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Snowflake Interview Questions and Answers

Commonly asked

What is Snowflake?

Snowflake is a cloud-based data warehousing platform that offers a unique architecture designed for simplicity, flexibility, and scalability. It separates computing from storage, allowing users to scale each independently, which leads to better performance and cost optimization. Snowflake also supports various data integration and querying options, making it an excellent choice for modern data analytics.

Explain the key features of Snowflake.

What do you mean when you say "virtual warehouse"?

To put it simply, a virtual warehouse is a collection of computing resources (such as CPU, RAM, Solid State Drive, etc.) that clients may utilize to execute queries, load data, and carry out other SQL (Structured Query Language) and DML (Data Manipulation Language) operations. 

It offers resources like memory, temporary storage, and CPU that can be utilized for DML operations and SQL execution, for instance. This autonomous compute cluster is available for use at any time and can be turned off when not required. For each virtual warehouse you operate, regardless of its size or duration, you are charged (paid). Because virtual warehouses do not exchange computational resources, their performance is not dependent on one another.

What do you mean when you say "snowflake computing"?

The ability of Snowflake to deliver immediate, secure, and controlled access to all data networks, combined with its basic architecture that supports all types of data workloads and provides a unifying platform for contemporary data applications, are all referred to as Snowflake computing.  Unlike other data warehouses, Snowflake doesn’t make use of a database or “big data” software platform like Hadoop. Snowflake, on the other hand, blends a wholly new SQL query engine with a fully cloud-based architecture.

Does Snowflake is an ETL tool?

Snowflake supports both ETL and ELT transformations, either before or after loading. Numerous data integration programs, such as Talend, Informatica, Matillion, Tableau, and others, are compatible with Snowflake.

Traditional processes like manual ETL coding and data cleansing are being replaced by new technologies and self-service pipelines in data engineering. Data engineers can devote more time to important data strategy and pipeline enhancement projects thanks to Snowflake’s straightforward ETL and ELT choices. Furthermore, since no pre-transformations or pre-schemas are required when using the Snowflake Cloud Platform as your data warehouse, and data lake, extract, convert, and load may be effectively avoided.

Snowflake is what kind of database?

An SQL database serves as the foundation for Snowflake. Excel, Tableau, and many other programs operate nicely with this columnar-stored relational database. In addition to supporting role-based security, multi-statement transactions, and other features typical of a SQL database, Snowflake also has its own query tool.

How does Snowflake store data?

Snowflake saves the data in a number of internally optimized and compressed micro partitions. The data is kept in Snowflake’s cloud storage in a columnar format. Snowflake’s data items are not accessible to or viewable by people. You can get access to them by executing SQL query operations on Snowflake.


How exactly does Snowflake Data Compression work?

In Snowflake, all the data is compressed by default. Since end users are unable to configure Snowflake, it chooses the best compression techniques. The best feature of Snowflake is that, after compressing the data, the user is only charged for the compressed data’s final size. 

The advantages of data compression in a snowflake are:

  • Compression reduces the cost of storage relative to native cloud storage.
  • Disk caches have no storage expense.
  • Cloning or sharing data results in almost minimal storage overheads. 

What is Snowflake's "Time Travel" feature?

Snowflake’s Time Travel feature enables users to access historical versions of their data. It allows querying data as it existed at different points in time, which is valuable for compliance, auditing, and data analysis of changes over time.

Describe Fail-safe

It is a sophisticated feature of Snowflake that helps to protect data. It is a crucial phase in the life cycle of snowflake data protection. Even once the trip period has been over, it still allows a further seven days of storage.

Why do we use Fail-safe?

DBA normally does complete and incremental data backups on a frequent basis to lower the risk factor. This technique occasionally uses double or treble the amount of storage. Data recovery also costs money, takes time, disrupts business operations, and does other things.    

What does Snowflake's Storage Layer do?

The storage layer in Snowflake is where different types of data, tables, & query results are kept. Scalable cloud blob storage (using the storage systems of AWS, GCP, or Azure) serves as the foundation for the storage layer. For optimum scalability, flexibility, and performance capabilities for data warehousing and analytics, the storage layer is created to scale totally independently of computing resources.

What does Snowflake's compute layer be used for?

In Snowflake, all data processing activities are carried out by virtual warehouses, that utilize one or more clusters of computing resources. To satisfy query requests, virtual warehouses only fetch the absolute minimum amount of data from the storage layer.

What different ways are there to access the Snowflake cloud data warehouse?

A web-based user interface is available for accessing all elements of utilizing and managing Snowflake. All aspects of Snowflake management and use are accessible via command-line clients for Snowflake (like SnowSQL). ODBC and JDBC drivers can be used to link Snowflake to other programs (like Tableau).

What advantages does snowflake compression offer?

Snowflake stores files in storage using gzip compression by default, which helps to save storage space and enhances the speed at which data is loaded and unloaded. Additionally, it recognizes compressed file types including gzip, bzip2, deflate, and raw_deflate.

In Snowflake, what is fail-safe?

Removable data is relocated to a fail-safe zone for Ent. edition Snowflake and higher after the time-traveling period has passed. This zone is 7 days long. In order to restore the data once it was moved to Failsafe, we must get in touch with Snowflake. Getting the data could take anywhere from 24 hours to days. The charges will start at the point at which the data’s status changes over a 24-hour period.

Where does Snowflake store its metadata?

When a table is formed in Snowflake, information about the table is generated that includes the number of rows, the time and date it was created, and aggregation functions for numerical columns like min, sum, and max.

