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What is Snowflake?
Snowflake is a SaaS (Software as a service is a cloud-based method of providing software to users.) based data warehouse (DWH) platform that is built on the top of AWS (Amazon Web Services), Microsoft Azure, and Google Cloud infrastructures to provide companies with flexible, scalable storage solutions while also hosting BI (Business Intelligence) solutions. It serves as a centralized platform for data management, data lakes, data engineering, data applications development, data science, and secure sharing and consumption of real-time and shared data. Providing a centralized system to consolidate all data, Snowflake revolutionized the data warehousing industry. With Snowflake, you can simplify data warehouse management without sacrificing features.
To satisfy the demanding needs of growing enterprises, Snowflake includes out-of-the-box features such as storage and compute separation, on-the-fly scalable computing, data sharing, data cloning, third-party tool support, etc. In comparison to traditional solutions, Snowflake delivers fast, easy-to-use, and flexible data storage, processing, and analytics. Several programming languages are supported by Snowflake, including Go, C, .NET, Java, Python, Node.js, etc.
In this article, we have shared the commonly asked Snowflake interview questions. These questions will better prepare you for the Snowflake jobs.
Contents
Snowflake Interview Questions
1. What are the essential features of Snowflake?
2. Explain Snowflake Architecture.
3. What do you mean by virtual warehouse?
4. Can you tell me how to access the Snowflake Cloud data warehouse?
5. What is the difference between Snowflake and Redshift?
6. Explain stages in Snowflake.
7. Explain Snowpipe.
8. What do you mean by Snowflake Computing?
9. Which cloud platforms does Snowflake currently support?
10. In Snowflake, how are data and information secured?
11. Is Snowflake an ETL (Extract, Transform, and Load) tool?
12. Which ETL tools are compatible with Snowflake?
13. What do you mean by Horizontal and Vertical Scaling?
14. Is snowflake OLTP (Online Transactional Processing) or OLAP (Online Analytical Processing)?
15. Snowflake is what kind of database?
16. Explain in short about Snowflake Clustering.
17. How is data stored in Snowflake? Explain Columnar Database.
18. Explain Schema in Snowflake.
19. State the difference between Star Schema and Snowflake Schema.
20. Explain what is Snowflake Time travel and Data Retention Period.
21. What is Data Retention Period in Snowflake?
22. Explain what is fail-safe.
23. Can you explain how Snowflake differs from AWS (Amazon Web Service)?
24. Could AWS glue connect to Snowflake?
25. Explain how data compression works in Snowflake and write its advantages.
26. Explain Snowflake caching and write its type.
27. What are different snowflake editions?
28. What do you mean by zero-copy cloning in Snowflake?
29. Explain what do you mean by data shares in Snowflake?
30. What is the best way to remove a string that is an anagram of an earlier string from an array?
1) What are the essential features of Snowflake?
Snowflake has the following key features:
The core architecture of Snowflake enables it to operate on the public cloud, where it uses virtualized computing instances and efficient storage buckets for processing huge amounts of big data cost-effectively and it is also scalable.
Snowflake integrates with several big data tools, including business intelligence, machine learning, data integration, security, and governance tools.
With advanced features such as simplicity, increased performance, high concurrency, and profitability, Snowflake is incomparable to other traditional data warehouse solutions.
Snowflake supports the storage of both structured and semi-structured data (such as JSON, Avro, ORC, Parquet, and XML data).
Snowflake automates cloud data management, security, governance, availability, and data resilience, resulting in reduced costs, no downtime, and better operational efficiency.
With it, users can rapidly query data from a database without impacting the underlying dataset. This allows them to receive data closer to real-time.
Most DDL (Data Definition Language) and DML (Data Manipulation Language) commands in SQL are supported by the Snowflake data warehouse. Additionally, advanced DML, lateral views, transactions, stored procedures, etc., are also supported.
