Snowflake SnowPro Interview Questions

The SnowPro Core Certification candidates must demonstrate their ability to apply specialized core expertise when implementing and migrating to Snowflake. A SnowPro Core Certified expert should have a complete grasp of Snowflake as a cloud data warehouse, as well as the skills to design, develop, and manage secure, scalable Snowflake solutions to meet corporate goals. This exam covers Snowflake architectural concepts and best practices, as well as key components of Snowflake solutions, such as data loading and unloading, performance and concurrency, scaling, security, data types, connectors, and account management.

Candidates who want to demonstrate their expertise of Snowflake, the Data Platform, should take the SnowPro Core Certification test. The candidate should be well-versed in the following topics:

• How to use Snowflake to load and transform data
• Scaling Virtual Warehouses for Performance and Concurrency 
• Snowflake Account Management and Monitoring
• Working with Data That Isn’t Well-Structured Snowflake’s Continuous Data Protection Method
• Utilize Data Sharing
• Query Constructs and DDL & DML Operations

Advanced Interview Questions

What is Snowflake and how does it differ from traditional data warehousing solutions?

Snowflake is a cloud-based data warehousing solution designed to handle large amounts of structured and semi-structured data. It differs from traditional data warehousing solutions in several key ways:

1. Scalability: Snowflake can scale up or down as needed, without any downtime or manual intervention, providing unlimited storage and computing resources.
2. Architecture: Snowflake uses a unique architecture based on a combination of microservices, columnar storage, and an automatic and elastic data cache.
3. Multi-cloud: Snowflake supports deployment in multiple cloud environments, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
4. Data Sharing: Snowflake supports secure and controlled data sharing between different organizations, departments, and users.
5. Cost Model: Snowflake charges for usage only, based on the number of virtual warehouses used and the amount of data stored and processed, eliminating upfront hardware and infrastructure costs.
6. Separation of storage and compute: Snowflake separates storage and compute, allowing for more efficient usage of resources and making it easier to manage costs.

In summary, Snowflake is a modern, cloud-based, scalable, and flexible data warehousing solution that offers a number of advantages over traditional on-premise data warehousing solutions.

What are the key features of Snowflake?

Snowflake is a cloud-based data warehousing platform that offers a number of key features designed to provide a flexible, scalable, and secure solution for modern data warehousing needs. Some of the most notable features of Snowflake include:

1. Scalability: Snowflake is designed to scale both horizontally and vertically, allowing users to easily add more storage and compute resources as needed. This means that Snowflake can handle very large amounts of data, and support demanding workloads, without sacrificing performance or reliability.
2. Multi-cloud architecture: Snowflake allows users to store data and run queries in any cloud, including AWS, Azure, or Google Cloud. This allows organizations to take advantage of the best features and services offered by each cloud provider, and to easily move data and workloads as needed.
3. Security: Snowflake includes a number of security features, such as encryption of data at rest and in transit, as well as role-based access control, to ensure that sensitive data is always protected.
4. Performance optimization: Snowflake uses a number of techniques to optimize query performance, such as automatic data clustering and data pruning. Additionally, Snowflake’s columnar storage architecture and its use of modern hardware make it much faster than traditional data warehousing solutions.
5. Data sharing: Snowflake allows users to share data with others within their organization, or with external partners, in a secure and controlled manner. This makes it easy to collaborate on data-driven projects, and to share data insights across the enterprise.
6. Data warehousing as a service: With Snowflake, organizations can rent a data warehousing solution on a per-second basis, without having to invest in expensive hardware or manage complex software. This makes it much easier and more cost-effective to get started with data warehousing, and to scale up as needed.
7. Integration with other tools and services: Snowflake integrates with a number of other tools and services, including data ingestion and data visualization tools, to make it easier to work with data in the cloud. Additionally, Snowflake provides a number of APIs and connectors that make it easy to integrate with other applications and services.

How does Snowflake handle data ingestion and loading?