S3, where Snowflake maintains the data storage, is where metadata is saved.

Because of this, maintaining a warehouse is not necessary while querying the metadata.

Is Snowflake an MPP database?

Yes. MPP stands for “Massively Parallel Processing.” Since Snowflake is based on the cloud, it gains cloud-specific attributes like scalability. By supplying the necessary computing resources, it can manage concurrent running queries.

The computational environment is shared across the users in a shared-nothing architecture that Snowflake enables. It automatically establishes numerous clusters on nodes equipped to handle the sophisticated query logic and execution when the query demand rises.

Give a brief explanation about Snowflake AWS.

The Snowflake AWS platform, commonly known as a SQL data warehouse, is an extension to the platform that improves data warehousing efficiency, control, and accessibility for all of the platform’s many data users. Additionally, it provides elastic data exchange and security for data-driven enterprises.

How can we reduce Snowflake's bills, in your opinion?

One of the numerous advantages of using Snowflake is that clients are only charged for the size of their data after compression, not their original data size. We can spend less on storage because of data compression. Other capabilities like data sharing and cloning are also cost-free.

What do you mean when you refer to Snowflake computing and Snowflake schema?

Snowflake Computing: The ability of the Snowflake cloud data warehouse to offer safe and managed access to the data network in order to enable various types of data workloads utilizing a single platform for creating contemporary data applications is referred to as snowflake computing.



Schema: Snowflake refers to the idea of arranging the data in Snowflake.  To put it simply, databases and schemas are used to organize the data kept in Snowflake. The logical arrangement of the database objects is known as a schema.  Tables, views, and other items make up the database. The Snowflake model has the advantage of using this limited disc space and delivering organized data.

What does "Snowflake caching" mean?

Snowflake has the ability to cache the results of queries.  It also checks the results of the previously completed queries as soon as a new query is entered.  The results are cached if it discovers any existing queries that match it. Then, rather than rerunning the query, it uses the cached result set. Because of this, the Snowflake can be utilized by any number of users worldwide, earning the moniker “global Snowflake catching.”

What are the key distinctions between Snowflake's vertical as well as horizontal scaling in brief?

The term “horizontal scaling” typically refers to scaling that will aid in boosting concurrency. You are permitted to use auto-scaling if it is necessary to support multiple users, as well as to increase the number of virtual warehouses, in order to obtain immediate support and satisfy the user’s requests.

The term “vertical scaling” refers to a scaling that will aid in speeding up processing. When there is a heavy demand or when optimization is necessary, you can think about selecting a larger virtual warehouse size.

Could you simply describe the differences between fail-safe and time travel?

The ability to set and retrieve data as needed by traveling across time gives the user this flexibility. This is based on the object that may be used to set up time travel for a specific account in the Snowflake version.

In fail-safe, the user is not permitted to retrieve the data and won’t be given any control over it until the time travel period has ended. The information is only available for seven days from the Snowflake support staff. 

Let’s assume you set the time travel to six days. In that case, you are permitted to retrieve database objects for six days starting on the seventh day after the transaction execution, or from the seventh to the thirteenth day. After the 13th day, you won’t be able to get the data back or restore it.

Can the AWS glue connect to the Snowflake?

Yes, there is a chance that the glue in the affected places will adhere to Snowflake.  Users can easily connect to Snowflake as a data warehouse service with the support of WS Blue’s comprehensive managed environment, which is offered to users. When used in tandem, Snowflake, and AWS glue are two distinct technologies that give you a lot of flexibility and convenience when handling data transformation and data ingestion.

Could you please describe the Snowflake data-loading process?

Data is imported into the Snowflake data warehouse during the loading process from a variety of sources, including files, cloud storage services, and databases. Numerous techniques, including bulk loading, automated data input, and real-time data streaming, can be used to complete the data loading process.

Bulk loading is a technique for adding a lot of data to Snowflake at once. The process for doing this includes uploading a data file to a cloud storage service, like Amazon S3 or Microsoft Azure, and then loading the data into the Snowflake data warehouse using the Snowflake data loading interface.

In order to automatically load data into Snowflake frequently, a data pipeline must be built up. Data can be extracted from source systems, transformed as necessary, and loaded into Snowflake using a variety of tools, such as Alteryx or Apache Nifi.

As data is generated, it is loaded into Snowflake via real-time data streaming. This can be done by capturing real-time data streams and loading the data into Snowflake in close to real-time using tools like Apache Kafka or Amazon Kinesis.

What is and how is Snowflake's Zero-Copy Cloning performed?

Users can make numerous, identical copies of their own data and metadata using Snowflake’s Zero-Copy Cloning capability, either inside the same Snowflake account or across other accounts. In order for each cloned database to share the same storage, Zero-Copy Cloning establishes a reference to the existing data rather than physically duplicating it. Since the same underlying data is accessed by queries to the cloned databases, there is no rise in storage usage and not any effect on performance.

What distinguishes transient from temporary tables?

You can show that you comprehend the subtleties of the underlying concept by describing how it differs from the other notion while responding to questions comparing the differences between two concepts. Start your response by defining both and describing how they are different from one another.

In Snowflake, temporary tables are comparable to permanent tables, with the exception that they lack a fail-safe period. Temporary tables are only present during a specific session. temporary tables are useful for storing temporary data that is only needed for one session but does not require additional levels of data security. For each user account, Snowflake’s overall storage limits include both transient and temporary tables.

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