Are you preparing for the Snowflake interview? If Yes, then this blog is for you! This blog helps you get to know the Top Snowflake Interview Questions that are possibly asked in any Snowflake interview. Thus, we have designed this blog with the latest 2022 Snowflake Interview Questions and Answers for freshers and experienced professionals. By going through these interview questions, you will be able to crack the Snowflake interview easily.
Before we begin with the Snowflake Interview Questions, here are some interesting facts you must know about Snowflake in the industry.
Snowflake is attaining momentum as the best cloud data warehouse solution because of its innovative features like separation of computing and storage, data sharing, and data cleaning. It gives support for popular programming languages like Java, Golang, .Net, Python, etc. Tech giants like Adobe systems, AWS, Informatica, Logitech, Looker are using the Snowflake platform to build data-intensive applications. Therefore, there is always a demand for Snowflake professionals.
According to indeed.com, the average salary for a Snowflake Data Architect in the US is around $179k per annum. If that is the career move you are making, and you are preparing for a Snowflake job interview, the below Snowflake interview questions and answers will help you prepare.
We have categorized Snowflake Interview Questions - 2022 (Updated) into 3 levels they are:
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Snowflake is an analytic data warehouse implemented as a SaaS service. It is built on a new SQL database engine with a unique architecture built for the cloud. This cloud-based data warehouse solution was first available on AWS as software to load and analyze massive volumes of data. The most remarkable feature of Snowflake is its ability to spin up any number of virtual warehouses, which means the user can operate an unlimited number of independent workloads against the same data without any risk of contention.
Snowflake is built on an AWS cloud data warehouse and is truly a Saas offering. There is no software, hardware, ongoing maintenance, tuning, etc. needed to work with Snowflake.
Three main layers make the Snowflake architecture - database storage, query processing, and cloud services.
Unique features of the Snowflake data warehouse are listed below:
Snowflake cloud data warehouse platform provides instant, secure, and governed access to the entire data network and a core architecture to enable various types of data workloads, including a single platform for developing modern data applications. Snowflake brings together the power of data warehouses, the scalability of big data platforms, the elasticity of the cloud, and real-time data sharing at a fraction of the cost of traditional solutions.
The services layer acts as the brain of the Snowflake. In Snowflake, the Services layer authenticates user sessions, applies security functions, offers management, performs optimization, and organizes all the transactions.
Snowflake supports both transformations during (ETL) or after loading (ELT). Snowflake integrates with a variety of data integration solutions, including Informatica, Talend, Tableau, Matillion, and others.
In data engineering, new tools and self-service pipelines are displacing traditional tasks such as manual ETL coding and data cleaning. With Snowflake's simple ETL and ELT options, data engineers can spend more time focusing on essential data strategy and pipeline improvement initiatives. Furthermore, using the Snowflake Cloud Platform as your data lake and data warehouse, extract, convert, and load may be efficiently avoided, as no pre-transformations or pre-schemas are needed.
Following are the best ETL tools for Snowflake
Snowflake is built entirely on a SQL database. It’s a columnar-stored relational database that works well with Excel, Tableau, and many other tools. Snowflake contains its query tool, supports multi-statement transactions, role-based security, etc., which are expected in a SQL database.
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Snowflake supports the most common standardized version of SQL, i.e., ANSI for powerful relational database querying.
Snowflake stores the data in multiple micro partitions which are internally optimized and compressed. The data is stored in a columnar format in the cloud storage of Snowflake. The data objects stored by Snowflake cannot be accessed or visible to the users. By running SQL query operations on Snowflake, you can access them.
Snowflake offers four editions depending on your usage requirements.
In Snowflake, a virtual warehouse, often known as a "warehouse," is a collection of computational resources. A virtual warehouse provides the resources required for the users like CPU, memory, and temporary storage to perform multiple Snowflake operations:
An OLTP (Online Transactional Processing) database contains detailed and up-to-date data, as well as a large volume of typically small data transactions. In turn, online analytical processing (OLAP) often necessitates complex and aggregated queries with a small number of transactions. Snowflake's database schema is built around online analytical processing.