Snowflake is a cloud-based data warehousing platform that provides a number of options for data ingestion and loading. Here is how Snowflake handles data ingestion and loading:

1. Data Ingestion: Snowflake allows data to be ingested from a variety of sources including local files, cloud-based data sources such as Amazon S3 and Microsoft Azure, and real-time data sources such as Kafka and Kinesis. The data can be in different formats including CSV, JSON, Avro, and Parquet.
2. File Upload: Snowflake provides a web interface to upload data files directly into a table. This is a simple and fast option to get small to medium-sized datasets into Snowflake.
3. Data Loader: Snowflake provides a data loading tool called Snowflake Data Loader that enables the ingestion of large amounts of data into Snowflake. The tool can be run in a number of ways, including through a command line interface, as a scheduled task, or as part of a custom script.
4. External Tables: Snowflake also allows external tables to be created that reference data stored in external sources such as S3, Azure Blob Storage, or Google Cloud Storage. These tables provide a convenient way to access data stored in the cloud, without having to load it into Snowflake.
5. Streaming: Snowflake provides a streaming data ingestion option that enables real-time data to be ingested into Snowflake as it is generated. Snowflake supports real-time data ingestion from sources such as Kafka and Kinesis, and can process data as soon as it is available.
6. Load Balancing: Snowflake uses a unique data loading architecture that distributes data across multiple compute nodes to ensure fast and efficient ingestion of large amounts of data. Snowflake automatically balances the load across multiple compute nodes, which helps ensure a fast and consistent data ingestion experience.

In conclusion, Snowflake provides multiple options for data ingestion and loading, enabling organizations to easily and efficiently bring data into the platform, regardless of the size or complexity of the data.

What are the various ways to connect to Snowflake?

Snowflake provides multiple ways to connect to the platform:

1. Snowflake Web UI: A web-based user interface that allows users to interact with Snowflake and perform various tasks, such as querying data, managing data, and monitoring performance.
2. Snowflake JDBC/ODBC Driver: Snowflake provides JDBC and ODBC drivers that allow users to connect to Snowflake using popular Business Intelligence (BI) tools, such as Tableau, PowerBI, and QlikView.
3. Snowflake Python Connector: A Python library that provides a Python interface for connecting to Snowflake and executing queries.
4. Snowflake REST API: A REST API that allows users to access Snowflake programmatically using HTTP requests.
5. Snowflake CLI: A command-line interface (CLI) that provides a terminal-based interface for connecting to Snowflake and performing various tasks, such as querying data and managing data.
6. Snowflake Partner Connectors: Snowflake also provides pre-built connectors to various cloud data sources, such as Amazon S3 and Microsoft Azure, that simplify the process of ingesting data into Snowflake.

These different connection options provide a wide range of flexibility for connecting to Snowflake, regardless of the platform, tool, or language being used.

How does Snowflake handle data storage and retrieval?

Snowflake handles data storage and retrieval through a combination of advanced cloud-based technologies and sophisticated data warehousing techniques.

1. Columnar storage: Snowflake stores data in a columnar format, as opposed to a row-based format. This allows for efficient data compression and retrieval, as only the relevant columns are retrieved, reducing data retrieval time and storage space.
2. Micro-partitioning: Snowflake divides large tables into smaller partitions, called micro-partitions, which are optimized for individual data retrieval. This enables fast and efficient retrieval of data for specific queries, without having to scan through the entire table.
3. Cloud storage: Snowflake uses Amazon S3 or Microsoft Azure for data storage. This allows for unlimited scalability and flexibility in data storage, as Snowflake can dynamically allocate and de-allocate storage as needed.
4. Data Compression: Snowflake uses various types of data compression, such as Snappy and Zstandard, to reduce the storage footprint of the data. This helps reduce the storage costs and improve the performance of data retrieval.
5. Query optimization: Snowflake uses a query optimizer that evaluates the most efficient way to retrieve data based on the specific query being run. It uses data statistics, index information, and query patterns to optimize the query and minimize the data retrieval time.
6. Virtual Warehouses: Snowflake’s virtual warehouses enable users to provision and manage resources for data retrieval. These virtual warehouses can be scaled up or down based on the data retrieval needs, providing a flexible and cost-effective solution for data retrieval.
7. Zero-Copy Cloning: Snowflake’s zero-copy cloning feature allows users to create a new table from an existing table, without copying the data. This reduces the data retrieval time and storage costs, as only the metadata is duplicated, not the actual data.

Overall, Snowflake’s combination of columnar storage, micro-partitioning, cloud storage, data compression, query optimization, virtual warehouses, and zero-copy cloning make it an efficient and effective solution for data storage and retrieval.

