Top 60 Snowflake Interview Questions for 2025

As per Statista, “The Snowflake company reported revenue of over 2.8 billion U.S. dollars in its financial year ending January 31, 2024, from almost 2.1 billion the previous year.”According to Glassdoor, the average Snowflake professional salary ranges between ₹ 4.0 lakhs to ₹31.0 lakhs per annum.  According to Naukri.com, there are about 34912+ jobs available for Snowflake professionals in India.

Hence, we’ve prepared a list of the most common Snowflake interview questions and answers to help you crack the interview.

Table of Contents

Most Frequently Asked Snowflake Interview Questions

1. What is Snowpipe, and how does it work?
2. What is the difference between Transient and Temporary tables in Snowflake?
3. What are the key structural and syntax differences between a Star Schema and a Snowflake Schema in data modeling?
4. Compare Snowflake and Redshift.
5. How does Time Travel help with data recovery in Snowflake?
6. What is automatic clustering in Snowflake?
7. How does Snowflake ensure data consistency in a distributed environment?
8. How can you access the Snowflake Cloud data warehouse?
9. What kind of SQL does Snowflake use?
10. What is the use of Snowflake Connectors?

Interview Questions for Freshers

1. What is Snowflake? How is it different from other data warehousing solutions?

Snowflake is a cloud-based, analytical data warehousing service for storing data and processing and analyzing data. Users can store their data and scale their resources to process independently. It is highly cost-effective with high performance.

Other warehousing solutions include Teradata and Oracle Exadata; they have an architecture monolithic type, where computers, storage, and services are integrated; even though, one of those needs to scale, it’s got to scale everything. Efficiency will be encountered. Snowflake is a multi-clustered architecture data: the storage layer is totally separated from the compute layer. Because of this both of them do independent scaling. The second main difference is that Snowflake accepts semi-structured and structured data types without much preprocessing. Other data warehousing solutions mainly allow only structured data types.

2. Describe Snowflake’s architecture.

The architecture is cloud-designed and has features like multi-cluster, shared data architecture, and massive storage capabilities. The three layers of Snowflake architecture are:

• Storage Layer: In this layer, structured and semi-structured data is stored and automatically compressed and encrypted in columnar format to reduce the data read from disk, improving the query performance.
• Compute Layer: This is also known as virtual warehouses. This layer consists of one or more computer clusters responsible for performing all the data processing tasks, where the complex tasks are broken down into more straightforward tasks and run simultaneously in multiple clusters, increasing the speed of query processing. The virtual warehouses are not interlinked, ensuring the workloads do not affect others.
• Cloud Services Layer: This layer has a range of services like data management and query optimizer, which uses techniques like result caching, where it remembers the results of the previous queries so that it can provide the same result if you ask the same question, metadata manager where it keeps tracks of information about the data, and maintains security by encryption and giving access controls. These services synchronize the communications between the user and the system to ensure a fully managed service.

3. What are the features of Snowflake?

Some of the features of the Snowflake data warehouse are:

• Database and Object Closing: Allows fast and cost-effective duplication of databases, schemas, or tables without replicating physical data.
• Support for XML: Gives built-in support for storing, querying, and processing XML data.
• External tables: Allows querying data stored outside Snowflake, such as in cloud storage, without loading it into Snowflake.
• Hive metastore integration: Helps in the seamless integration with existing Hive metadata for better data access.
• It supports geospatial data and has functions for storing, querying, and analyzing spatial data types.
• Security and data protection: Provides strong encryption, access control, and compliance features for data protection.
• Data sharing: Helps in the secure sharing of live data in real time with other Snowflake users or external organizations.
• Search optimization service: It improves performance for search-heavy queries by optimizing table structures and indexes.
• Table streams on external and shared tables: Tracks changes on external and shared tables for optimized data synchronization.
• Result Caching: Caches query results to improve performance for redundant queries.

4. What is a Snowflake virtual warehouse, and why is it important?

A virtual warehouse, which is often simply referred to as a “warehouse,” in Snowflake is actually a group of computational resources- CPU, memory, solid-state drive, and so on and can be scaled up or down based on the need. This virtual warehouse contains at least one or more computing nodes needed to perform a variety of SQL queries and data operations.