The columnar database is opposite to the conventional databases. It saves the data in columns in place of rows, eases the method for analytical query processing and offers more incredible performance for databases. Columnar database eases analytics processes, and it is the future of business intelligence.
|Related Article - Snowflake vs Redshift|
Whenever we load the data into the Snowflake, it organizes the data into the compressed, columnar, and optimized format. Snowflake deals with storing the data that comprises data compression, organization, statistics, file size, and other properties associated with the data storage. All the data objects we store in the Snowflake are inaccessible and invisible. We can access the data objects by executing the SQL query operation through Snowflake.
In Snowflake, Virtual warehouses perform all the data handling tasks. Which are multiple clusters of the compute resources. While performing a query, virtual warehouses extract the least data needed from the storage layer to satisfy the query requests.
We can access the Snowflake data warehouse through:
Snowflake is highly successful because of the following reasons:
Data security plays a prominent role in all enterprises. Snowflake adapts the best-in-class security standards for encrypting and securing the customer accounts and data that we store in the Snowflake. It provides the industry-leading key management features at no extra cost.
For managing today’s data analytics, companies rely on a data platform that offers rapid deployment, compelling performance, and on-demand scalability. Snowflake on the AWS platform serves as a SQL data warehouse, which makes modern data warehousing effective, manageable, and accessible to all data users. It enables the data-driven enterprise with secure data sharing, elasticity, and per-second pricing.
Definitely. AWS glue presents a comprehensive managed environment that easily connects with Snowflake as a data warehouse service. These two solutions collectively enable you to handle data ingestion and transformation with more ease and flexibility.
Snowflake comes along with a robust and unique kind of data partitioning known as micro partitioning. Data that exists in the Snowflake tables are systematically converted into micro partitions. Generally, we perform Micro partitioning on the Snowflake tables.
Both Redshift and Snowflake provide on-demand pricing but vary in package features. Snowflake splits compute storage from usage in its pricing pattern, whereas Redshift integrates both.
|Snowflake is a comprehensive SaaS solution that requires no maintenance.||AWS Redshift clusters necessitate some manual maintenance.|
|Snowflake separates computing and storage, allowing for customizable pricing and setup.||Reserved/Spot instance price in Redshift provides for cost optimization.|
|Snowflake uses real-time auto-scaling.||Redshift, on the other hand, involves the addition and removal of nodes in order to scale.|
|Snowflake provides less data customisation options.||Where Redshift facilitates data flexibility with features such as partitioning and distribution.|
|Snowflake provides always-on encryption with strict security checks.||While Redshift offers a flexible, customised security strategy.|
Snowpipe is Snowflake's continuous data ingestion service. Snowpipe loads data in minutes once files are uploaded to a stage and submitted for ingestion. Snowflake maintains load capacity with Snowpipe's serverless compute approach, assuring appropriate compute resources to meet demand. In a nutshell, Snowpipe provides a "pipeline" for loading new data in micro-batches as soon as it becomes available.
The data is loaded using the COPY command defined in a connected pipe. Snowpipe can use a pipe, which is a named, first-class Snowflake object containing a COPY statement. The COPY statement specifies the location of the data files (i.e., a stage) as well as the target table. All data types, including semi-structured data types like JSON and Avro, are supported.
There are several ways for detecting staged files:
The Snowpipe benefits are as follows:
In Snowflake, a schema is a logical grouping of database objects such as tables, views, etc. The snowflake schema is made up of fact tables that are centralised and linked to multiple dimensions. A Snowflake Schema is a dimension-added extension of a Star Schema. The dimension tables have been normalized, resulting in the data being split into additional tables.
The benefits of using Snowflake schemas are it provides structured data and uses small disk space.
