Are you preparing for an interview for a Snowflake related job? If it is yes, you need to have knowledge of the subject before we attend the interview. Snowflake is a popular cloud-based data warehousing platform and it offers highly scalable and powerful analytics capabilities.
As Snowflake continues to gain popularity, it’s important to be well-prepared for your interview. To aid you with your preparation, we have enlisted some of the top Snowflake interview questions and answers that can help you showcase your skills and knowledge in Snowflake.
Whether you are applying for a Snowflake developer, data engineer, or data analyst role, these questions and answers can provide you with a solid foundation for your interview preparation.
In this blog post, we will cover some of the most commonly asked Snowflake interview questions, along with their detailed answers, to help you ace your upcoming Snowflake interview. Read on to level up your Snowflake interview preparation!
Salary of Snowflake certified candidates
Snowflake, with its prominent features such as isolation of computing and storage, data sharing, and data cleaning, has gained momentum as one of the best cloud data warehouse solutions. It provides support for various programming languages like Java, Golang, .Net, Python, and it is being used by tech industries like Adobe Systems, AWS, Informatica, Logitech, and so on.
Also Read: Which snowflake certification is best for you?
As a result, the demand for Snowflake professionals is on the rise, with the average salary for a Snowflake Data Architect in the US being around $179k per annum, according to indeed.com. If you are appearing for a Snowflake job interview, the below shown Snowflake interview questions and answers can really help you out in your preparation.
Top 25 Snowflake Interview Questions & Answers
Here are some most frequently asked scenario-based Snowflake interview questions and answers that will help you to ace the interview in top companies like Capgemini, leetcode, Accenture,etc.
1. What is Snowflake ETL?
Snowflake follows a three-step process, commonly known as ETL (Extract, Load, Copy), for data ingestion and processing. The process includes the following steps:
- Extract: In this step, data is extracted from the source system and transformed into data files in various formats such as XML, CSV, JSON, etc.
- Load: The extracted data is loaded into an external or internal stage, which could be an Amazon S3 bucket, Microsoft Azure Blob, or any Snowflake-managed location.
- Copy: In this step, the data gets copied from certain stage into database table of Snowflake with usage of “COPY INTO” command and thus it loads data into table for data processing.
This kind of three-step process in Snowflake allows can allow for efficient extraction, loading, and copying of data, and thus making it easier to ingest and process large volumes of data in wide range of formats.
2. Define the term Snowflake Architecture.
The Snowflake architecture is a hybrid approach that combines elements of both shared-disk and shared-nothing database architectures to leverage their strengths. It uses a central data repository for persistent data, which is accessible to all compute nodes like in a shared-disk architecture. At the same time, Snowflake employs massively parallel computing (MPP) clusters for query processing, where each node stores a portion of the complete data set locally, similar to a shared-nothing architecture.
The Snowflake architecture is organized into three main layers, as illustrated below:
- Database Storage Layer: After data is loaded into Snowflake, it is reorganized into a specific format, such as columnar, compressed, and optimized format. The optimized data is then stored in cloud storage.
- Query Processing Layer: In this layer, queries are executed using virtual warehouses, which are independent MPP (Massively Parallel Processing) compute clusters consisting of multiple compute nodes allocated by Snowflake from cloud providers. Virtual warehouses operate autonomously, without sharing compute resources with each other, ensuring their performance is independent of each other.
- Cloud Services Layer: This layer provides various services for administering and managing a Snowflake data cloud, including access control, authentication, metadata management, infrastructure management, query parsing, optimization, and more
3. Is Snowflake an ETL tool?
Yes, Snowflake can be considered as an ETL (Extract, Transform, and Load) tool, as it follows a three-step process which includes:
- Extract: Data is extracted from the source and saved in data files in various formats such as JSON, CSV, XML, etc.
- Transform: The extracted data can be loaded into a stage, which can be either internal or external.
- Load: The data is then copied into the Snowflake database using the COPY INTO command, which enables data ingestion into Snowflake for further processing and analysis.
This ETL process in Snowflake allows for efficient data movement, transformation, and loading into the Snowflake data cloud, enabling organizations to leverage Snowflake’s powerful analytical capabilities for data-driven insights.
4. What type of database is used in Snowflake?
Snowflake is a relational database system and it is primarily based on SQL, and it retains the data in columns. It also get compatible with tools such as Excel, Tableau, and so on. In addition, Snowflake has its own query tool that aids multi-statement transactions and integrates role-based security. These kinds of unique features can be expected in SQL-based database system.