A Snowflake virtual warehouse is essential because it:

1. Scalability: The warehouse size can be adjusted by the users based on their workload to ensure the best performance of small and large queries.
2. Concurrent query management: Multiple virtual warehouses running in parallel do not interfere with each other’s execution.
3. Cost Control: The warehouse can be stopped and resumed according to the need to save costs.
4. Workload Isolation: Different workloads, like data loading and data transformations, can be isolated across different virtual warehouses to avoid resource contention where different processes compete for the same resource, causing degradation in performance, and keeping the performance up.
5. Auto-scales to handle spikes efficiently.

5. How does Snowflake handle data storage and compression?

Snowflake uses micro-partitioning to handle data storage. In this, the data is divided into smaller units, and compute and storage are divided, and this division optimizes performance and efficiency. Snowflake uses a columnar storage format that helps to improve query performance. This means reading only those columns required, not the whole row, hence reducing the scanned data amount for queries and making retrieval times much better.

Snowflake handles the compression of data using various algorithms based on types of data. It automatically compresses the data when loaded into Snowflake. This compressing reduces the space required for storage, and the speed of data retrieval is also accelerated by reducing the amount of data to be read.

6. What is Snowpipe, and how does it work?

Snowpipe is a feature of Snowflake, which can quickly and automatically ingest data from any external sources like cloud storage into Snowflake tables. It thus simplifies the process of loading data without requiring human intervention for data ingestion in real-time or near real-time analysis in Snowflake tables.

Let us now understand how Snowpipe works:

It stores data files using external stages like cloud storage (AWS S3, Azure Blob Storage, or Google Cloud Storage). Snowpipe continuously monitors the external stage for data files and automatically populates them into suitable Snowflake tables once detected. Then it automatically pre-processes the data and loads the same to Snowflake tables. Then, the Snowflake user can manage Snowpipe pipelines through the user interface or via running SQL queries.

7. What are the stages in Snowflake?

In Snowflake, stages refer to storage locations for data. Snowflake allows two types of staging data files, and these are differentiated based on where the stored data resides. If it is located outside of a cloud system, like AWS, Azure, or GCP, after importing into Snowflake, then this is called an external stage. If it resides in a Snowflake environment that then temporarily stores it before the final load into Snowflake tables, it will be an internal stage.

8. What is Snowflake Fail-Safe?

Fail-Safe is an essential feature that enhances data protection and recovery. It provides a 7-day recovery period after the standard data recovery period(also known as Time Travel, which is mainly 90 days). However, data is not directly accessible to users; it can be recovered only by contacting Snowflake support.

9. How does Snowflake ensure data security?

Data-sharing capabilities by Snowflake make it possible to have secure and regulated real-time sharing of data across many different accounts without the original data being copied or moved. This is beneficial to companies collaborating with clients or external partners with strict regulatory and security needs. There are options for sharing whole databases and schemas or distinct tables and views.

10. What is data sharing in Snowflake?

Snowflake Data sharing allows organizations to share their data securely without moving it around or creating copies of it. It also allows real-time access, where if the organization makes any changes, it is instantly reflected on the consumer. It also enables the sharing of different types of data like view, table, and secure views.

11. What types of caches does Snowflake use, and how do they affect performance?

In Snowflake, there are three primary types of caches that enhance query performance:

• Query Result Cache
• Metadata Cache
• Virtual Warehouse Cache (or Local Disk Cache)

These caches are pre-enabled in your Snowflake environment and ready to optimize your data processing tasks.

12. How does Snowflake handle semi-structured data like JSON?

Snowflake handles semi-structured JSON data as follows:

1. Semi-structured data can be loaded into relational tables without a schema definition.
2. JSON data is loaded directly into the table in a columnar format within the VARIANT
3. Data is queried using SQL SELECT statements that refer to JSON elements by their paths.