An example of Snowflake Schema is shown below:
Both Snowflake and Star Schemas are identical, yet the difference exists in dimensions. In Snowflake, we normalize only a few dimensions, and in a star schema, we denormalise the logical dimensions into tables.
|Star Schema||Snowflake Schema|
|The fact tables and dimension tables are both contained in the star schema.||The fact tables, dimension tables, and sub dimension tables are all contained in the snowflake schema.|
|The star schema is a top-down model.||While it is a bottom-up model.|
|The star schema takes up more space.||While it takes up less space.|
|Queries are executed in less time.||Here query execution takes longer than with the star schema.|
|Normalization is not employed in the star schema.||Both normalisation and denormalization are employed in this.|
|It has a very simple design.||While its design is complex.|
|Star schema has a low query complexity.||Snowflake schema has a higher query complexity than star schema.|
|It contains fewer foreign keys.||It has a larger number of foreign keys.|
|It has a high level of data redundancy.||While it has a minimal level of data redundancy.|
Snowflake Time Travel tool allows us to access the past data at any moment in the specified period. Through this, we can see the data that we can change or delete. Through this tool, we can carry out the following tasks:
|According to the Snowflake edition, account or object particular time travel setup, users can retrieve and set the data reverting to the history.||Fail-Safe, the User does not have control over the recovery of data valuable merely after completing the period. In this context, only Snowflake assistance can help for 7 days. Therefore if you set time travel as six days, we retrieve the database objects after executing the transaction + 6 days duration.|
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Zero copy cloning is a snowflake implementation in which a simple keyword CLONE allows you to generate a clone of your tables, schemas, and databases without replicating the actual data. As a result, you can have practically real-time data from production cloned into your dev and stage environments to conduct various activities.
The data retention period is an important aspect of Snowflake Time Travel.
When data in a table is modified, such as deletion or discarding an object holding data, Snowflake saves the data's previous state. The data retention period determines the number of days that this historical data is kept and, as a result, Time Travel operations (SELECT, CREATE... CLONE, UNDROP) can be performed on it.
The standard retention period is one day (24 hours) and is enabled by default for all Snowflake accounts.
SnowSQL is the command-line client used to connect to Snowflake and conduct SQL queries as well as complete all DDL and DML actions such as loading and unloading data from database tables.
SnowSQL (snowsql executable) can be operated as an interactive shell or in batch mode via stdin or with the -f option.
The Snowflake connector is a piece of software that allows us to connect to the Snowflake data warehouse platform and conduct activities such as Read/Write, Metadata import, and Bulk data loading.
The Snowflake connector can be used to execute the following tasks:
Following are the types of Snowflake Connectors:
Views are useful for displaying certain rows and columns in one or more tables. A view makes it possible to obtain the result of a query as if it were a table. The CREATE VIEW statement defines the query. Snowflake supports two different types of views:
In Snowflake, data partitioning is called clustering, which specifies cluster keys on the table. The method by which you manage clustered data in a table is called re-clustering.
A clustering key is a subset of columns in a table (or expressions on a database) that are deliberately intended to co-locate the table's data in the same micro-partitions. This is beneficial for very large tables where the ordering was not perfect (at the time the data was inserted/loaded) or if extensive DML has weakened the table's natural clustering.
Some general indicators that can help determine whether a clustering key should be defined for a table are as follows:
Snowflake Data sharing allows organizations to securely and immediately share their data. Secure data sharing enables sharing of the data between the accounts through Snowflake secure views, database tables.
|Also, Read - Databricks vs Snowflake|
No, Snowflake does not use indexes. This is one of the aspects that set the Snowflake scale so good for the queries.
Snowflake systematically creates metadata for the files in the external or internal stages. We store metadata in the virtual columns, and we can query through the standard “SELECT” statement.
In Snowflake, stages are data storage locations. If the data to be imported into Snowflake is stored in another cloud area, such as AWS S3, Azure, or GCP, these are referred to as External stages; if the data is stored within Snowflake, they are referred to as Internal stages.
Internal Stages are further divided as below
Stored procedures allow us to create modular code comprising complicated business logic by adding various SQL statements with procedural logic. For executing the Snowflake procedure, carry out the below steps:
All the data we enter into the Snowflake gets compacted systematically. Snowflake utilizes modern data compression algorithms for compressing and storing the data. Customers have to pay for the packed data, not the exact data.
Following are the advantages of the Snowflake Compression:
To create a Snowflake task, we have to use the “CREATE TASK” command. Procedure to create a snowflake task:
To create temporary tables, we have to use the following syntax:
Create temporary table mytable (id number, creation_date date);
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If you have applied for a job as a Snowflake Developer or Administrator, here are some tips you need to remember:
Make sure you do your research on the company before heading to an interview.