5. What is meant by schema in Snowflake?
In Snowflake, data organization can be achieved with the help of schemas and databases. A schema can be defined as a logical grouping of database objects, such as tables, views, and so on. Snowflake’s schema feature provides some other benefits such as efficient disk space utilization and structured data management, and it can permit organized data storage within the Snowflake data cloud.
6. What is the difference between Star Schema and Snowflake Schema?
The Star and Snowflake schemas are formally termed as logical descriptions of how data can be organized in a database.
Star Schema: A star schema typically includes a single fact table that is connected to multiple dimension tables. The structure of the star schema looks like a star, with the fact table at the center and dimension tables radiating out from it. In a star schema, the dimensions are denormalized, which means that repeating values are stored within the table, resulting in denormalization.
Snowflake Schema: A snowflake schema is a variation of the star schema, where the dimension tables are normalized, meaning that they are split into smaller related tables to avoid repeating values. The snowflake schema is named so because the diagram of the schema looks like a snowflake, with the fact table in the center and the dimension tables connected in a hierarchical manner.
Both the Star and Snowflake schemas are commonly used in data warehousing and OLAP (Online Analytical Processing) environments for efficient querying and analysis of large datasets.
7. What is meant by Snowflake cloud data warehouse?
A Snowflake cloud data warehouse is defined as an analytical data warehouse that can be built with the help of a modern SQL database engine. Snowflake is mainly designed with a unique architecture for cloud computing optimization. It is delivered as a software-as-a-service (SaaS) and initially launched on Amazon Web Services (AWS) for loading and analyzing large volumes of data.
One of the standout features of Snowflake is its ability to dynamically spin up multiple virtual warehouses, allowing users to operate independent workloads on the same data without any risks or issues. This flexibility provides users with the ability to scale their analytics workloads as needed, making Snowflake a powerful and versatile solution for data warehousing in the cloud.
8. What are the prominent features of Snowflake?
Snowflake has unique features that set it apart as a modern cloud data warehouse and they are:
- XML support: Snowflake allows for handling and processing of XML data, providing versatility in data ingestion and manipulation.
- Data protection and security: Snowflake prioritizes data security with advanced encryption, access controls, and auditing features to ensure data integrity and confidentiality.
- Database and object closing: Snowflake allows for easy and efficient management of databases and objects, including the ability to close and archive them when not in use to optimize costs.
- Data sharing: Snowflake facilitates secure data sharing across different organizations, enabling seamless collaboration and insights without data movement or duplication.
- Metastore integration: Snowflake supports integration with external metastores, enabling users to leverage their existing metadata management tools and processes.
- External tables: Snowflake allows for the creation of external tables that reference data stored in external cloud storage, enabling data integration and analysis across different cloud platforms.
- Geospatial data support: Snowflake offers extensive support for geospatial data types and functions, enabling advanced spatial analytics and location-based insights.
- Result caching: Snowflake provides result caching capabilities to improve query performance and reduce query latency, enhancing overall query performance for frequently executed queries.
- Search optimization service: Snowflake offers a search optimization service that helps optimize complex and resource-intensive queries for faster and more efficient execution.
- Table streams on external and shared tables: Snowflake allows for table streams on external and shared tables, enabling real-time data streaming and processing for near real-time analytics and data integration.
9. What is meant by Snowflake caching?
Snowflake has a powerful feature that allows for caching query results to optimize query performance. When a new query is submitted, Snowflake checks if there is a matching query that has been executed previously.
If a match is found, the results are cached, and the cached result set is used instead of re-executing the query. This caching capability enables efficient query performance and allows Snowflake to be used by multiple users globally.
10. Explain zero-copy cloning in Snowflake?
Snowflake’s zero-copy cloning is a powerful feature that enables duplicating source objects without incurring additional storage costs or making physical copies. When a clone (cloned object) is created, a snapshot of the data in the source object is taken and made available to the cloned object.
Cloned objects are independent of the source object, allowing for write operations, and changes made to either object do not affect the other. The “CLONE” keyword in Snowflake allows for copying tables, schemas, and databases without actually duplicating any data, providing a highly efficient and cost-effective way to create copies of objects in Snowflake.
11. What are the benefits of using Snowflake virtual warehouse?
A virtual warehouse in Snowflake is a collection of computing resources, such as CPU, memory, and solid-state drives, that customers can utilize to run queries, load data, and perform other Data Manipulation Language (DML) and Structured Query Language (SQL) operations. It provides dedicated compute resources, including memory and temporary storage, for executing DML operations and SQL queries.