Can you explain Snowflake’s multi-cloud architecture and its benefits?

Snowflake’s multi-cloud architecture enables customers to store and process data on multiple public cloud platforms, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). This architecture provides a number of benefits:

1. Cloud Platform Agnosticism: Snowflake’s multi-cloud architecture allows customers to choose the cloud platform that best meets their needs, without being locked into a single provider.
2. Hybrid Deployments: Snowflake’s architecture supports hybrid deployments, allowing customers to store and process data across multiple public cloud platforms and on-premises data centers.
3. Increased Resilience: Snowflake’s multi-cloud architecture provides increased resilience and high availability, as data can be stored across multiple cloud platforms to mitigate the risk of outages or data loss.
4. Improved Performance: Snowflake’s architecture can optimize performance by storing data closer to where it is being processed, reducing latency and increasing performance.
5. Cost Optimization: Snowflake’s multi-cloud architecture allows customers to take advantage of the best pricing and service offerings from multiple cloud providers, reducing overall costs.
6. Flexible Data Management: Snowflake’s architecture provides customers with the flexibility to store and process data in the cloud platform of their choice, allowing them to take advantage of the specific features and services offered by each provider.

Overall, Snowflake’s multi-cloud architecture provides customers with the ability to store and process data in the cloud platform that best meets their needs, while providing increased resilience, improved performance, cost optimization, and flexible data management.

What is Snowflake’s security model?

Snowflake’s security model is based on the principle of least privilege, where each user or role is granted only the minimum permissions necessary to perform their job. The security model includes the following features:

1. Authentication: Snowflake supports authentication via a variety of methods, including single sign-on (SSO) and multi-factor authentication (MFA).
2. Authorization: Snowflake implements role-based access control (RBAC), where each user is assigned one or more roles that determine the actions they can perform within the system.
3. Data encryption: Snowflake encrypts all data at rest and in transit, using industry-standard encryption algorithms. The encryption keys are managed by Snowflake and are separate from the data, ensuring that only authorized users can access the data.
4. Virtual Private Snowflake (VPS): VPS is a feature that allows Snowflake customers to create isolated, dedicated environments within Snowflake for their data. This provides an extra layer of security and privacy.
5. Auditing and Compliance: Snowflake maintains detailed logs of all activity within the system, allowing customers to monitor and auditing activity, comply with regulations and track data lineage.

In addition to these features, Snowflake is also SOC 2, SOC 3, and PCI DSS compliant, demonstrating its commitment to data security and privacy.

What are the different types of users in Snowflake and their roles and responsibilities?

Snowflake has four main types of users:

1. Account User: These are the users who have been granted access to the Snowflake account by an administrator. They are responsible for managing the account, such as creating and deleting databases, schemas, and tables. They also have the ability to grant and revoke access to other users.
2. Database User: This type of user has been granted access to a specific database within the Snowflake account. They are responsible for managing the data within the database, such as adding new tables, creating views, and modifying data. They also have the ability to grant and revoke access to other users.
3. Schema User: This type of user has been granted access to a specific schema within a database. They are responsible for managing the tables within the schema, such as creating and modifying tables, creating views, and modifying data. They also have the ability to grant and revoke access to other users.
4. Role User: This type of user is assigned a specific role within Snowflake, such as a data analyst or data scientist. Roles define the level of access and responsibilities a user has within Snowflake. For example, a data analyst role may have access to only read data, while a data scientist role may have the ability to modify data as well.

In summary, the different types of users in Snowflake have different levels of access and responsibilities, ranging from managing the entire account to managing specific data within a database or schema. It is important for administrators to properly manage and assign user roles to ensure data security and compliance.

How does Snowflake handle data compression and encryption?

Snowflake handles data compression and encryption to maximize storage efficiency and protect sensitive data.

Data compression in Snowflake is achieved through a columnar storage format that compresses data at the column level, as opposed to row level compression used in traditional data warehousing solutions. This results in a higher level of compression and faster query performance. Snowflake supports multiple compression algorithms, including Snappy, ZSTD, and LZO, allowing users to choose the one that best fits their data and performance needs.

