Snowflake Interview Questions

Snowflake Interview Questions for Experienced

20. Is Snowflake an OLTP or OLAP database?

Snowflake is intended to be used as an OLAP database. The separation of storage and processing is one of Snowflake's distinguishing features: Amazon S3 is in charge of storage. The data is stored on Amazon servers, processing nodes access, and used for analytics. 

21. In Snowflake architecture, explain the cloud services layer.

The cloud services layer is a group of services that work together to coordinate Snowflake's activities. These services connect Snowflake's different components to process user requests from login to query dispatch. Snowflake also provided compute instances from the cloud provider for the cloud services layer.
This layer controls the following services:
a. Authentication
b. Management of infrastructure
c. Controlling metadata
d. Optimization and parsing of queries
e. Controlled access.

22. How would you describe Materialized View?

Snowflake's materialized view is a pre-calculated data set derived from the query definition. Because the data is pre-calculated, challenging the materialized view is more accessible than challenging from the view's base table. Materialized views boost query performance for common and recurring query patterns.

23. In Snowflake, what are the different types of Catches?

The various types of catches available in Snowflake are listed below:

The cache of Query Output

Cache for Metadata

Local Disk Caching for Virtual Warehouses   

24. What role does SQL play in Snowflake?

SQL (Structured Query Language) is a programming language that is primarily used for data communication. General operators were merged into DDL (Data Definition Language) and DML (Data manipulation language ) in SQL to run various statements such as UPDATE, SELECT, CREATE, INSERT, DROP, and so on. Snowflake supports SQL standard edition. We use SQL in Snowflake to perform general data warehousing operations such as insert, alter, create, delete, update, etc. 

25. How do we keep Snowflake data safe?

All organizations place a high priority on data security. Snowflake encrypts and secures data and customer accounts using industry-leading security standards. It comes with industry-leading key management features at no additional cost.

Snowflake takes the following security measures to protect our data:

a. Snowflake uses a managed key to encrypt the data it stores systematically.

b. Snowflake uses TLS to protect communication between clients and servers.

c. It allows us to select a geological location for data storage.

26. How do we verify Snowflake's history?

Query the "TASK HISTORY" table function in the information schema to get activity history details for executing in an executing or scheduled state.

27. Did snowflake introduce any of the objects?

Primary keys, foreign keys, unique constraints, and the Not Null constraint can all be found in Snowflake tables. On the other hand, PKs, FKs, and Unique controls are not enforced. We can add duplicate records to a column even if it has a Primary Key.

28. What are the various Snowflake data sharing types?

There are three different types of data sharing:

a. Sharing between functional units.

b. Data is shared between management units.

c. Data sharing between geologically dispersed locations.

29. In Snowflake, describe the various types of warehouses.

In Snowflake, a virtual warehouse, also known as a "warehouse," is a collection of computing resources. In a Snowflake session, a warehouse provides the necessary resources, such as CPU, memory, and temporary storage, to perform the following operations:

Using computing resources to execute SQL SELECT statements (e.g., retrieving rows from tables and views).

30. Describe the advantages of Snowflake compression.

Snowflake Compression Benefits: 
Because of compression, storage costs are lower than native cloud storage.
On-disk caches have no storage costs.
For data cloning or data sharing, there is almost no storage overhead.

31. In Snowflake, what's the difference between Time-Travel and Fail-Safe?

Users can set and retrieve data going back into history based on their snowflake edition and object or account-specific time travel (day-data retention time in days) setup; users can set and retrieve data going back into history.

1. Failsafe: the user has no control over data retrieval, which is only possible after the time travel period. Only Snowflake support will be able to assist you in this situation for the next seven days. So, assuming you've set the time travel to 6 days, you'll be able to retrieve DB objects after the transaction has been completed + 6 days. Snowflake support can help you recover your objects between the 7th and 13th days after transaction execution. It is not possible to retrieve or restore things after the 13th day.

32. What is the Snowflake Data Retention Period?

Snowflake Time Travel relies heavily on the data retention period. Snowflake preserves the state of data before it is updated when data in a table is modified, such as when Data is deleted or objects containing data are removed. Data retention specifies how long historical data will be kept to perform Time Travel operations (SELECT, CREATE, CLONE, UNDROP, and so on).