Virtual warehouses can be started and stopped as needed, allowing users to use them only when required and turn them off when not in use. Customers are charged based on the size and duration of the virtual warehouses they run.
Each virtual warehouse operates independently and does not share its compute resources with other virtual warehouses, ensuring performance isolation and independence.
12. How is the data storage carried out in Snowflake?
Once data is loaded into Snowflake, it undergoes an automatic process of reorganization into a compressed, optimized, and columnar format known as micro-partitions. These optimized data are then stored in cloud storage. Snowflake handles all aspects of data storage, including file structure, size, statistics, compression, metadata, etc., without requiring customer or user intervention.
Data objects in Snowflake are not directly visible to customers or users, and data can only be accessed through SQL queries. Snowflake uses a columnar format for data storage, where data is organized by columns rather than rows. This columnar format enables efficient analytical querying methods and enhances database performance.
13. Define Snowflake Cluster.
Snowflake employs a technique called clustering, which involves data partitioning by specifying unique cluster keys for each table. Cluster keys are subsets of columns within a table that determine how data is co-located within the table. Clustering is particularly beneficial for large tables with comprehensive data. This process of managing data clustering in a table is referred to as re-clustering.
14. What are the different ETL tools that can be used with Snowflake?
Snowflake seamlessly integrates with a variety of popular ETL (Extract, Transform, Load) tools, including but not limited to:
- Matillion
- Blendo
- Hevo Data
- StreamSets
- Etleap
- Apache Airflow
15. How does Snowflake handle concurrency and multi-user access?
Snowflake is specifically designed to support concurrent access by multiple users, utilizing a unique architecture that separates storage and computational resources. With Snowflake, multiple virtual warehouses can concurrently operate on the same data, providing individual users with a private and isolated computing environment.
Each virtual warehouse in Snowflake has its own dedicated set of resources and can be easily scaled up or down to meet the requirements of the workload, ensuring efficient resource utilization. Whenever the query gets executed, it will run on its own virtual warehouse, and the results output to the user. This kind of architecture can eliminate the requirement for control mechanisms of traditional concurrency including locks and thus allowing Snowflake to handle high volumes of users and queries with high efficiency.
16. What is the purpose of compute layer in Snowflake?
In Snowflake, the compute layer is responsible for executing data processing tasks, typically leveraging one or more clusters of compute resources. The virtual warehouses in Snowflake are responsible for fetching data from the storage layer and processing query requests.
17. List of the different types of caching in Snowflake?
Snowflake supports three types of caching:
- Query result caching: Snowflake caches the results of executed queries to improve query performance. When a new query is submitted, Snowflake checks if the same query has been executed before and if the results are available in the cache. If so, it uses the cached results instead of re-executing the query.
- Metadata cache: Snowflake caches metadata, such as table schema information, to reduce the overhead of metadata retrieval during query execution. This helps in improving query performance by reducing the amount of metadata retrieval from the underlying storage layer.
- Virtual warehouse local disk caching: Snowflake allows caching of frequently accessed data on the local disks of virtual warehouses. This enables faster data retrieval and processing within the virtual warehouse, improving query performance and reducing the need to fetch data from the storage layer.
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18.What are the different Snowflake editions?
Snowflake offers different editions tailored to meet various customer requirements. These editions include:
- Standard edition: This is Snowflake’s introductory level offering and is suitable for beginners. It provides unlimited access to standard features for users.
- Enterprise edition: The Enterprise edition includes all the features and services of the Standard edition, along with additional features designed for large-scale enterprises.
- Business-critical edition: Also known as the enterprise edition for sensitive data, the business-critical edition provides advanced data protection features to meet the needs of organizations dealing with sensitive data.
- Virtual private Snowflake: It is designed for organizations that require heightened security, particularly for financial activities. It provides enhanced security measures to safeguard sensitive data.
19. List the advantages of Snowflake compression?
Snowflake employs advanced data compression algorithms to automatically compress data upon ingestion, reducing storage costs compared to the original data size. The compressed data is stored efficiently in Snowflake, and customers are billed based on the compressed size of their data, rather than the original data size. This approach offers several advantages:
- Cost savings: Compression helps reduce storage costs as customers are billed based on the compressed size of their data, resulting in more economical cloud storage.