Many Snowflake job seekers, despite passing their certification exams, fail to land well-paying jobs because they make broad comments and speak in generic terms when describing their accomplishments. Make sure you prepare particular facts and speak about details to distinguish yourself apart. Ensure you have facts and figures to back up what you've done in previous jobs.
Prepare yourself for the fact that Snowflake interview questions won’t necessarily be a walk in the park. At first, you'll be asked basic questions, but as the interview proceeds, you'll be asked in-depth technical questions about the position you've applied for.
Prepare thoroughly with all of the necessary Snowflake concepts, such as data warehouse, data integration, and more. Your answer should also include any specific tools or technical competencies demanded by the job you’re interviewing for. Review the job description, and if there are any tools or software you haven't used previously, it's a good idea to familiarise yourself with them before the interview.
Employees who can successfully express technical concepts are highly valued by employers. Communication is a crucial skill, and even if you're a technical guru, if you can't communicate well with others, it’s going to be a major disadvantage.
The majority of Snowflake interview questions will be broad rather than specific. As a result, you must ensure that you are familiar with a wide range of services that may be asked about. Make sure you understand how the Snowflake services and features work, as well as how they can help businesses.
You can prepare all you want, but still won't be confident on the big day! This could lead to missing out on the job you've wanted.
Good intuition for data and data architecture
So, demonstrate your hunger for a Snowflake career by following any of the above methods, instil passion in yourself, and you'll be able to land your dream job.
There may be additional steps during your interview process, depending on the team and role you apply for. If you pass the interview, you will be hired.
You could crack the Snowflake interview through proper practice and preparing through the right materials. In order to get a good mastery of Snowflake, get yourself registered for a course on Snowflake.
It's important to get training that covers both the lab and theory thoroughly. Interaction with a tutor, mentor support, and improved collaboration using online tools should all be included. You can find all the skills you need with MindMajix’s Snowflake Training.
The interview procedure may differ depending on the role and team of the company you apply for. Based on the experience of previous candidates, the hiring process can be broken down into 5 steps, and reportedly ranges from one to four weeks.
Snowflake's popularity as a top cloud data warehousing solution stems from the fact that it covers a wide range of areas, including business intelligence, data integration, advanced analytics, security and governance. It supports programming languages such as Go, Java, Python, and others. It has out-of-the-box features like storage and computation isolation, on-the-fly scalable compute, data sharing, data cloning, and more.
The demand for Snowflake professionals is at an all-time high, and it's only getting higher. In recent years, the industry has experienced tremendous growth in Snowflake’s job postings. It is expected that there will be even more opportunities in the near future.
Snowflake offers a rewarding career path; even the simplest of jobs would earn $88k per year, with the highest paying jobs reaching $159k. Talking about India only, the average salary is roughly ₹24.2lakhs per annum. This salary is not stationary, it continuously evolves since this technology is hot and in high demand.
Formal education is mandatory to break down into a data sector. A bachelor’s degree in Computer Science, Business Administration or a related field is a fundamental prerequisite. Besides academic skills, the job of a Snowflake Developer demands a lot. A Snowflake Developer must possess the following skills:
A Snowflake developer is responsible for designing, developing and managing secure and scalable Snowflake solutions to drive business objectives. Snowflake developers are expected to be familiar with core Snowflake principles and follow best practices.
Snowflake offers various certifications based on the role to grow your career. Below you will find details about the certifications offered by Snowflake.
Getting Snowflake Certified can help you advance your career, whether you're seeking for a new role, showcasing your talents for a new project, or becoming the go-to expert on your team.
A Snowflake Developer’s specific tasks vary greatly depending on the industry they’re in and the company they work for. Generally speaking, a Snowflake developer might expect to encounter some or all of the tasks and responsibilities listed below.
Career options in Snowflake are plenty as the entire economy pivots on data. So, let's have a look at the various Snowflake job profiles:
Your response to this question will reveal a lot about how you view your role and the value you offer to a company to a hiring manager. You might mention how Snowflake necessitates a unique set of competencies and skills in your response. A good Snowflake Developer must be able to mix technical skills like parsing data and building models with business sense like understanding the challenges they're solving and recognising actionable insights in their data.
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Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .
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