Snowflake also provides robust data encryption capabilities to protect sensitive data both in storage and during transmission. Snowflake supports both encryption-at-rest and encryption-in-transit using the Advanced Encryption Standard (AES) 256-bit encryption algorithm. In addition, Snowflake also supports customer-managed keys for encryption-at-rest, allowing customers to manage the encryption keys themselves and meet specific regulatory requirements.

Overall, Snowflake’s approach to data compression and encryption helps organizations store and process large amounts of data efficiently while maintaining the security and privacy of sensitive data.

What is Snowflake’s approach to data warehousing performance optimization?

Snowflake approaches data warehousing performance optimization through a combination of several key techniques. These include:

1. Separation of storage and compute: Snowflake separates storage and compute functions, so data is only processed when needed, reducing query processing time.
2. Automatic indexing: Snowflake automatically creates indexes on the fly based on query patterns, improving query performance.
3. Query optimization: Snowflake has a sophisticated query optimization engine that uses machine learning algorithms to identify the most efficient query execution plan.
4. Columnar storage: Snowflake stores data in a columnar format, which allows for faster query execution as the system only needs to read the relevant columns, not the entire table.
5. Materialized views: Snowflake provides a feature called Materialized Views that allows pre-aggregated data to be stored and queried, improving query performance.
6. Clustering: Snowflake clusters data based on frequently accessed columns, further improving query performance.
7. Micro-partitioning: Snowflake partitions data at a fine-grained level, allowing for highly parallel processing and improved query performance.
8. Parallel processing: Snowflake leverages its multi-cloud architecture to distribute data and processing across multiple nodes in parallel, resulting in improved performance for large and complex queries.

These optimizations, along with Snowflake’s highly scalable infrastructure, allow organizations to process large amounts of data quickly and efficiently, and make data warehousing more accessible to organizations of all sizes.

Basic Interview Questions

1.) What are the key characteristics of Snowflake?

The following are the main characteristics of Snowflake:

• Snowflake provides a web interface for interacting with the data cloud. Users can use the online GUI to manage their accounts, monitor resources, and run system use queries, among other things.
• A variety of client connectors and drivers are available to connect to Snowflake’s data cloud. Python Connector (an interface for creating Python applications to connect to Snowflake), Spark connector, NodeJS driver,.NET driver, JBDC driver for Java development, ODBC driver for C or C++ programming, and others are among these connectors.

• Snowflake’s primary architecture allows it to run on public clouds, where it uses virtualized CPU instances and efficient storage buckets to process massive amounts of big data in a cost-effective and scalable manner.
  Snowflake interfaces with a variety of big data tools, including BI, machine learning, data integration, security, and governance.
  Snowflake outperforms typical data warehousing solutions with enhanced characteristics like simplicity, increased performance, high concurrency, and profitability.
  Both organized and semi-structured data can be stored in Snowflake (such as JSON, Avro, ORC, Parquet, and XML data).
  Snowflake automates cloud data management, security, governance, availability, and data resilience, which results in lower costs, no downtime, and improved operational efficiency.
• It allows users to quickly query data from a database without affecting the underlying dataset. As a result, they can receive data that is closer to real-time.
  The Snowflake data warehouse supports most SQL DDL (Data Definition Language) and DML (Data Manipulation Language) commands. Advanced DML, lateral views, transactions, stored procedures, and other features are also available.

2.) Explain the structure of a snowflake.

To combine the best of both worlds, the Snowflake architecture is a combination of shared-disk (all computer nodes share a common disc or storage device) and shared-nothing (each computing node has its own memory and storage space). Snowflake stores permanent data in a central data repository that is accessible to all compute nodes, similar to a shared-disk architecture.

Snowflake uses massively parallel computing (MPP) clusters for query processing, similar to shared-nothing architectures, in which each node keeps a portion of the entire data set locally.

As indicated below, the Snowflake architecture is divided into three major layers:

• Database Storage Layer: After data is put into Snowflake, this layer reorganizes it into a specified format, such as columnar, compressed, or optimized. The optimized data is saved on the cloud.
• Query Processing Layer: Virtual warehouses are used to run queries in the processing layer. Snowflake allocates virtual warehouses from cloud providers as independent MPP (Massively Parallel Processing) compute clusters with numerous compute nodes.
• Because virtual warehouses do not share computational resources, their performance is unaffected by the performance of other virtual warehouses.
• Access control, authentication, metadata management, infrastructure management, query parsing, optimization, and many more services are provided by the Cloud Services Layer to administer and govern a Snowflake data cloud.

