Browse [LATEST] Snowflake Interview Questions and Answers

Last updated on 10th Nov 2021, Blog, Interview Questions

These TypeScript Interview Questions have been designed specially to get you acquainted with the nature of questions you may encounter during your interview for the subject of TypeScript . As per my experience good interviewers hardly plan to ask any particular question during your interview, normally questions start with some basic concept of the subject and later they continue based on further discussion and what you answer.we are going to cover top 100 TypeScript  Interview questions along with their detailed answers. We will be covering TypeScript  scenario based interview questions, TypeScript  interview questions for freshers as well as TypeScript  interview questions and answers for experienced.

1. What is Snowflake cloud data warehouse?

Ans:

Snowflake is an analytic data warehouse implemented as the SaaS service. It is built on new SQL database engine with the unique architecture built for the cloud. This cloud-based data warehouse solution was the first available on AWS as software to load and analyze the massive volumes of data. The most remarkable feature of a Snowflake is its ability to spin up the any number of virtual warehouses, which means user can operate the unlimited number of independent workloads against same data without any risk of contention.

2. Is Snowflake ETL tool?

Ans:

Yes, Snowflake is ETL tool. It’s three-step process, which includes:

• Extracts data from a source and creates data files. Data files support the multiple data formats like a JSON, CSV, XML, and more.
• Loads data to internal or external stage. Data can be staged in internal, Microsoft Azure blob, Amazon S3 bucket, or a Snowflake managed location.
• Data is copied into the Snowflake database table using COPY INTO command.

3. Describe Snowflake computing?

Ans:

Snowflake cloud data warehouse platform provides the instant, secure, and governed access to entire data network and core architecture to enable various types of a data workloads, including the single platform for developing a modern data applications.

4. How is data stored in Snowflake?

Ans:

Snowflakes store a data in the multiple micro partitions which are internally optimized and compressed. The data is stored in columnar format in the cloud storage of the Snowflake. The data objects stored by a Snowflake cannot be accessed or visible to users. By running SQL query operations on a Snowflake, can access them.

5. How is Snowflake distinct from AWS?

Ans:

Snowflake offers the storage and computation independently, and storage cost is similar to the data storage. AWS handles this aspect by inserting Redshift Spectrum, which enables the data querying instantly on S3, yet not as acontinuous as Snowflake.

6. What type of database is Snowflake?

Ans:

Snowflake is a built entirely on the SQL database. It’s a columnar-stored relational database that works well with the Excel, Tableau, and many other tools. Snowflake contains query tool, supports the multi-statement transactions, role-based security, etc., which are expected in the SQL database.

7. Can AWS glue connect to Snowflake?

Ans:

Definitely. AWS glue presents the comprehensive managed environment that easily connects with the Snowflake as data warehouse service. These two solutions collectively enable to handle the data ingestion and transformation with more ease and flexibility.

8. Explain Snowflake editions?

Ans:

• Snowflake offers the multiple editions depending on the usage requirements.
• Standard edition – Its introductory level offering provides the unlimited access to the Snowflake’s standard features.
• Enterprise edition – Along with the Standard edition features and services, offers additional features required for a large-scale enterprises.

9. Define Snowflake Cluster

Ans:

In Snowflake, data partitioning is called the clustering, which specifies a cluster keys on the table. The method by which manage clustered data in the table is called re-clustering.

10. Explain Snowflake architecture?

Ans:

Snowflake is built on AWS cloud data warehouse and is a truly Saas offering. Three main layers make a Snowflake architecture – database storage, query processing, and cloud services.

• Data storage – In Snowflake, stored data is reorganized into internal optimized, columnar, and optimized format.
• Query processing – Virtual warehouses process queries in Snowflake.

11. What are features of Snowflake?

Ans:

• Database and Object Closing
• Support for an XML
• External tables
• Hive meta store integration
• Supports the geospatial data
• Security and data protection
• Data sharing

12. Why is Snowflake highly successful?

Ans:

• It assists the wide variety of technology areas like a data integration, business intelligence, advanced analytics, security, and governance.
• It offers the cloud infrastructure and supports advanced design architectures ideal for dynamic and quick usage developments.
• Snowflake supports the predetermined features like a data cloning, data sharing, division of computing and storage, and directly scalable computing.
• Snowflake eases the data processing.