13. What are the different Snowflake editions?

Snowflake offers four different editions, each providing progressively more features:

1. Standard Edition:
   A beginner-level course that offers unlimited access to essential features is suitable for small- to medium-sized businesses.
2. Enterprise Edition:
   Built on the Standard Edition, it adds better performance, scalability, and security features like multi-factor authentication and data encryption.
3. Business Critical Edition:
   Designed for organizations with strict security and compliance requirements, it includes all Enterprise features and advanced data governance with end-to-end encryption.
4. Virtual Private Snowflake (VPS):
   The highest-tier edition provides a fully managed, dedicated environment with complete network isolation and adjustable configurations.

14. What is the difference between Transient and Temporary tables in Snowflake?

15. What are the key structural and syntax differences between a Star Schema and a Snowflake Schema in data modeling?

The Star Schema and Snowflake Schema have different syntaxes in terms of structure and relationships between tables:

1. Star Schema Syntax:

• Simple structure with a single fact table surrounded by dimension tables.
• Each dimension table is directly connected to the fact table.
• No normalization or minimal normalization (denormalized).
• All dimensions are primarily depicted in one table (wide dimensions).

Example:

-- Fact Table (Stores student data and metrics, with foreign keys to dimension tables)
CREATE TABLE FactStudent (
    Student_ID INT NOT NULL,
    Teacher_ID INT NOT NULL,
    School_ID INT NOT NULL,
    Time_ID INT NOT NULL,
    Customer_ID INT NOT NULL,
    Amount DECIMAL(10, 2),
    PRIMARY KEY (Student_ID),
    FOREIGN KEY (Student_ID) REFERENCES DimStudent(Student_ID),
    FOREIGN KEY (Teacher_ID) REFERENCES DimTeacher(Teacher_ID),
    FOREIGN KEY (School_ID) REFERENCES DimSchool(School_ID),
    FOREIGN KEY (Time_ID) REFERENCES DimTime(Time_ID),
    FOREIGN KEY (Customer_ID) REFERENCES DimCustomer(Customer_ID)
);

-- Dimension Table: Student
CREATE TABLE DimStudent (
    Student_ID INT PRIMARY KEY,
    Student_Name VARCHAR(100),
    Class VARCHAR(50)
);

-- Dimension Table: Teacher
CREATE TABLE DimTeacher (
    Teacher_ID INT PRIMARY KEY,
    Teacher_Name VARCHAR(100),
    City VARCHAR(50)
);

-- Dimension Table: School
CREATE TABLE DimSchool (
    School_ID INT PRIMARY KEY,
    School_Name VARCHAR(100),
    Location VARCHAR(100)
);

-- Dimension Table: Time
CREATE TABLE DimTime (
    Time_ID INT PRIMARY KEY,
    Date DATE NOT NULL,
    Year INT NOT NULL,
    Month INT NOT NULL,
    Day INT NOT NULL
);

-- Dimension Table: Customer (Assuming CustomerID is referring to a customer dimension)
CREATE TABLE DimCustomer (
    Customer_ID INT PRIMARY KEY,
    Customer_Name VARCHAR(100),
    Customer_Type VARCHAR(50)
);

Characteristics:

• Dimension tables are denormalized, making queries faster; therefore, no joins across multiple-dimension tables are needed.
• Simple and easy to understand.

2. Snowflake Schema Syntax:

• The normalized structure is where dimension tables are split into additional tables.
• Some dimensions are normalized into related tables.
• More relationships (joins) between tables compared to the star schema.

Example:

-- Fact Table (Stores student's data and metrics)
CREATE TABLE FactSales (
    StudentID INT PRIMARY KEY,
    ClassID INT,
    MentorID INT,
    BooksID INT,
    TimeID INT,
    Amount DECIMAL(10, 2)
);

-- Dimension Tables (Normalized structure)
create table DimStudent (
    StudentID INT PRIMARY KEY,
    StudentName VARCHAR(100),
    CategoryID INT
);

CREATE TABLE DimCategory (
    CategoryID INT PRIMARY KEY,
    CategoryName VARCHAR(50)
);