2. Explain Snowflake Architecture.
The Snowflake architecture is a hybrid of shared-disk (A common disk or storage device is shared by all computing nodes) and shared-nothing (Each computing node has a private memory and storage space) database architecture in order to combine the best of both. Snowflake utilizes a central data repository for persistent data, which is available to all compute nodes similar to a shared-disk architecture. But, equally, as with shared-nothing architectures, Snowflake uses massively parallel computing (MPP) clusters for query processing, in which each node stores part of the whole data set locally.
The Snowflake architecture is divided into three key layers as shown below:
3. What is a virtual warehouse?
A virtual warehouse is a collection of computing resources (like CPU, memory, Solid state drive, etc.) that customers can access to run queries, load data, and perform other DML (Data Manipulation Language) and SQL (Structured Query Language) operations.
For example, it provides memory, temporary storage, and CPU resources that can be used for DML operations and SQL execution. You can use this independent compute cluster at any time and then turn it off when not needed. You are charged (paid) for each virtual warehouse you run, their size, and how long they run. Virtual warehouses do not share their compute resources with each other, and therefore, their performance is independent of each other.
As shown in the following diagram, different groups of users can be assigned separate and dedicated virtual warehouses. Therefore, ETL processes can continuously load and execute complex transformation procedures on separate warehouses, ensuring no impact on data scientists or finance reports.
4. Can you tell me how to access the Snowflake Cloud data warehouse?
Snowflake's data warehouse can be accessed using the following ways:
5. What is the difference between Snowflake and Redshift?
Cloud-based data warehouses are becoming increasingly popular, with Redshift and Snowflake being two of the biggest players. These are large data analytics databases capable of analyzing and reading vast amounts of data.
Snowflake vs Redshift -
6. Explain stages in Snowflake.
Stages are locations in Snowflake where data is stored, and staging is the process of uploading data into a stage. Data that needs to be loaded or stored within Snowflake is stored either in the other cloud regions like in AWS (Amazon Web Service) S3, GCP (Google Cloud Platform), or Azure, or is stored internally within Snowflake. When data is stored in another cloud region, this is known as an external stage; when it is stored inside a snowflake, it is known as an internal stage. Internal stages can be further categorized as follows:
7. Explain Snowpipe.
In simple terms, Snowpipe is a continuous data ingestion service provided by Snowflake that loads files within minutes as soon as they are added to a stage and submitted for ingestion. Therefore, you can load data from files in micro-batches (organizing data into small groups/matches), allowing users to access the data within minutes (very less response time), rather than manually running COPY statements on a schedule to load large batches. By loading the data into micro-batches, Snowpipe makes it easier to analyze it. Snowpipe uses a combination of filenames and file checksums to ensure that only new data is processed.
Advantages of Snowpipe -
8. What do you mean by Snowflake Computing?
The term snowflake computing refers to Snowflake's ability to provide instant, secure, and governed access to all data networks, along with its core architecture that enables multiple types of data workloads and offers a unified platform for modern data applications. In contrast to other data warehouses, Snowflake does not use a database or "big data" software platform such as Hadoop. Snowflake, however, combines an entirely new SQL query engine with a natively cloud-based architecture.
9. Which cloud platforms does Snowflake currently support?
Snowflake currently supports the following cloud platforms:
10. In Snowflake, how are the data and information secured?
Every organization considers data security to be one of its top priorities. The Snowflake platform adheres to the best security standards in the industry to encrypt and safeguard customer data. The platform provides the best key management features at no additional charge. To protect client data, Snowflake employs the following security measures:
11. Is Snowflake an ETL (Extract, Transform, and Load) tool?
Yes, Snowflake is an ETL (Extract, Transform, and Load) tool, since it is performed in three steps:
12. Which ETL tools are compatible with Snowflake?
Snowflake is compatible with the following ETL tools:
