Snowflake Interview Questions

Top Snowflake Interview Questions One Should Know

Snowflake is a cloud-based data warehousing platform that disrupted the data warehouse industry with its modern features and cost-effectiveness. It has gained huge momentum in the cloud data warehouse industry because of its out-of-box features like separation of storage and computing, scalable computation, data cloning, data sharing, easy integration with third-party tools, and much more.

Snowflake is one of the widely deployed data warehouse platforms by all types of organizations to fulfil their modern data warehouse requirements. As corporations around the globe are moving into a Snowflake, the demand for snowflake professionals is also spiking day by day.

Have you come here for the frequently asked Snowflake interview questions and answers? Well, then you are at the right place! We have gathered a list of top Snowflake database interview questions from industry experts. Mastering these questions would surely help you with the right knowledge to crack any Snowflake interview. All the very best.

Frequently Asked Snowflake Interview Questions and Answers in 2021

1) What is Snowflake?

Snowflake is a cloud data warehouse provided as a software-as-a-service (SaaS). It consists of unique architecture to handle multiple aspects of data and analytics. Snowflake sets itself apart from all other traditional data warehouse solutions with advanced capabilities like improved performance, simplicity, high concurrency, and cost-effectiveness.

Snowflake’s shared data architecture physically separates the computation and storage which is not possible by the traditional offerings. It streamlines the process for businesses to store and analyze massive volumes of data using cloud-based tools. Snowflake has transformed the data warehouse industry by making it possible to bring all data together into a centralized system.

2) What is unique about Snowflake Architecture?

Snowflake has come up with an advanced and unique architecture that is a combination of shared-disk and shared-nothing architectures. It uses a central data repository to store data consistently and makes it available to access from all compute nodes in the platform. Similar to shared-nothing architecture, Snowflake also executes queries by using MPP (massively parallel processing) compute clusters where every node in the cluster stores a certain amount of the whole data set locally.

This architecture simplifies data management with shared-disk architecture and adds performance and scalability advantages with shared-nothing architecture. Snowflake unique architecture consists of three layers which are database storage, Query processing, and Cloud services.

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3) Explain the Database storage layer in Snowflake?

Whenever the data gets loaded into Snowflake it arranges the data into optimized, compressed, and columnar formats. After this process, the data gets optimized and then stored in cloud storage.

Snowflake looks after how the data gets stored which includes data organization, compression, structure, file size, statistics, metadata, and many other aspects related to data storage. All the data objects stored in Snowflake are invisible and inaccessible. One can only access the data objects by running SQL query operations using Snowflake.

4) What is the Query Processing layer in Snowflake architecture?

All the query executions are performed in this processing layer. Snowflake uses “virtual warehouses to process queries. Each virtual warehouse is an MPP (massively parallel processing) compute cluster which consists of multiple nodes allotted by Snowflake from a cloud provider.

Each virtual warehouse in the query processing layer is independent and does not share its computational resources with any other virtual warehouses. This makes each virtual warehouse independent and shows no impact on the other virtual warehouses in case of any failover.

5) What is the Cloud Services layer in Snowflake architecture?

The Cloud Services layer consists of a set of services that coordinates multiple tasks across the Snowflake platform. All these services tie together and work with great coordination to process user requests, from login to query dispatch. This layer also executes compute instances assigned by Snowflake from the cloud manager.

Following are the various services managed under this layer:

  • Authentication
  • Metadata management
  • Infrastructure management
  • Access control
  • Optimization and query parsing

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6) How can we access the Snowflake data warehouse?

Following are the typical ways one can use to access:

  • Web User Interface
  • JDBC Drivers
  • ODBC Drivers
  • Python Libraries
  • SnowSQL Command-line Client

7) What are the advantages of a Snowflake database?

Snowflake is natively built for the cloud and addresses many issues that are not solved by traditional warehouse systems. Following are the five core advantages that we gain by using the Snowflake data platform:

  • High-speed performance
  • Supports both structured and unstructured data
  • Concurrency and accessible
  • Seamless data sharing
  • High availability
  • High security

8) What is a columnar database? 

A columnar database is quite opposite of traditional databases. It stores the data by columns instead of rows, which simplifies the way for analytical query processing and provides higher performance for the databases. The Columnar database simplifies the analytics processes and is often called the future of business intelligence.

The following image shows how exactly the Columnar database stores data:

Columnar Database Stores

9) How data is secured in Snowflake?

Data security is the highest priority for all organizations. Snowflake follows industry-leading security standards to encrypt and secure the customer accounts and data stored in Snowflake.