The default retention period for all Snowflake accounts is one day (24 hours). The default data retention period for common objectives is one day, while it ranges from 0 to 90 days for enterprise editions and higher accounts.

33. Can you explain the difference between Snowflake and AWS (Amazon Web Service)?

Snowflake and Amazon Redshift, for example, are cloud-based data warehouse platforms that offer excellent performance, scalability, and business intelligence tools. Both platforms offer similar core functionality, such as relational management, security, scalability, and cost-efficiency. Pricing, user experience, and deployment options are among the differences.

• Snowflake is a complete SaaS (Software as a Service) offering, so no maintenance is required. AWS Redshift clusters, on the other hand, necessitate manual care.
   
• Snowflake's security model employs always-on encryption to enforce stringent security checks, whereas Redshift uses a more flexible, customizable approach.
   
• Snowflake's storage and computation are entirely independent, which means that storage costs are comparable to S3. On the other hand, AWS uses a Red Shift spectrum to get around this problem and allows you to query data that is directly available in S3. Despite this, it isn't as perfect as Snowflake.

34. When the underlying data changes, what happens to Cache results?

When the data in the underlying micro-partition changes, Snowflake Cache results are invalidated. Although the Snowflake Documentation contains more details, tests have shown that the result cache is reused unless the underlying data (or SQL query) has changed. Inserts, updates, and deletes that do not affect the underlying data are ignored. The result cache is used if the data in the micro-partitions remains unchanged, as demonstrated by a series of additional tests.

Finally, results are typically kept for 24 hours, though the clock is reset every time the query is re-executed, up to a maximum of 30 days, after which they query the remote disk. 

35. What are the advantages of Snowflake Query Caching?

• The default setting for the Results Cache is Automatic. You do not need to do anything special to use this feature.
   
• For 24 hours, all results are cached.
   
• There are no restrictions on space. Snowflake Cache (AWS/GCP/Azure) has infinite storage.
   
• The cache is global and accessible to all WH and users.
   
• As a result of caching, your BI dashboards will load faster.
   
• As a result of caching, the compute cost is reduced.

36.  How do I stop Snowflake from caching query results?

Run the query below to disable the Snowflake Results cache. It should turn off the query for the duration of the session.

alter session set use_cached_result =false;

37. In Snowflake, what are the various types of caching?

Various types of caching in snowflake are:

• Caching of Search Results
• Local Disk Caching for Virtual Warehouses
• Cache for Metadata

38. How do data and information in Snowflake get protected?

Data security is one of the most critical concerns for any company. The Snowflake platform follows the industry's best security practices to encrypt and safeguard customer data. At no additional cost, the platform offers the best key management features. The following security measures are used by Snowflake to protect client data:

• Snowflake uses a managed key to encrypt the Data it holds automatically.
   
• Snowflake relies on Transport Layer Security (TLS) to ensure data security between customers and servers.
   
• You can choose a geographical location for your data storage based on your cloud region.

39. Explain Snowpipe.

The Snowpipe continuous data ingestion service loads files minutes after being added to a stage and submitted for ingestion. Instead of manually running COPY statements on a schedule to load large batches, you can load data from files in micro-batches (organizing data into small groups/matches), allowing users to access the data within minutes (speedy response time). Snowpipe divides data analysis into micro-batches, making it more manageable. Snowpipe uses a combination of filenames and file checksums to ensure that only new data is processed.

The benefits of Snowpipe are as follows:

• Snowpipe makes real-time analytics possible by removing obstacles.
• It's a good deal.
• It's easy to operate.
• Management isn't necessary.
• It offers a variety of benefits, including adaptability and resilience.

40. What features does Snowflake have for data security?

At no extra charge, Snowflake encrypts all customer data by default with End-to-End encryption (E2EE), which adheres to the most current security standards. Snowflake offers customers best-in-class key management that is entirely transparent.

a. Snowflake uses Snowflake-managed keys to encrypt all data automatically.

b.TLS secures all client-server communication and data transfer.

c.Depending on your cloud region, you can choose where your data is stored.

41. How does Snowflake handle data compression?

Snowflake compresses all of the data by default. Snowflake selects the best compression algorithms and does not allow end-users to customize them. The best part is that Snowflake charges customers based on the final data size after compression.