3.) What exactly do you mean when you say “virtual warehouse”?

A virtual warehouse is essentially a collection of computer resources (such as CPU, memory, and solid-state drives) that customers may use to run queries, load data, and do other DML and SQL (Structured Query Language) processes.

It provides memory, temporary storage, and CPU resources, for example, that can be utilised for DML and SQL operations.

It provides memory, temporary storage, and CPU resources, for example, that can be utilised for DML and SQL operations. This autonomous compute cluster can be used at any moment and then turned off when not in use. You are charged (paid) for each virtual warehouse you run, as well as the size and duration of those virtual warehouses. Because virtual warehouses do not share computational resources, their performance is unaffected by the performance of other virtual warehouses.

Different groups of users can be assigned separate and dedicated virtual warehouses, as shown in the diagram below. As a result, ETL operations can load and perform sophisticated transformation procedures on various warehouses in real time, guaranteeing that data scientists and finance reports are not impacted.

4.) Is it possible for you to inform me how to get access to the Snowflake Cloud data warehouse?

The data warehouse at Snowflake can be accessed in the following ways:

• Drivers for ODBC (a driver for connecting to Snowflake).
• Drivers for JDBC (a driver enabling a Java application to interact with a database).
• Python Packages (for creating Python applications that connect to Snowflake and perform standard operations).
• User Interface for the Web (can be used for almost any task you can accomplish with SQL and the command line, such as: Creating and managing users and other account-level objects).
• SnowSQL Command-Line Client is a command-line client for SnowSQL (Python-based command-line interface to connect to Snowflake from Windows, Linux, and MacOS).

5.) In Snowflake, explain the steps.

Data is saved in stages in Snowflake, and staging is the act of uploading data into a stage. Data that needs to be loaded or saved in Snowflake is either stored somewhere in the cloud, such as in AWS S3, GCP (Google Cloud Platform), or Azure, or it is stored internally within Snowflake. An external stage is when data is stored outside another cloud region, whereas an internal stage is when data is stored within a snowflake. Internal stages are further divided into the following categories:

• User stages: Because each of these stages is associated with a distinct user, they will be assigned to all users by default for file storage.
• Table stages: Because each of these stages is specific to a database table, they’ll be allocated to all tables by default.
  Internal phases with names: These stages provide a greater degree of freedom than the user or table phases. All actions that may be performed on objects can also be performed on internally named stages, as these are some of the Snowflake objects. These stages must be manually constructed, and we can specify file formats when doing so.

6.) Explain Snowpipe.

In simple terms, Snowpipe is a Snowflake continuous data ingestion service that loads files within minutes of being added to a stage and submitted for ingestion.

As a result, rather than manually performing COPY statements on a schedule to load big batches, you may load data from files in micro-batches (organising data into small groups/matches), allowing users to access the data within minutes (extremely fast response time). Snowpipe makes it easier to evaluate data by dividing it into micro-batches. Snowpipe ensures that only new data is processed by combining filenames and file checksums.

Snowpipe’s Benefits –

• Snowpipe allows real-time analytics by removing obstacles.
• It is a cost-effective solution.
• It’s quite easy to use.
• There is no need for management.
• It provides scalability, resilience, and other benefits.

7.) What exactly do you mean when you say Snowflake Computing?

Snowflake computing refers to Snowflake’s capacity to deliver immediate, secure, and controlled access to all data networks, as well as its fundamental architecture, which supports a variety of data workloads and provides an uniform platform for modern data applications. Snowflake does not employ a database or a “big data” software platform like Hadoop, unlike other data warehouses. Snowflake, on the other hand, combines a completely new SQL query engine with a cloud-native architecture.

8.) Snowflake currently supports which cloud platforms?

The following cloud platforms are currently supported by Snowflake:

• Amazon Web Services (AWS)
• Google Cloud Platform (GCP)
• Microsoft Azure (Azure).

9.) How is data and information protected?

