Snowflake's Data Cloud is built on a cutting-edge data platform delivered as Software-as-a-Service (SaaS). Snowflake provides data storage, processing, and analytic solutions faster, easier, and more flexible than traditional options.
Snowflake is not based on existing database technology or "big data" software platforms like Hadoop. On the other hand, Snowflake combines a brand-new SQL query engine with cutting-edge cloud architecture. Snowflake offers all of the features and capabilities of an enterprise analytic database to users.
The whole series of Snowflake Interview Questions consists of 90 interview questions divided into three parts. This Blog is Part 2, so you may refer to Part 1 and Part 3 of this series before moving on. Each consists of 30 Snowflake Interview Questions at the beginner and advanced levels. In this Blog, let us start with some vital Snowflake Interview Questions at the intermediate level.
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Snowflake Interview Questions
- What is Snowflake ETL, exactly?
Ans: The ELT process loads data into the Snowflake data warehouse. This configuration extracts relevant data from the data source, performs the necessary transformations to prepare the data analysis, and loads the data into Snowflake.
- What makes Snowflake unique?
Ans: It includes support for programming languages such as Go, Java, and others, data integration, business intelligence, advanced analytics, and security and governance. Snowflake only stores data in an encrypted format. End-to-end encryption is included. Snowflake offers scalable and dynamic computing power for a fee-based solely on usage.
- Snowflake Cloud Data Warehouse is a cloud-based data warehouse. Explain.
Ans: Snowflake's data cloud is supported by an advanced data platform that uses the software-as-a-service (SaaS) model. Compared to traditional products, it makes data processing, storage, and analytics solutions more accessible, faster, and flexible.
These data platforms aren't based on existing database technologies or "Big Data" software platforms like Hadoop. Snowflake is working on a cloud-based SQL query engine instead. As a result, Snowflake can offer all of an enterprise analytics database features and some unique.
- In a few sentences, describe the Snowflake architecture.
Ans: The Snowflake software architecture combines shared disk and database architectures. It works on the same principle as shared disk architecture in that it has a central repository that can access persistent data from all of the platform's computer nodes.
- In Snowflake, describe time travel.
Ans: Snowflake Time Travel allows you to view historical data (that is, data that has been changed or removed) at any point in time. It can help you with the following tasks:
Restoring data-related objects (tables, schemas, and databases) that were deleted by accident or on purpose.
Duplicating and archiving data from previous events.
Observing how data is used and manipulated over time.
- Is Snowflake concerned with indexing?
Ans: Indexes are not used in Snowflake. One of the reasons Snowflake scales so well for arbitrary queries is this. Instead, Snowflake calculates statistics about columns and records in files you load, then uses those statistics to determine which parts of which tables/records to load to run a query.
- How well do you understand Snowflake's failsafe?
Ans: Fail-safe is a best-effort data recovery service that should only be used after all other options have failed.
After the Time Travel retention period has expired, there is no fail-safe mechanism for accessing historical data. Only Snowflake can use it to recover data that has been lost or damaged as a result of extreme operational failures.
Fail-safe data recovery can take a few hours to a few days.
- The contrast between time travel and failsafe in Snowflake.
Ans: 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.
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 a maximum of 7 days. So, assuming you've set the time travel to 6 days, you'll be able to retrieve DB. Objects after the transaction have been completed + 6 days. Snowflake support can help you regain your objects between the 7th and 13th days after transaction execution. It is not possible to recover or restore things after the 13th day.
- How much does Snowflake time travel cost?
Ans: The fees are calculated every 24 hours (i.e., one day) starting from when the data was updated. The table type and Time Travel retention period determine how many days of historical data are kept.
Snowflake also reduces the amount of storage needed for historical data by storing only the information required to restore individual table rows that have been updated or deleted. As a result, storage consumption is calculated as a percentage of the changing table. When tables are dropped or truncated, complete copies of the tables are kept.
- In Snowflake, describe the various types of warehouses.
Ans: 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).
- Describe the advantages of Snowflake compression.
Ans: 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.
- What's your understanding of Snowflake's format data storage?
Ans: Snowflake divides the data into multiple micro partitions, each compressed and optimized internally. To store information, it employs a columnar format. Data is stored in the cloud and is managed using a shared-disk model.
- Is Snowflake an OLTP or OLAP database?
Ans: 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.
- What makes Snowflake work at such a breakneck pace?
Ans: Snowflake compresses data and stores it in blocks, unlike previous technologies that saved data in rows and columns. When compared to fetching rows, query processing is significantly faster.
- In Snowflake architecture, explain the cloud services layer.
Ans: 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:
b. Management of infrastructure
c. Controlling metadata
d. Optimization and parsing of queries
e. Controlled access.
- Is Snowflake a data lake?
Ans: Snowflake's platform combines the advantages of data lakes with data warehousing and cloud storage. Snowflake gives your company best-in-class performance, relational querying, security, and governance. Alternatively, you can store your data in Amazon S3 or Azure Data Lake and use Snowflake to speed up data transformations and analytics.
- A large Snowflake warehouse contains how many nodes?
Ans: There are a total of eight nodes in this network. A small VWH has four nodes, while a large VWH has eight. When a query is run on a cluster, it will execute the query in parallel on as many nodes as are available.
- Is Snowflake capable of handling unstructured data?
Ans: Unstructured Data and Snowflakes
Snowflake accelerates time-to-value for unstructured data by allowing customers to store, govern, process, and share files in the same way as structured and semi-structured data.
- State the unique feature of Snowflake?
Ans: Snowflake only keeps encrypted versions of its data. It's encrypted from start to finish. Users can forget about building complex security models from their end when the data is stored inside disks or when it is in motion. It has a single sign-on option and supports two-factor authentication and a federal authenticator.
- Did snowflake introduce any of the objects?
Ans: 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.
- What is a snowflake cluster, and how does it work?
Ans: Snowflake reorganizes column data during reclustering by using the clustering key for a clustered table to move related records to the same micro-partition. This DML operation deletes and re-inserts the affected documents, grouped by clustering key.
- How do we verify Snowflake's history?
Ans: Query the "TASK HISTORY" table function in the information schema to get activity history details for executing in an executing or scheduled state.
- What are the advantages of Materialized perspectives?
Ans: The following are some of the advantages of Materialized views:
a. Improves query response time
b. Materialized views are handled automatically by Snowflake.
c. Updated data is included in materialized views.
- What is Auto-scaling?
Ans: Snowflake's auto-scaling feature allows it to start and stop clusters based on the workloads in the warehouse.
- How would you describe Materialized View?
Ans: 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.
- In Snowflake, what are the different types of Catches?
Ans: 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
- What does the Clustering key mean?
Ans: The Clustering key in Snowflake is a subcategory of the table's columns that aids in co-locating data within the table. It works well in situations where tables are extensive.
- What are the various Snowflake data sharing types?
Ans: 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.
- What role does SQL play in Snowflake?
Ans: 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.
- How do we keep Snowflake data safe?
Ans: 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.
- Make sure you have a clear picture of the database schematics and architecture in your head before jumping into the technical interview's syntax and technicalities. The database key relationships across all tables should be visible.
- When a recruiter asks a question, your answer isn't the only thing recorded. They attempt to determine how you approach the situation and analyze the problem.
- In short, brushing up on your technical and conceptual skills while remembering these frequently asked Snowflake interview questions will significantly improve your tech interview preparation.
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 code studio library. The knowledge never stops, have a look at more related articles: Data Warehouse, MongoDB, AWS, and many more. See Operating System, Unix File System, File System Routing, and File Input/Output to learn more.
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