42. In Snowflake, describe the stages.

Staging is the process of uploading data into a stage in Snowflake. Data that needs to be loaded or stored in Snowflake is stored elsewhere in the cloud, such as AWS S3, GCP (Google Cloud Platform), Azure, or internally within Snowflake. An external stage occurs when Data is stored in another cloud region; an internal stage occurs when Data is stored within a snowflake. Internal stages are subdivided into the following categories:

• Because each stage is associated with a specific user, they will be assigned to all users by default for file storage.
   
• Table stages: Because each stage is associated with a specific database table, they will be assigned to all tables by default.
   
• Internally named stages provide more flexibility than user or table stages. All operations that can be performed on objects can also be performed internally called stages because these are some of the Snowflake objects. These stages must be created manually, and file formats can be specified.

43. What's the difference between Redshift and Snowflake?

Redshift and Snowflake, two of the most popular cloud-based data warehouses, are becoming increasingly popular. These massive data analytics databases can analyze and read vast amounts of data.

SNOWFLAKE

• Snowflake and Redshift package their features differently, despite having similar on-demand pricing. Compute usage and storage usage are separated in Snowflake's pricing structure.
   
• In terms of JSON storage, Snowflake is more reliable than Redshift. Snowflake, in essence, allows you to store and query JSON using native functions.
   
• Snowflake editions include security and compliance features tailored to ensure that your data is protected to the highest level possible per your data strategy.
   
• Snowflake allows for automated data vacuuming and compression. It has the most significant advantage because it automates a large portion of the process, saving time and effort. 

REDSHIFT

• Both computer and storage usage is combined in Redshift.
   
• On the other hand, JSON is split into strings when loaded into Redshift, making it more difficult to query and work with.
   
• The Redshift platform includes several encryption options.
   
• Because data vacuuming and compression on Redshift cannot be automated, the system requires more manual maintenance. 

44. What exactly does "virtual warehouse" imply?

Customers can use a virtual warehouse to run queries, load data, and perform other DML and SQL operations.

For example, it offers memory, temporary storage, and CPU resources for DML and SQL operations. This self-contained compute cluster can be used and then turned off when no longer required. You'll be charged (paid) for each virtual warehouse you run and the size and duration of those virtual warehouses. Because virtual warehouses don't share compute resources, their performance is unrelated.

As shown below, different user groups can be assigned separate and dedicated virtual warehouses. As a result, ETL processes can load and execute complex transformation procedures on different warehouses in real-time without impacting data scientists or financial reports.

45. What Makes Snowflake Cloud Data Warehouse Different?

The Snowflake is a cloud creature (built for the cloud). As a result, it takes advantage of all the benefits of the cloud and adds exciting new features such as:

• Scaling by itself
• Cloning with no copies
• Virtual storage facilities
• Traveling through time
• Military-grade security and encryption
• Strong data security features

It is well-designed with sensible defaults - all Data is compressed by default.

• The Data is all encrypted.
• Because it's columnar, it speeds up column-level analytical operations.
• The product's numerous innovations, such as the Intelligent Services layer, data sharing, tasks, and streams. Snowflake's pricing is also simple and transparent, making it easy for even small businesses to afford a cloud data warehouse.

Frequently Asked Questions

Is the Snowflake interview hard?

Depending on the job and the interviewer, a Snowflake interview's procedure and level of difficulty might change. Snowflake interviews are often of a medium to hard level.

How to crack Snowflake interview questions?

It's crucial to have a solid foundation of data modelling, data warehousing concepts, architecture, cloud-services layers, and practical expertise employing cloud-based data platforms if you want to succeed in a Snowflake interview. Python and SQL familiarity are also suggested.

What is the limitation of Snowflake?

Depending on a company's demands, Snowflake's cloud-agnostic nature can be both a benefit and a drawback. This can take a lot of time and resources, especially if you have a lot of data.

Conclusion

For freshers with little experience, the article discussed frequently asked Snowflake interview questions. The questions listed above are some of the most common Snowflake interview questions, and knowing how to answer them will help you ace your job interviews with ease. 

We hope that this blog has helped you enhance your preparation for Snowflake Interview and if you would like to learn more, check out our articles in the code360 library. 

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