13. Explain Horizontal and Vertical Scaling?
14. Is snowflake OLTP (Online Transactional Processing) or OLAP (Online Analytical Processing)?
Snowflake is developed as an OLAP (Online Analytical Processing) database system, not as an OLTP (Online Transaction Processing) database system. In OLTP (Online Transaction Processing), data is collected, stored, and processed from real-time transactions, but in OLAP (Online Analytical Processing), complex queries are used to evaluate aggregated historical data from OLTP systems. Snowflake is not designed to handle much updating and inserting of small amounts of data like a transactional database would. Snowflake, for instance, cannot handle referential integrity because, even though it supports integrity and other constraints, they are not enforced (except the NOT NULL constraint which is always enforced). Other than the NOT NULL constraint, all other constraints are created as disabled constraints. However, depending on the use, we may also use it for online transaction processing (OLTP).
15. What kind of database is Snowflake?
All of Snowflake's features are built on top of SQL (Structured Query Language) databases. The data in this relational database system is stored in columns and it is compatible with other tools, including Excel and Tableau. As a SQL database, Snowflake contains a query tool, supports multi-statement transactions, provides role-based security, etc.
16. Explain in short about Snowflake Clustering.
In Snowflake, clustering is a type of data partitioning, where unique cluster keys are specified for each table. Cluster keys are subsets of a table's columns that are used to co-locate data within the table. These keys are appropriate for comprehensive tables. The process of managing clustered data in a table is known as re-clustering.
17. How is data stored in Snowflake? OR Explain Columnar Database.
After data is loaded into Snowflake, it automatically reorganizes the data into a compressed, optimized, columnar format (micro-partitions). The optimized data is then stored in the cloud storage. Snowflake manages all aspects of storing these data, including file structure, size, statistics, compression, metadata, etc. Snowflake data objects aren't visible to customers or users. Users can only access data by performing SQL queries on Snowflake. Snowflake uses a columnar format to optimize and store data within the storage layer. With it, data is stored in columns instead of rows, allowing for an analytical querying method and improving database performance. With Columnar databases, business intelligence will be easier and more accurate. Compared to row-level operations, column-level operations are faster and use fewer resources than row-level operations.
Above is an example of a table with 24 rows divided into four micro-partitions, arranged and sorted by column. As the data is divided into micro-partitions, Snowflake can first remove those micro-partitions that are not relevant to the query, followed by pruning the remaining micro-partitions by column. The result is fewer records traversed, resulting in significantly faster response times.
18. Explain Schema in Snowflake.
The Snowflake Schema describes how data is organized in Snowflake. Schemas are a logical grouping of database objects (such as tables, views, etc.). Snowflake schemas consist of one fact table linked to many dimension tables, which link to other dimension tables via many-to-one relationships. A fact table (stores quantitative data for analysis) is surrounded by its associated dimensions, which are related to other dimensions, forming a snowflake pattern. Measurements and facts of a business process are contained in a Fact Table, which is a key to a Dimension Table, while attributes of measurements are stored in a Dimension Table. Snowflake offers a complete set of DDL (Data Definition Language) commands for creating and maintaining databases and schemas.
As shown in the above diagram, the snowflake schema has one fact table and two- dimension tables, each with three levels. Snowflake schemas can have an unlimited number of dimensions, and each dimension can have an infinite number of levels.
19. State differences between Star Schema and Snowflake Schema.
Schemas like Star and Snowflake serve as a logical description of the entire database, or how the data is organized in a database.
20. Explain what is Snowflake Time travel and Data Retention Period.
Time travel is a Snowflake feature that gives you access to historical data present in the Snowflake data warehouse. For example, suppose you accidentally delete a table named Employee, using time travel, it is possible to go back five minutes in time to retrieve the data you lost. Data that has been altered or deleted can be accessed via Snowflake Time Travel at any point within a defined period. It is capable of performing the following tasks within a specific/defined period:
As soon as the defined/specific period (data retention period) expires, the data moves into Snowflake Fail-safe and these actions/tasks cannot be performed.