13. What about Snowflake AWS?

Ans:

For managing today’s data analytics, companies rely on the data platform that offers rapid deployment, compelling a performance, and on-demand scalability. Snowflake on AWS platform serves as SQL data warehouse, which makes a modern data warehousing effective, manageable, and accessible to all the data users. It enables a data-driven enterprise with the secure data sharing, elasticity, and per-second pricing.

14. Explain Snowflake ETL?

Ans:

Snowflake ETL is approach to applying ETL process for loading the data into a Snowflake data warehouse or database. Snowflake ETL also includes the extracting the data from a data sources, doing necessary transformations, and loading a data into Snowflake.

15. What is schema in Snowflake?

Ans:

Schemas and databases used for an organizing data stored in the Snowflake. A schema is the logical grouping of database objects such as tables, views, etc. The benefits of using the Snowflake schemas are it provides a structured data and uses small disk space.

16. What are benefits of Snowflake Schema?

Ans:

• In denormalized model, use a less disk space.
• It provides best data quality.

17. Differentiate Star Schema and Snowflake Schema?

Ans:

	Aspect	Star Schema	Snowflake Schema
Structure	Centralized fact table connected to dimension tables.	Centralized fact table connected to normalized dimension tables.
Complexity	Denormalized, simpler structure with direct links between the fact table and dimensions.	Normalized, more complex structure with hierarchies in dimension tables.
Normalization	Less normalized, dimensions are usually not further divided.	More normalized, dimensions may be divided into sub-dimensions.

18. What kind of SQL does Snowflake use?

Ans:

The Snowflake supports the most common standardized version of SQL. ANSI for a powerful relational database querying.

19. What are cloud platforms currently supported by Snowflake?

Ans:

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

20. What ETL tools do use with Snowflake?

Ans:

Following are best ETL tools for Snowflake:

• Matillion
• Blendo
• Hevo Data
• StreamSets
• Etleap
• Apache Airflow

21. Explain zero-copy cloning in Snowflake?

Ans:

In Snowflake, Zero-copy cloning is implementation that enables us to generate the copy of our tables, databases, schemas without replicating actual data. To carry out zero-copy in a Snowflake, have to use the keyword known as the CLONE. Through this action, can get the live data from the production and carry out the multiple actions.

22. Explain “Stage” in Snowflake?

Ans:

In Snowflake, Stage acts as a middle area that use for uploading the files. Snowpipe detects files once they arrive at staging area and systematically loads them into Snowflake.

Following are stages supported by a snowflake:

• Table Stage
• User Stage
• Internal Named Stage

23. Explain data compression in Snowflake?

Ans:

All data we enter into the Snowflake gets compacted systematically. Snowflake utilizes a modern data compression algorithms for the compressing and storing the data. Customers have to pay for packed data, not for exact data.

24. How do secure data in the Snowflake?

Ans:

Data security plays the prominent role in all enterprises. Snowflake adapts best-in-class security standards for an encrypting and securing the customer accounts and data that store in the Snowflake. It provides industry-leading key management features at the no extra cost

25. Explain Snowflake Time Travel?

Ans:

Snowflake Time Travel tool allows us to access past data at any moment in specified period. Through this, can see the data that can change or delete. Through this tool, can carry out following tasks:

• Restore a data-associated objects that may have a lost unintentionally. For examining a data utilization and changes done to data in the specific time period.
• Duplicating and backing up a data from essential points in history.

26. What is database storage layer?

Ans:

Whenever load the data into Snowflake, it organizes a data into the compressed, columnar, and optimized format. Snowflake deals with the storing the data that comprises a data compression, organization, statistics, file size, and the other properties associated with data storage. All data objects store in the Snowflake are the inaccessible and invisible. And can access data objects by executing SQL query operation through Snowflake.

27. Explain Fail-safe in Snowflake?

Ans:

Fail-safe is the modern feature that exists in a Snowflake to assure data security. Fail-safe plays the vital role in the data protection lifecycle of a Snowflake. Fail-safe provides the seven days of additional storage even after a time travel period is completed.

28. Explain Virtual warehouse?

Ans:

In Snowflake, Virtual warehouse is one or more clusters endorsing the users to carry out operations like the queries, data loading, and other DML operations. Virtual warehouses approve users with necessary resources like a temporary storage, CPU for performing the various snowflake operations.

29. Explain Data Shares?

Ans:

Snowflake Data sharing allows an organizations to securely and immediately share data. Secure data sharing enables the sharing of the data between accounts through the Snowflake secure views, database tables.