Data security is one of the top responsibilities for every firm. To secure and safeguard customer data, the Snowflake platform adheres to the industry’s top security standards. At no additional cost, the platform provides the best key management features. Snowflake uses the following security procedures to secure customer data:

• Snowflake uses a managed key to automatically encrypt the data it holds.
• To enhance data security between consumers and servers, Snowflake uses Transport Layer Security (TLS).
• You can choose a geographic area to store your data based on your cloud region.

10.) Is Snowflake a tool for extracting, transforming, and loading data?

Snowflake is classified as an ETL (Extract, Transform, and Load) tool since it involves three steps:

• The information is extracted from the source and saved in a variety of file formats, including JSON, CSV, XML, and others.
• Loads data into a stage, which can be internal (Snowflake maintained location) or external (not managed by Snowflake) (Microsoft Azure, Amazon S3 bucket, Google Cloud).
• To copy data into the Snowflake database, use the COPY INTO command.

11.) What ETL tools can I use?

The following ETL tools are compatible with Snowflake:

• Hevo Data
• StreamSets
• Etleap
• Apache Airflow
• Matillion
• Blendo, etc.

12.) What does it mean to scale horizontally and vertically?

Horizontal Growing: By scaling horizontally, horizontal scaling enhances concurrency. You can utilise auto-scaling to increase the number of virtual warehouses as your customer base grows, allowing you to answer to more queries quickly.
Vertical scaling is the process of boosting the processing power (CPU, RAM) of an existing computer. It usually entails scaling in order to reduce processing time. If you want to optimise your workload and make it run faster, go with a larger virtual warehouse size.

13.) Is snowflake an OLTP (Online Transactional Processing) system or an OLAP (Online Analytical Processing) system?

Snowflake is an OLAP (Online Analytical Processing) database, not an OLTP (Online Transaction Processing) database. Data is collected, stored, and processed from real-time transactions in OLTP (Online Transaction Processing), but complicated queries are used to assess aggregated historical data from OLTP systems in OLAP (Online Analytical Processing). Snowflake, unlike a transactional database, is not meant to manage a lot of updating and adding of little quantities of data. Snowflake, for example, is unable to manage referential integrity because, while it provides integrity and other constraints, none of them are enforced, with the exception of the NOT NULL constraint, which is always enforced. Other than NOT NULL, other constraints are generated as disabled constraints.

However, depending on the application, we may also utilise it to conduct online transactions (OLTP).

14.) What kind of database is Snowflake?

All of Snowflake’s features are based on SQL databases (Structured Query Language). Data is saved in columns in this relational database system, which is interoperable with other programmes like Excel and Tableau. Snowflake is a SQL database that includes a query tool, enables multi-statement transactions, and has role-based security, among other features.

15.) Explain Snowflake Clustering in a few words.

Clustering is a method of data partitioning in Snowflake that specifies unique cluster keys for each table. Cluster keys are subsets of a table’s columns that are used to group data in the table together. These keys are excellent for tables with a lot of information.

Re-clustering is the process of managing clustered data in a table.

16.) How is data stored? Explain the concept of a columnar database.

Snowflake automatically reorganises data after it is loaded into a compressed, efficient, columnar format (micro-partitions). After that, the data is optimised and saved in the cloud. Snowflake is in charge of all aspects of data storage, such as file structure, size, statistics, compression, metadata, and so on. Customers and users cannot see Snowflake data items. Snowflake users can only access data by running SQL queries. Within the storage layer, Snowflake uses a columnar format to optimise and store data. It stores data in columns rather than rows, enabling for analytical querying and improved database speed.

Business intelligence will be easier and more accurate with columnar databases. When compared to row-level operations, column-level operations are faster and consume fewer resources.

A table with 24 rows separated into four micro-partitions, ordered and sorted by column, as shown above. Snowflake may remove non-relevant micro-partitions first, then prune the remaining micro-partitions per column because the data is separated into micro-partitions. As a result, there are fewer records traversed, resulting in much faster response times.

17.) Explain Schema.

The Snowflake Schema defines how Snowflake organises data. Schemas are a logical organisation of database items (such as tables, views, etc.).