21. What is Data Retention Period in Snowflake?
The data retention period is a critical component of Snowflake Time Travel. When data in a table is modified, such as when data is deleted or objects containing data are removed, Snowflake preserves the state of that data before it is updated. Data retention specifies for how many days historical data will be preserved enabling Time Travel operations (SELECT, CREATE, CLONE, UNDROP, etc.) to be performed on it.
All Snowflake accounts have a default retention period of 1 day (24 hours). By default, the data retention period for the standard edition is 1 day, while for enterprise editions and higher accounts, it is 0 to 90 days.
22. Explain what is fail-safe.
Snowflake offers a default 7-day period during which historical data can be retrieved as a fail-safe feature. Following the expiration of the Time Travel data retention period, the fail-safe default period begins. Data recovery through fail-safe is performed under best-effort conditions, and only after all other recovery options have been exhausted. Snowflake may use it to recover data that has been lost or damaged due to extreme operational failures. It may take several hours to several days for Fail-safe to complete data recovery.
23. Can you explain how Snowflake differs from Redshift?
Cloud-based data warehouse platforms like Snowflake and Amazon Redshift provide excellent performance, scalability, and business intelligence tools. In terms of core functionality, both platforms provide similar capabilities, such as relational management, security, scalability, cost efficiency, etc. There are, however, several differences between them, such as pricing, user experience, and deployment options.
24. Explain how data compression works in Snowflake and write its advantages.
An important aspect of data compression is the encoding, restructuring, or other modifications necessary to minimize its size. As soon as we input data into Snowflake, it is systematically compacted (compressed). Compressing and storing the data in Snowflake is achieved through modern data compression algorithms. What makes snowflake so great is that it charges customers by the size of their data after compression, not by the exact data.
Snowflake Compression has the following advantages:
25. Explain Snowflake caching and write its type.
Consider an example where a query takes 15 minutes to run or execute. Now, if you were to repeat the same query with the frequently used data, later on, you would be doing the same work and wasting resources. Alternatively, Snowflake caches (stores) the results of each query you run, so whenever a new query is submitted, it checks if a matching query already exists, and if it did, it uses the cached results rather than running the new query again. Due to Snowflake's ability to fetch the results directly from the cache, query times are greatly reduced.
Types of Caching in Snowflake
The following diagram visualizes the levels at which Snowflake caches data and results for subsequent use.
26. What are different snowflake editions?
Snowflake offers multiple editions to meet your organization's specific needs. In every subsequent edition, either new features are introduced or a higher level of service is provided. It's easy to switch editions as your organization's needs change. The following are some of the Snowflake Editions:
for organizations that deal with highly sensitive data. This edition includes all the features and services of the Enterprise Edition, plus enhanced security and data protection.
27. What is zero-copy cloning in Snowflake?
Zero-copy cloning is one of the great features of Snowflake. It allows you to duplicate the source object without making a physical copy of it or adding additional storage costs to it. A snapshot of the data in a source object is taken when a clone (cloned object) is created, and it is made available to the cloned object. Cloned objects are independent of the source object and are therefore writable, and any changes made to either object are not reflected in the other. The keyword CLONE allows you to copy tables, schemas, and databases without actually copying any data.
Zero copy cloning syntax in Snowflake
CREATE DATABASE Development CLONE Production;
To clone a schema
CREATE SCHEMA Development.DataSchema1 CLONE Production.DataSchema1;
To clone a single table:
CREATE TABLE C CLONE Development.public.Clone;
28. Explain what do you mean by data shares in Snowflake?
Data sharing via Snowflake allows organizations to share data quickly and securely between Snowflake accounts. Database objects that are shared between snowflake accounts are only readable and can't be changed or modified. The three types of sharing are as follows:
29. How can we create temporary tables?
In the CREATE TABLE DDL, specify the TEMPORARY keyword (or the TEMP abbreviation) to create a temporary table. The following syntax must be used to
create temporary tables:
Create temporary table myschematable (emp_id number, creation_date date);
Krsna (GCKR)
Database architect, SnowPro Certified, Trainer.