Create table DimTeacher (
    TeacherID INT PRIMARY KEY,
    TeacherName VARCHAR(100),
    CityID INT
);

CREATE TABLE DimCity (
    CityID INT PRIMARY KEY,
    CityName VARCHAR(50),
    State VARCHAR(50)
);

CREATE TABLE DimSchool (
    SchoolID INT PRIMARY KEY,
    SchoolName VARCHAR(100),
    LocationID INT
);

CREATE TABLE DimLocation (
    LocationID INT PRIMARY KEY,
    Region VARCHAR(50)
);

CREATE TABLE DimTime (
    TimeID INT PRIMARY KEY,
    Date DATE,
    Year INT,
    Month INT,
    Day INT
);

Characteristics:

• Dimension tables are normalized, reducing redundancy and making updates easier.
• Queries require more joins, leading to slower performance in large datasets.

Critical Differences in Syntax:

• Star Schema: Dimension tables are flat and not normalized.
• Snowflake Schema: Dimension tables are normalized and are divided into multiple related tables.

16. What are the types of data sharing in Snowflake?

You can share data in Snowflake using one of the following options:

1. Listing: It offers to create a share and provides additional metadata as a product to one or more than one account.
2. Direct Share: This option allows one to directly share some important database objects with another account in the same region.
3. Data Exchange: One can set up and manage a group of accounts and offer some share to that group.

Intermediate Snowflake Interview Questions

17. What factors should be considered when selecting a Snowflake region or cloud platform?

When choosing a Snowflake cloud platform, the most crucial features are the data residency and regulatory standards it follows, how close it is to end-users, latency involved, integration of existing cloud services, cost-related factors, and disaster recovery capabilities. Additionally, one should look into the possibility of availability for some features or cloud services, as well as the ability to scale up or down according to requirements.

18. How does Snowflake handle data governance?

Snowflake Data Governance gives organizations the appropriate permissions and practices for ensuring secure collaboration in Snowflake’s Data Cloud. Organizations can use this tool to describe and implement access controls, secure data sharing, detailed security controls, data usage monitoring, and inclusion into the organization’s rules governing data management.

19. Is snowflake OLTP (Online Transactional Processing) or OLAP (Online Analytical Processing)?

An OLTP (Online Transactional Processing) database has detailed real-time data with several small transactions. OLAP, in turn, involves complex queries with few transactions. Snowflake uses online analytical processing (OLAP) as a primary component of its database schema.

20. How would you characterize the database type of Snowflake?

Snowflake is a full SQL database, built exclusively on a columnar-stored relational model. It supports such tools as Excel and Tableau. It allows the use of query tools and multiple-statement transactions, but the most vital aspect is security by role: essential attributes in any SQL database.

21. What is a micro-partition in Snowflake?

Micro partitioning is data partitioning in Snowflake, stored in continuous units. These micro-partitions contain 50 to 500 MB of uncompressed data arranged in a columnar manner in the Snowflake tables. This method helps arrange data better.

22. Explain Snowflake’s Time Travel feature.

The Snowflake Time Travel feature allows access to the historical data within a specific retention period, even if it has been updated or deleted. This tool helps to perform the following tasks:

• Data Restoration: It allows the retrieval of data-related objects that might have been deleted by mistake.
• Data Analysis: It gives the user access to check the usage of data patterns and changes made to the data over time.
• Data Duplication and Backup: It also allows data duplication and backup from any historical point, giving a comprehensive data history.

23. What is zero-copy cloning in Snowflake?

Zero copy cloning is an implementation in which the keyword CLONE allows users to create a copy of tables, schemas, or databases. This approach provides instant access to real-time production data copied into your development and staging environments for various testing or analytical activities.

24. How does Snowflake support ETL and ELT processes?

Due to their architecture and capabilities, the Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) processes are used in the Snowflake. The platform has a wide range of data incorporation and transformation techniques required for users, allowing companies to optimize data processing effectively.

In ETL, data is extracted from various sources and changed into the user’s required format before loading into the data warehouse. Using Snowflake’s SQL engine, complex changes can be made to the loaded data using SQL queries.