One fact table is related to multiple dimension tables, which are linked to other dimension tables through many-to-one relationships. A snowflake pattern is formed by a fact table (which holds quantitative data for analysis) surrounded by its associated dimensions, which are coupled to other dimensions. A Fact Table, which is a key to a Dimension Table, contains the measurements and facts of a business process, while a Dimension Table stores the attributes of measurements. Snowflake comes with a full range of DDL (Data Definition Language) commands for building and maintaining databases and schemas.

• The snowflake schema has one fact table and two-dimension tables, each having three layers.
• Snowflake schemas can have an infinite number of dimensions and levels for each dimension.
• Star Schema and Snowflake Schema have different states.
• Star and Snowflake schemas are logical descriptions of a database’s whole structure, or how data is organised.
• A star schema is made up of one fact table and many dimension tables that are linked together. The star schema gets its name from the fact that the structure resembles a star. The star schema’s dimensions have been denormalized. Denormalization occurs when the same values appear repeatedly in a table.
• Snowflake Schema: A snowflake schema has a fact table in the middle that is linked to a number of dimension tables. Those dimension tables, in turn, are linked to more dimension tables. Snowflake schemas are data structures that are completely normalised. For each level of hierarchy (city > country > region), separate dimensions tables are employed.

18.) What is the difference between Snowflake Time Travel and Data Retention Period?

Snowflake’s time travel capability allows you to access historical data stored in the Snowflake data warehouse. Consider the case of deleting a table named Employee by accident. It is possible to travel back in time five minutes to retrieve the data you have lost using time travel. Snowflake Time Travel allows you to access data that has been changed or deleted at any point in time. Within a specific/defined time frame, it is capable of doing the following tasks:

• Observing and analysing data alterations and consumption over a period of time.
• Data-related objects (tables, schemas, and databases) that have been mistakenly lost (dropped) are being restored.
• Data duplication (clones) and backup at or before particular times in the past.
• The data moves into Snowflake Fail-safe as soon as the defined/specific amount of time (data retention period) expires, and these actions/tasks are no longer possible.

19.) What is the Data Retention Period?

Snowflake Time Travel relies heavily on the data retention time. Snowflake preserves the state of data before it was updated when data in a table is modified, such as when data is deleted or objects containing data are removed. Data retention determines how long past data will be kept in order to conduct Time Travel operations (SELECT, CREATE, CLONE, UNDROP, and so on). The default retention duration for all Snowflake accounts is one day (24 hours). The default data retention duration for standard purposes is one day, whereas it ranges from 0 to 90 days for enterprise editions and higher accounts.

20.) Describe the term “fail-safe.”

As a fail-safe feature, Snowflake provides a default 7-day timeframe during which previous data can be retrieved. The fail-safe default term begins once the Time Travel data retention period has expired. Data recovery via fail-safe is done with the best of intentions and only after all other methods have been explored. It could be used by Snowflake to recover data that has been lost or destroyed as a result of severe operational failures. Fail-safe data recovery might take anywhere from a few hours to many days.

21.) Can you describe the differences between Snowflake and AWS (Amazon Web Service)?

Snowflake and Amazon Redshift, for example, are cloud-based data warehouse technologies that offer exceptional performance, scalability, and business intelligence tools. Both platforms offer similar basic features, such as relational administration, security, scalability, and cost efficiency, among other things. However, there are some distinctions between them, including pricing, user experience, and deployment possibilities.

Snowflake is a comprehensive SaaS (Software as a Service) offering that requires no maintenance. AWS Redshift clusters, on the other hand, require manual maintenance.
Snowflake’s security methodology employs always-on encryption to enforce stringent security checks, whereas Redshift employs a more flexible and adaptable approach.

Snowflake’s storage and compute are totally separate, which means that storage costs are comparable to those of S3. AWS, on the other hand, uses a Red Shift spectrum to get around this difficulty and allows you to query data that is immediately available in S3. Despite this, it isn’t as perfect as Snowflake.

22.) Is it possible to connect AWS glue to Snowflake?

Yes, you may use AWS glue to connect the Snowflake. As a data warehouse service, AWS glue integrates smoothly with Snowflake and provides a fully managed environment. Data intake and transformation become easier and more versatile when these two systems are combined.

23.) Explain how Snowflake’s data compression works and what the benefits are.