- On-disk cache: Snowflake’s on-disk cache does not incur additional storage costs, as it utilizes the compressed data, further optimizing storage utilization.
- Data sharing and cloning: Snowflake’s data sharing and cloning features do not result in additional storage expenses, as they leverage the compressed data, making it a cost-effective option for collaboration and replication.
By leveraging efficient data compression techniques, Snowflake provides a cost-effective and storage-efficient solution for managing and analyzing data in the cloud.
20. Define Snowpipe.
Snowpipe is a real-time data ingestion service offered by Snowflake that allows for quick loading of files within minutes after they are added to a designated stage. This service enables data to be loaded in micro-batches, allowing for faster data access with minimal response time.
Instead of manually running COPY statements on a schedule to transfer large batches of data, Snowpipe automates the process and loads data in smaller, manageable micro-batches. This approach makes it easier to analyze the data in near real-time. Snowpipe uses file names and file checksums to ensure that only new data is processed, ensuring efficient and reliable data ingestion.
21. Is snowflake OLTP or OLAP?
Snowflake is primarily designed as a database system for OLAP (Online Analytical Processing), which focuses on complex queries and analytics on large datasets. Unlike OLTP (Online Transaction Processing), which deals with real-time transactions, OLAP is used for evaluating aggregated historical data from OLTP systems.
Snowflake is optimized for fast and scalable analytics, making it well-suited for data warehousing and data analytics use cases. However, it’s worth noting that depending on specific requirements and use cases, Snowflake can also be utilized for online transaction processing (OLTP) to handle transactional workloads.
22. Which cloud platforms does Snowflake currently support?
Snowflake supports the following cloud platforms:
- Amazon Web Services (AWS)
- Microsoft Azure (Azure).
- Google Cloud Platform (GCP)
23. What is meant by Horizontal and vertical scaling?
Horizontal scaling is a technique that increases concurrency by scaling out, allowing you to add more virtual warehouses as your customer base grows. This enables you to respond quickly to additional queries by leveraging auto-scaling capabilities. It can make you to respond in a faster manner by means of adding the queries by the usage of auto-scaling capabilities.
In another hand, vertical scaling involved increased power, which includes the RAM and CPU of the existing machine. This kind of scaling can be used for the reduction of processing time. If anyone needs to optimize the workload and leverage the workload, then it is significant to select larger sized virtual warehouse in the snowflake environment.
24. What is the Snowflake Data Retention Period?
The data retention period is a crucial aspect of Snowflake Time Travel feature. When data in a table gets altered, such as through deletions or removal of objects containing data, Snowflake retains the state of the data before it was updated. The data retention period specifies the number of days for which historical data will be preserved, allowing Time Travel operations like SELECT, CREATE, CLONE, UNDROP, etc. to be performed on it.
By default, all Snowflake accounts have a data retention period of 1 day for standard editions. For enterprise editions and higher accounts, the data retention period can range from 0 to 90 days.
25. What is fail-safe in Snowflake?
Snowflake provides a default Time Travel data retention period of 7 days, during which historical data can be retrieved as a fail-safe feature. Once the Time Travel data retention period expires, the fail-safe default period begins, during which data recovery may be performed as a last resort after all other recovery options have been exhausted.
Data recovery through fail-safe is a best-effort process and may take several hours to several days to complete. Snowflake may use this fail-safe feature to recover data that has been lost or damaged due to extreme operational failures, ensuring data durability and reliability in case of unforeseen events.
26. What are the views of a snowflake?
When presenting certain columns and rows in one or more tables, views were helpful. With the use of a view, it is possible to access a query’s results as if they were tables. The query is specified in the CREATE VIEW statement. Two distinct interpretations are supported by Snowflake:
Non-materialized views: The outcomes of a non-materialized view are retrieved by running the query at the time when the view is identified in the query. Performance is slower when compared with materialized views.
Materialized views: Despite having the name of a sort of view, a materialized view functions more frequently like a table.
27. Does Snowflake use Indexes?
No, Snowflake does not have indexes. It is a major aspect that makes the Snowflake scale more for the queries.
Conclusion
I hope the above frequently asked Snowflake interview questions will help you in cracking the interview. Preparing for a Snowflake interview requires a solid understanding of its key concepts and features, as well as practical experience in using Snowflake for data warehousing and data analytics.
With thorough preparation and practice, you can confidently tackle Snowflake interview questions and increase your chances of success in landing your dream job.
If you need any clarifications on above blog post, please ping us!
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