In ELT, the data is loaded into the warehouse in its raw form, and changes are made. Snowflake has the feature of loading the raw data into the data warehouse by separating its computing and storage abilities.

25. Compare Snowflake and Redshift.

Below is the comparison between Snowflake and Redshift:

26. How does Time Travel help with data recovery in Snowflake?

In Snowflake, Time Travel helps with data recovery that allows you to:

1. Access historical data to undo any mistakes, for example, if any updations or deletions were made by mistake.
2. Recover dropped tables or objects within a period.

27. What are Materialised Views in Snowflake, and how do they differ from standard views?

Materialised Views in Snowflake allow the users to compute first and then store the results of complex queries, thus improving the performance of the queries for the continuously accessed data.

In contrast, standard views are executed dynamically each time they are queried. Performance is much slower than materialized views, as standard views provide a virtual representation of data, whereas materialized views store actual data, allowing faster access to data.

28. How does Snowflake handle indexing and data partitioning?

Snowflake uses indexing to enhance the query performance. It creates and manages indexes in the backend to accelerate the data retrieval process.

Snowflake uses data partitioning to optimize query performance with respect to size and structure. Data is partitioned based on a certain criterion for optimal data organization for efficient execution of queries, and metadata creation for micro-partition.

29. What strategies can be used to manage compute costs in Snowflake?

To manage compute costs in Snowflake:

1. Auto-Stop/Continue: Stop and restart warehouses automatically when not in use or in use.
2. Perfect-Sized Warehouses: For opting the proper warehouse size for your workload.
3. Query Improvement: Writing proper queries to decrease the compute usage.
4. Scaling Options usage: Resort to multi-cluster warehouses or scaling options only when really needed.
5. Monitor Options: Regularly check warehouse activities and adjust configurations to optimize the cost.

30. What are Streams and Tasks in Snowflake?

Streams in Snowflake use the data manipulation language (DML) to make changes to tables using INSERTs, UPDATEs, and DELETEs commands. This allows users to check for changes made to data.

Tasks in Snowflake give users access to schedule automated SQL statements, primarily by using the changes made by streams. Streams and Tasks can automate and synchronize the increase of data processing workflows, like loading data into the main tables.

Snowflake Architect interview questions

31. How does Snowflake integrate with external data sources and ETL tools?

Snowflake’s Data Exchange has removed the long ETL and FTP process and uses extensive data integration tools like leading vendors such as Informatica, SnapLogic, Stitch, Talend, and others.

32. What is automatic clustering in Snowflake?

Automatic clustering is the service that effortlessly and continuously manages all reclustering, as needed, in the clustered tables.

The benefits of automatic clustering include:

• Ease of maintenance
• Full control
• Non-blocking DML
• Optimal Efficiency

33. How does Snowflake address high availability and disaster recovery?

Snowflake replicates data across multiple geographic regions, ensuring high availability and disaster recovery. This replication is done systematically and transparently to the users.

Snowflake ensures the highest availability and disaster recovery with automated replication into several availability zones and regions. It supports few interruption failovers and failbacks with point-in-time recovery as the data protection service ensures no data loss.

34. How do Snowflake’s virtual warehouses impact scalability and cost?

Snowflake’s virtual warehouses impact scalability and cost in the following ways:

• Scalability: Virtual warehouses allow scalability features for users to scale up or down their resources based on their varying workloads, ensuring high performance. When your processing task load increases, you can add additional computing resources without impacting the ongoing operations.
• Cost: The virtual warehouses operate independently, which allows high performance and consistency when performing specific data processing tasks, such as data enrichment. One has to pay only for the computing resources used, which provides cost management features compared to old data warehousing

35. How does Snowflake handle data concurrency?

Snowflake possesses its own architecture of multiple clusters, and in this multi-cluster architecture, all virtual warehouses act independently. Because of this property, more queries can run together without making a difference in performance. Besides this, it supports a locking mechanism and transaction isolation to keep all the data synchronized and in line during any of the concurrent activities.