The encoding, restructuring, or other alterations required to reduce the size of data are an important part of data compression. Data is systematically compacted as soon as it is entered into Snowflake (compressed). Modern data compression technologies are used to compress and store the data in Snowflake. Snowflake is unique in that it charges users based on the size of their data after compression, rather than the actual data.

The following are some of the benefits of snowflake compression:

• When compared to traditional cloud storage, compression reduces storage expenses.
• Storage expenses are not incurred by on-disk caches.
• In general, there are no storage costs associated with data sharing and cloning.

24.) Write the kind of Snowflake caching and explain it.

Take the case of a query that takes 15 minutes to perform or execute. Now, if you ran the same query again with the same commonly used data, you’d be repeating the same effort and wasting resources.
Alternatively, Snowflake caches (stores) the results of each query you run, so whenever you submit a new query, it checks to see if a matching query already exists, and if it does, it uses the cached results instead of running the new query again. Query speeds are substantially decreased thanks to Snowflake’s ability to retrieve results directly from the cache.

Snowflake’s Caching Types

• Caching of Query Results: It saves the results of all queries run in the previous 24 hours.
• Local Disk Caching (LDC) is a technique for storing data that is utilised or required when running SQL queries. It’s also known as Remote Disk Cache since it saves results for later use.

25.) What are the differences between the various editions?

Snowflake has a variety of versions to suit your organization’s needs. Every subsequent edition either introduces new features or provides a higher level of service. It’s simple to swap editions as your company’s demands evolve.

Some of the Snowflake Editions are as follows:

• Normal Edition: This is Snowflake’s most basic package, giving you full access to all of the software’s standard features. This edition hits an excellent mix between features, support, and pricing.
• Enterprise Edition: Enterprise Edition includes all of the features and services of Standard Edition, as well as some additional features tailored to the needs of large businesses.

• Business-critical Edition: Formerly known as Enterprise for Sensitive Data (ESD), Business Critical Edition provides even higher levels of data security for enterprises that handle very sensitive information. This package offers all of the Enterprise Edition’s features and services, as well as better security and data protection.
• VPS (Virtual Private Snowflake): This edition provides the highest level of security for organisations with stringent security requirements, such as financial institutions and companies that collect, analyse, and share extremely sensitive data.

26.) What do you mean by zero-copy cloning?

One of Snowflake’s best characteristics is zero-copy cloning. It essentially allows you to duplicate an object without creating a physical copy or incurring additional storage costs. When a clone (cloned object) is formed, a snapshot of the data in the source object is captured and made available to it. Cloned objects are readable because they are independent of the source object, and any changes made to one do not affect the other. You can use the CLONE keyword to clone tables, schemas, and databases without copying any data.

27.) What do you mean when you say “data sharing” ?

Snowflake data sharing allows enterprises to communicate data between Snowflake accounts rapidly and securely. Database objects shared between snowflake accounts can only be viewed; they cannot be edited or modified. The following are the three types of sharing:

• Information sharing among management units
• Data exchange between departments
• Data sharing among physically separated places.

28.) What is the best technique to remove a string from an array that is an anagram of a previous string?

For example, arr is an array of strings. The goal is to delete all strings that are anagrams of a previous string, then sort the remaining array.

Examples:  
Input: arr[] = { “Scaler”, “Lacers”, “Accdemy”, “Academy” }, N = 4 

Output: [“Scaler”, “Academy”,]  

Explanation: “Listen” and “Silent” are anagrams, so we remove “Silent”. Similarly, “Scaler” and “Lacers” are anagrams, so we remove “Lacers”. 

29.) What should we do to make temporary tables?

To construct a temporary table, use the TEMPORARY term (or the TEMP acronym) in the CREATE TABLE DDL. To construct temporary tables, use the following syntax:

Create temporary table mytable (id number, creation_date date);

30.) What exactly is a Snowflake?

Snowflake is a cloud-based data warehouse (DWH) platform developed on top of AWS (Amazon Web Services), Microsoft Azure, and Google Cloud infrastructures to provide businesses with flexible, scalable storage options as well as hosting BI (Business Intelligence) solutions. It acts as a centralised platform for data management, data lakes, data engineering, data application development, data science, and safe real-time and shared data sharing and consumption. Snowflake changed the data warehousing market by providing a centralised approach for consolidating all data. You can simplify data warehouse management with Snowflake without compromising capabilities.