It offers best-in-class key management features at no additional cost.

The following are the security measures used by Snowflake to protect customer data:

  • Snowflake uses a managed key to automatically encrypt the data entered into it.
  • Snowflake uses TLS to protect communication between clients and servers.
  • It allows you to select a geographical location to store your data based on your cloud region.

10) Explain the data compression in Snowflake?

All the data entered into Snowflake gets compressed automatically. Snowflake uses advanced data compression algorithms to compress and store data. The customers need to pay for the compressed data, not for the actual data.

11) Name a few advantages that arise out of data compression in Snowflake?

The following are the advantages of Data compression:

  • Lowers storage costs
  • Less disk space
  • Near zero storage overhead for data sharing or data cloning
  • Byte order-independent

12) What is Snowflake Caching?

Snowflake stores data cache in SSD in addition to a result cache to improve SQL query performance. It catches the result of every query that you run and wherever a new query is submitted it checks with the previously executed queries to find if any match. If any matching query exists then it uses a cached result set instead of executing the query. This process brings down the time taken for the queries and retrieves results directly from the cache.

Following are the different cache layers in Snowflake:

  • Result Cache
  • Local Disk Cache
  • Remote Disk Cache

13) Name the types of catches in Snowflake? 

  • Query Results Caching
  • Metadata Cache
  • Virtual Warehouse Local Disk Caching

14) What is Snowflake Time Travel?

Snowflake Time Travel tool enables you to access historical data at any given point within a defined time period. Using this you can see the data that has been deleted or changed. Using this tool you can perform the below tasks:

  • Restore data-related objects (Schemas, tables, and databases) that might have been lost accidentally.
  • To examine data usage and changes made to data within a time period
  • Backing up and duplicating data from key points in the past.

15) What is Fail-safe in Snowflake?

Fail-safe is an advanced feature available in Snowflake to ensure data protection. This plays an important role in Snowflake’s data protection lifecycle. Fail-safe offers 7 days extra storage even after the time travel period is over.

16) Why fail-safe instead of Backup?

To minimize the risk factor, DBAs traditionally execute full and incremental data backups at regular intervals. This process occupies more storage space, sometimes it may be double or triple. Moreover, the data recovery process is costly, takes time, requires business downtime, and more.

Snowflake comes with a multi-datacenter, redundant architecture that has the capability to minimize the need for traditional data backup. Fail-safe features in Snowflake are an efficient and cost-effective way that substitutes the traditional data backup and eliminates the risks and scales along with your data.

17) What is the Data retention period in Snowflake?

Data retention is one of the key components of Snowflake and the default data retention period for all Snowflake accounts is 1 day (24 hours). This is a default feature and is applicable to all Snowflake accounts.

18) Explain data shares in Snowflake?

The data shares option in Snowflake allows the users to share the data objects in a database in their account with other Snowflake accounts in a secure way. All the database objects shared between Snowflake accounts are only readable and one can not make any changes to them.

Following are the sharable database objects in Snowflake:

  • Tables
  • Secure views
  • External tables
  • Secure UDFs
  • Secure materialized views

19) What are the data-sharing types in Snowflake?

Following are the 3 types of data sharing types:

  • Sharing Data between functional units. 
  • Sharing data between management units.
  • Sharing data between geographically dispersed locations

20) What do you know about zero-copy cloning in Snowflake?

Zero copy cloning is a snowflake implementation that allows you to create a copy of your schemas, tables, and databases without copying the actual data. In order to perform zero-copy in Snowflake, you need to use a keyword called CLONE. With this option, you can get real-time data from production and perform multiple actions.

21) Name the cloud platforms supported by Snowflake?

The following are the cloud providers supported by Snowflake:

  • Google Cloud Platform (GCP)
  • Amazon Web Services (AWS)
  • Microsoft Azure (Azure)
22) What are the different Snowflake editions?

Following are the various Snowflake editions available:

  • Standard Edition
  • Enterprise Edition
  • Business Critical Edition
  • Virtual Private Snowflake (VPS) Edition.
23) What are the different Connectors and Drivers available in Snowflake?

Below mentioned are the various connectors and drivers available in Snowflake:

  • Snowflake Connector for Python
  • Snowflake Connector for Kafka
  • Snowflake Connector for Spark
  • Go Snowflake Driver
  • Node.js Driver
  • JDBC Driver
  • .NET Driver
  • ODBC Driver
  • PHP PDO Driver for Snowflake
24) What is “Stage” in Snowflake?

A stage in Snowflake is defined as an intermediate area used to upload files. Snowpipe Identifies the files as soon as they enter the staging area and automatically loads them into a snowflake.

