrity

Streams in Snowflake are used to continuously replicate data from a table to another destination in real-time.

• Streams capture changes made to a table, such as inserts, updates, and deletes.

• They can be used to track changes and replicate data to other tables or external systems.

• Streams are created on a specific table and can be monitored for changes using SQL commands.