30. What are various ways to access Snowflake Cloud data warehouse?

Ans:

ODBC Drivers
• JDBC Drivers
• Web User Interface
• Python Libraries
• SnowSQL Command-line Client

31. Explain Micro Partitions?

Ans:

Snowflake comes along with the robust and unique kind of data partitioning known as amicro partitioning. Data that exists in a Snowflake tables are systematically converted into the micro partitions. Generally, perform Micro partitioning on a Snowflake tables.

32. Explain Columnar database?

Ans:

The columnar database is opposite to conventional databases. It saves a data in columns in place of rows, eases method for analytical query processing and offers the more incredible performance for a databases. Columnar database eases the analytics processes, and it is future of business intelligence.

33. How to create Snowflake task?

Ans:

To create Snowflake task, have to use “CREATE TASK” command. Procedure to create the snowflake task:

• CREATE TASK in a schema.
• USAGE in a warehouse on task definition.
• Run SQL statement or stored procedure in a task definition.

34. How do create temporary tables?

Ans:

• To create a temporary tables, have to use the following syntax
• Create the temporary table
• mytable (id number, creation_date date);

35. Where do store data in Snowflake?

Ans:

Snowflake systematically creates the metadata for the files in the external or internal stages. Store the metadata in the virtual columns, and can query through the standard “SELECT” statement.

36. Does Snowflake use Indexes?

Ans:

• No, Snowflake does not use the indexes.
• This is one of the aspects that set a Snowflake scale so good for queries.

37. How is Snowflake distinct from AWS?

Ans:

Snowflake offers the storage and computation independently, and storage cost is similar to the data storage. AWS handles this aspect by inserting the Redshift Spectrum, which enables the data querying instantly on S3, yet not as a continuous as Snowflake.

38. How do execute Snowflake procedure?

Ans:

Stored procedures allow us to create the modular code comprising complicated business logic by adding the various SQL statements with the procedural logic. For an executing Snowflake procedure, carry out the below steps:

• Run SQL statement
• Extract a query results
• Extract a result set metadata

39. Does Snowflake maintain stored procedures?

Ans:

Yes, Snowflake maintains the stored procedures. The stored procedure is a same as a function; it is created once and used the several times. Through the CREATE PROCEDURE command, can create it and through “CALL” command, we can execute it. In Snowflake, stored procedures are the developed in Javascript API. These APIs enable a stored procedures for executing database operations like SELECT, UPDATE, and CREATE.

40. Is Snowflake OLTP or OLAP?

Ans:

Snowflake is the developed for the Online Analytical Processing(OLAP) database system. Subject to the usage, can utilize it for the OLTP(Online Transaction processing) also.

41. How is Snowflake distinct from Redshift?

Ans:

Both Redshift and Snowflake provide the on-demand pricing but vary in the package features. Snowflake splits the compute storage from usage in its pricing pattern, whereas the Redshift integrates both.

42. What is use of Cloud Services layer in Snowflake?

Ans:

The services layer acts as a brain of the Snowflake. In Snowflake, Services layer authenticates user sessions, applies security functions, offers the management, performs optimization, and organizes all transactions.

43. What is use of Compute layer in Snowflake?

Ans:

In Snowflake, Virtual warehouses perform all data handling tasks. Which are the multiple clusters of the compute resources. While performing the query, virtual warehouses extract least data needed from a storage layer to satisfy query requests.

44. What is Unique about Snowflake Cloud Data Warehouse?

Ans:

Snowflake is a cloud native (built for cloud).So, It takes advantage of all good things about the cloud and brings exciting the new features like,

• Auto scaling
• Zero copy cloning
• Dedicated virtual warehouses
• Time travel
• Military grade encryption and security

45. What is Fail-safe in Snowflake?

Ans:

Fail-safe is the advanced feature available in Snowflake to ensure a data protection. This plays the important role in Snowflake’s data protection lifecycle. Fail-safe offers the 7 days extra storage even after time travel period is over.

46. What are different types of caching in Snowflake?

Ans:

• Query Results Caching
• Virtual Warehouse Local Disk Caching
• Metadata Cache

47. What is Snowflake Time Travel?

Ans:

Snowflake Time Travel tool enables to access the historical data at any given point within defined time period. Using this can see the data that has been deleted or changed. Using this tool can perform below tasks:

• Restore a data-related objects (Schemas, tables, and databases) that might have the lost accidentally.
• To examine a data usage and changes made to data with time period

48. What is Snowflake Caching?

Ans:

Snowflake caches results of every query ran and when a new query is submitted, it checks previously executed queries and if matching query exists and the results are still cached, it uses cached result set instead of executing query. Snowflake Cache results are the global and can be used across the users.