36. What is the role of the Snowflake Compute layer?

In the Snowflake compute layer, virtual warehouses manage the data handling tasks. These warehouses contain multiple clusters of computing resources. While performing any operations, the warehouses extract the most minor data from the storage layer to fulfill the query requests by filtering, sorting, joining, and scaling up or down, which are required for faster performance.

37. Describe the Snowflake Cloud Services layer.

The Cloud Services layer serves as Snowflake’s central intelligence hub. This layer is responsible for verifying user sessions, applying security functions, giving management abilities, enhancing processes, and optimizing all transactions within the Snowflake environment.

38. Star Schema Vs Snowflake Schema

The star schema is easier and provides more efficient query performance because it minimizes the number of joins that have to occur between tables. It is good for simple, small datasets in which query speed is important.

On the other hand, a snowflake schema has normalization. It eliminates the redundancy of data and thus brings storage efficiency. A snowflake schema is good when the dataset is more complex and requires more detailed analysis.

39. What is clustering in Snowflake?

In Snowflake, data partitioning is called clustering, which has cluster keys on the table. A clustering key is a subset of table columns meant to co-locate the data in the micro-partitions. This is useful for large tables where the ordering could have been better or if DML has decreased the table’s natural clustering.

40. How do you optimize SQL queries in Snowflake?

To optimize SQL queries in Snowflake, there are several properties to focus on:

• Use clustering keys to sort the data correctly and enhance the query performance by decreasing the number of data scanned.
• Utilise Snowflake’s caching mechanisms, like results and metadata caching, to speed up duplicated queries.
• Ensure that columns use the proper data types to decrease storage and enhance query execution.
• Regulate the size of the virtual warehouse to regularise the compute resources depending on the workload requirements.

41. How does Snowflake manage schema evolution and versioning?

Snowflake allows schema evolution, where the users can modify table structures without disturbing the present queries. Versioning includes tracking the changes made to the objects over time, giving you a history.

42. What is the difference between Fail-Safe and Time Travel in Snowflake?

Fail-Safe: Snowflake provides a standard 7-day time when historical data can be accessed as a fail-safe feature. After the Time-Travel data retention period ends, the fail-safe period begins. Data recovery by fail-safe is executed under optimal conditions and occurs only after all other recovery alternatives have been exhausted. Snowflake may use it to retrieve the data that might have been lost or damaged due to excessive operational failures. It can take several hours to several days for Fail-Safe to complete data retrieval.

Time-Travel in Snowflake: Time travel is a feature that gives you access to historical data in the Snowflake data warehouse. For example, if you delete a table named Student, you can go back 2-3 minutes to get the data you lost by using time travel. Data that has been changed or deleted can be retrieved using Snowflake Time Travel at any point within a given period.

43. How does Snowflake ensure data consistency in a distributed environment?

Snowflake uses transactional ACID properties (Atomicity, Consistency, Isolation, Durability) to ensure data consistency. It uses an architecture designed for distribution and scalability to maintain consistency.

44. Explain the data-sharing capabilities of Snowflake.

Data-sharing capabilities by Snowflake make it possible to have secure and regulated real-time sharing of data across many different accounts without the original data being copied or moved. This is beneficial to companies collaborating with clients or external partners with strict regulatory and security needs. There are options for sharing whole databases and schemas or distinct tables and views.

45. Which cloud platforms does Snowflake currently support?

Snowflake currently supports:

• Amazon Web Services (AWS)
• Microsoft Azure (Azure)
• Google Cloud Platform (GCP)

46. Could AWS glue connect to Snowflake?

Yes, it is possible to connect Snowflake through AWS glue. It is a perfect fit into Snowflake as a warehouse service and presents a wholly managed environment. Combining this solution allows for more flexibility in data ingestion and transformation.

47. What is Snowflake’s approach to handling data deduplication?

Snowflake automatically handles data deduplication while performing data loading and storage, eliminating manual process requirements.

48. Explain the role of Snowflake’s Result Set Caching.

Result Set Caching in Snowflake allows the system to store the results of regularly executed queries. When a similar query is run, Snowflake can get the results from the cache, improving query performance.