Following are the three different stages supported by Snowflake:

  • User Stage
  • Table Stage
  • Internal Named Stage
25) What is Snowpipe in Snowflake?

Snowpipe is a continuous, and cost-effective service used to load data into Snowflake. The Snowpipe automatically loads the data from files once they are available on stage. This process simplifies the data loading process by loading data in micro-batches and making data ready for analysis.

26) What are the benefits of using a Snowpipe:

The following are the major advantages of using a Snowpipe:

  • Real-time insights
  • Ease of use
  • Cost-effective
  • Flexibility
  • Zero Management
27) What is a virtual warehouse in Snowflake?

A Virtual warehouse in Snowflake is defined as one or more compute clusters supporting users to perform operations like data loading, queries, and many other DML operations. Virtual warehouses support users with the required resources such as CPU, temporary storage, memory, etc, to perform different Snowflake operations.

28) Explain the features of Snowflake?

Following are some of the notable features of Snowflake:

  • Database Storage
  • Cloud Services
  • Compute layer
  • Concurrency and Accessibility
  • Supports structured and unstructured data
  • Easy data sharing
  • High-speed performance
  • Availability and Security 
29) What are the programming languages supported by Snowflake?

Snowflake supports different programming languages like Go, Java, .NET, Python, C, Node.js, etc.

30) What are micro partitions in Snowflake?

Snowflake comes with a unique and powerful form of data partitioning called micro-partitioning. Data residing in all snowflake tables is automatically converted into micro partitions. In general Micro partitioning is performed on all Snowflake tables.

31) What is Clustering in Snowflake?

Clustering in Snowflake is defined as grouping a bunch of values into a record or file to enhance query performance. 

32) What is a Clustering key?

The clustering key in Snowflake is a subset of columns in a table that helps us co-locate data within the table. It is best suitable for situations where tables are extensive; the order was not perfect due to DML.

33) What is Amazon S3?

Amazon S3 is a storage service that offers high data availability and security. It provides a streamlined process for organizations of all sizes and industries to store their data. 

34) What is a Snowflake Schema?

The Snowflake schema is defined as a logical representation of tables in a multidimensional database. A fact table represents it in the middle with diversified connected dimensions. Snowflake schema’s primary goal is to normalize data.

35) What are the advantages of the Snowflake Schema?

The following are the core advantages of the Snowflake Schema:

  • Uses less disk space
  • Minimal data redundancy.
  • Eliminates data integration challenges
  • Less maintenance
  • Executes complex queries
  • Supports many-to-many relationships
36) What is a Materialized view in Snowflake?

A materialized view in Snowflake is a pre-computed data set derived from a query specification. As the data is pre-computed, it becomes far easier to query materialized view than a non-materialized view from the view’s base table.

In simple words, materialized views are designed to enhance the query performance for common and repetitive query patterns. Materialized Views are primary database objects and speedup projection, expensive aggregation, and selection operations for queries that run on larger data sets.

37) What are the advantages of Materialized Views?

The following are the distinct advantages of Materialized Views:

  • Improves query performance
  • Snowflake automatically manages materialized Views.
  • Materialized views provide updated data.
38) What is the use of SQL in Snowflake?

SQL stands for Structured Query Language and is the common language used for data communication. Within SQL, common operators are clubbed into DML (Data Manipulation Language) & DDL (Data Definition Language) to perform various statements such as  SELECT, UPDATE, INSERT, CREATE, ALTER, DROP, etc.

Snowflake is a data warehouse platform and supports the standard version of SQL. Using SQL in Snowflake, we can perform the typical data warehousing operations like create, insert, alter, update, delete, etc.

39) What are the ETL tools supported by Snowflake?

Following are the top ETL tools supported by Snowflake.

  • Matillion
  • Infromatica
  • Tableau
  • Talend, etc.
40) Where does the metadata gets stored in Snowflake?

In Snowflake, the Metadata is stored in virtual columns that can be easily queried using the SELECT statement and loaded into a table using the COPY INTO command. 

41) What is Auto-scaling in Snowflake?

Autoscaling is an advanced feature in Snowflake that starts and stops clusters based on the requirement to support workloads in the warehouse.

42) What is the use of Stored Procedures in Snowflake?

A stored procedure is a group of database statements that can be written using SQL  JavaScript.

43) How does Snowflake handle scalability?

Snowflake’s architecture enables automatic and elastic scaling of compute resources (virtual warehouses) and storage independently. Users can scale up or down the virtual warehouses in real-time to accommodate changing workloads, and storage scales automatically as data is added or removed.