49. How can you access the Snowflake Cloud data warehouse?

Snowflake’s data warehouse can be accessed using the following ways:

• ODBC Drivers (a driver used to connect to Snowflake).
• JDBC Drivers (driver allows a Java application to interact with a database).
• Python Libraries (to create applications connected to Snowflake and perform operations).
• Web User Interface (can be used for almost any task you can do with SQL and the command line, such as Creating users and other account-level works).
• SnowSQL Command-line Client (Python-centric command-line interface that connects Snowflake from Windows, Linux, or MacOS)

50. Which ETL tools are compatible with Snowflake?

Snowflake is compatible with the following ETL tools:

• Matillion
• Blendo
• Hevo Data
• StreamSets
• Etleap
• Apache Airflow
• Stitch
• io, etc.

51. Explain how to use Resource Monitors in Snowflake.

Resource Monitors track and control computing resource usage, ensuring usage stays within budget. They’re helpful where multiple team environments exist, and users can set them up for specific warehouses or the complete account.

Candidates would explain how to use them, and they would,

• Define a resource monitor
• Set credit or time-based limits and mention actions for when resource usage reaches or exceeds those limits.
• Actions could include sending notifications, suspending virtual warehouses, or even shutting them.

52. How would you audit data access in Snowflake?

Snowflake has different tools for auditing data access:

• The Access History function allows you to track when and who accessed the data and when it was accessed.
• Snowflake’s role-based access control allows you to review and manage who has the authority to access which data, further improving your auditing capabilities.
• Third-party tools and services can help monitor, log, and analyze access patterns.

53. What kind of SQL does Snowflake use?

Snowflake supports the most common version of SQL, i.e., ANSI, for powerful relational database queries.

54. Tell me something about Snowflake AWS.

Organizations rely on a data platform that provides rapid deployment, high performance, and on-demand scalability to manage today’s data analytics. Snowflake on the AWS platform serves as an SQL data warehouse, enhancing data warehousing by making it more manageable and accessible. It allows data-driven organizations to secure data sharing, elasticity, and per-second pricing.

55. What is the SnowSQL CLI client used for?

SnowSQL CLI is the command-line tool used to connect to Snowflake. It allows users to execute SQL queries and complete all DDL and DML actions, such as loading and unloading data from database tables.

SnowSQL (now SQL executable) can be used as an interactive shell or in batch method via stdin or with the -f option.

56. What is the use of Snowflake Connectors?

The Snowflake connector is software that enables users to connect to the Snowflake data warehouse to platform various activities such as Reading and Writing, Metadata importing, and Bulk data loading.

The Snowflake connector is used to perform the following tasks:

• Read data from or publish data to tables in the data warehouse.
• Load data in bulk into the data warehouse table.
• Using the Numerous input connections functionality, you can insert or load bulk data into many tables simultaneously.
• To look into the records from a table in the data warehouse.

Below are the types of Snowflake Connectors:

• Snowflake Connector for Kafka
• Snowflake Connector for Spark
• Snowflake Connector for Python

57. Does Snowflake use indexes?

No, Snowflake does not use indexes. It uses automatic indexing internally, although users don’t manage them directly  This is one feature that makes Snowflake scale better than others for queries.

58. How do we create temporary tables?

To create temporary files, you can run the below query:

Create temporary table table name (id number, creation_date date);

Explanation:

• Temporary tables exist only for the session in which they are created.
• Once the session ends, the table and its data are automatically dropped.
• Temporary tables are useful for storing intermediate results without affecting the main database schema.

59. How is the execution of a Snowflake procedure carried out?

Executing a Snowflake procedure involves the following steps:

• Run the SQL command.
• Retrieve the query results.
• Retrieve the result set metadata.

Conclusion

Snowflake is a cloud-based warehousing service for data storage, processing, and analysis. Excelling at Snowflake substantially increases your skills in data and career. By preparing these interview questions, you’re good to go on your way to amaze the employers. Enrol today in our comprehensive Executive Post Graduate Certification in Cloud Computing and DevOps to start your career or enhance your skills in the field of  Cloud Computing and get certified today.