44) What are the main data types supported by Snowflake?

Snowflake supports a wide range of data types, including numeric (INTEGER, FLOAT, DECIMAL), string (VARCHAR, CHAR), date and time (DATE, TIME, TIMESTAMP), semi-structured (VARIANT, OBJECT, ARRAY), and others like BOOLEAN, BINARY, and GEOGRAPHY.

45) Can you explain the difference between a transient and a permanent table in Snowflake?

A permanent table in Snowflake retains the data and metadata indefinitely, while a transient table retains the data but not the metadata. Transient tables are useful for temporary storage or staging purposes and can help reduce storage costs since the metadata is not preserved.

46) How does Snowflake support data ingestion?

Snowflake supports data ingestion through various methods, such as batch loading using COPY commands, using Snowpipe for continuous and near-real-time data ingestion, and integrating with third-party ETL/ELT tools like Fivetran, Matillion, and Talend.

47) Can you describe your experience with Snowflake’s performance tuning and optimization?

Yes, I have experience identifying and addressing performance bottlenecks, such as inefficient queries and poorly optimized virtual warehouses.

48) How do you approach creating and managing database objects in Snowflake?

This involves creating tables, views, and stored procedures, as well as managing their metadata and dependencies.

49) Can you describe your experience with Snowflake’s data ingestion and integration capabilities?

Yes, I have experience using Snowflake’s built-in data ingestion tools, as well as integrating with external ETL tools like Apache NiFi and Talend.

50) How do you approach ensuring data privacy and compliance in Snowflake?

This involves implementing data masking, anonymization, and other techniques to protect sensitive data, as well as adhering to regulatory requirements like GDPR and HIPAA.

51) Can you describe your experience with Snowflake’s data modeling tools?

Yes, I have experience using Snowflake’s Schema Designer and other tools to create and maintain data models.

52) How do you approach managing and optimizing Snowflake’s storage costs?

This involves understanding Snowflake’s pricing model, implementing efficient storage and compression techniques, and monitoring usage to identify cost-saving opportunities.

53) Can you describe your experience with Snowflake’s cloud provider integrations?

Yes, I have experience integrating Snowflake with cloud providers like AWS, Azure, and Google Cloud Platform, and using their services to enhance Snowflake’s capabilities.

54) How do you approach debugging Snowflake queries and resolving errors?

This involves reviewing error messages, using query profiling and monitoring tools, and consulting documentation and support resources.

55) Can you describe your experience with Snowflake’s data governance and security features?

 Yes, I have experience implementing Snowflake’s access controls, encryption, and auditing features, as well as creating data governance policies and procedures.

56) How do you approach designing Snowflake’s data pipelines?

This involves defining data ingestion sources and destinations, selecting appropriate data integration tools, and configuring the pipeline for optimal performance.

57) Can you describe your experience with Snowflake’s multi-cluster architecture?

Yes, I have experience using Snowflake’s multi-cluster architecture to optimize query performance and manage to compute resources.

58) How do you approach managing Snowflake’s data warehouses and virtual warehouses?

This involves monitoring usage and performance, adjusting resources as needed, and configuring automated scaling and suspension policies.

59) Can you describe your experience with Snowflake’s support for semi-structured data?

Yes, I have experience using Snowflake’s support for JSON, Avro, and other semi-structured data formats, and querying and transforming this data using SQL.

60) How do you approach creating Snowflake’s dashboards and visualizations?

This involves selecting appropriate visualization tools, defining metrics and KPIs, and creating dashboards and reports that provide actionable insights.

61) Can you describe your experience with Snowflake’s integration with BI and analytics tools?

Yes, I have experience integrating Snowflake with tools like Tableau, Looker, and Power BI, and using them to create compelling data visualizations and reports.

62) How do you approach managing and organizing Snowflake’s databases and objects?

This involves creating and managing schemas, organizing database objects into logical groups, and ensuring that database objects are properly documented.

63) Can you describe your experience with Snowflake’s data warehousing best practices?

Yes, I have experience implementing best practices like dimensionality modeling, data partitioning, and query optimization to ensure optimal performance and scalability.

64) How do you approach implementing Snowflake’s data-sharing capabilities?

This involves creating secure data shares and defining access.


Hope you have enjoyed reading these Snowflake database interview questions and answers. These are some of the commonly asked questions in any snowflake interview, preparing these questions will help you clear any Snowflake interview. We are going to add more Scenario-based snowflake computing interview questions in the near future, so stay tuned to this blog for the latest questions. Happy reading!

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