49. Why fail-safe instead of Backup?

Ans:

To minimize a risk factor, DBA’s traditionally execute the full and incremental data backups at regular intervals. This process occupies the more storage space, sometimes it may be double or a triple. Moreover, data recovery process is costly, takes a time, requires business downtime, and more. Snowflake comes with the multi-datacenter, redundant architecture that has capability to minimize the need for a traditional data backup. Fail-safe features in Snowflake is efficient and cost-effective way that substitutes traditional data backup and eliminates risks and scales along with the data.

50. What is Data retention period in Snowflake?

Ans:

Data retention is one of the key components of a Snowflake and the default data retention period for all the snowflake accounts is 1 day (24 hours). This is the default feature and applicable for all the Snowflake accounts.

51. Explain data shares in Snowflake?

Ans:

The data shares option in the snowflake allows the users to share data objects in a database in your account with the other snowflake accounts in a secured way. All the database objects shared between the snowflake accounts are only readable and one can not make any changes to them.

Following are sharable database objects in Snowflake:

• Tables
• Secure views
• External tables
• Secure UDFs
• Secure materialized views

52. What are data sharing types in Snowflake?

Ans:

• Sharing a Data between the functional units.
• Sharing a data between the management units.
• Sharing a data between geographically dispersed location

53. What are different Connectors and Drivers available in Snowflake?

Ans:

• Snowflake Connector for a Python
• Snowflake Connector for a Kafka
• Snowflake Connector for a Spark
• Go Snowflake Driver
• Node.js Driver
• JDBC Driver
• NET Driver
• ODBC Driver
• PHP PDO Driver for Snowflake

54. What is Snowpipe in Snowflake?

Ans:

Snowpipe is the continuous, and cost-effective service used to load data into the Snowflake. The Snowpipe automatically loads a data from files once they are available on stage. This process simplifies a data loading process by loading data in the micro-batches and makes data ready for the analysis.

55. What are benefits of using Snowpipe?

Ans:

• Real-time insights
• Ease of use
• Cost-effective
• Flexibility
• Zero Management

56. What is virtual warehouse in Snowflake?

Ans:

A Virtual warehouse in the Snowflake is defined as one or more compute clusters supporting the users to perform operations like a data loading, queries, and many other DML operations. Virtual warehouses support the users with the required resources like CPU, temporary storage, memory, etc, to perform the different Snowflake operations.

57. What are programming languages supported by Snowflake?

Ans:

Snowflake supports the different programming languages like:

• Go,
• Java,
• .NET
• Python,
• C,
• Node.js.

58. What are micro partitions in Snowflake?

Ans:

Snowflake comes with the unique and powerful form of data partitioning called the micro-partitioning. Data resided in all the snowflake tables is automatically converted into micro partitions. In a general Micro partitioning is performed on all the Snowflake tables.

59. What is Clustering key?

Ans:

The clustering key in the Snowflake is a subset of columns in the table that helps us in co-locating data within the table. It is the best suitable for situations where tables are extensive; the order was not a perfect due to the DML.

60. What is Amazon S3?

Ans:

Amazon S3 is the storage service that offers the high data availability and security. It provides the streamlined process for organizations of all the sizes and industries to store their data.

61.What is the architecture of Snowflake?

Ans:

Snowflake adopts a multi-cluster, shared data architecture. The compute layer comprises virtual warehouses responsible for query processing, while the storage layer uses cloud-based object storage to store data in micro-partitions. A metadata store manages information about table structures, user permissions, and queries. This separation of compute and storage allows for automatic and elastic scaling based on workload demands, ensuring efficient and scalable data processing in the cloud.

63. What are advantages of Snowflake Schema?

Ans:

• Uses a less disk space
• Minimal data redundancy.
• Eliminates the data integration challenges
• Less maintenance
• Executes a complex queries
• Supports the many-to-many relationships

64. What is Materialized view in Snowflake?

Ans:

A materialized view in the Snowflake is a pre-computed data set derived from query specification. As data is pre-computed, it becomes far easier to query materialized view than non-materialized view from view’s base table. In simple words, materialized views are the designed to enhance the query performance for a common and repetitive query patterns. Materialized Views are the primary database objects and speedup projection, expensive aggregation, and selection operations for the queries that run on larger data sets.

65.What are advantages of Materialized Views?

Ans:

• Improves the query performance
• Snowflake automatically manages the materialized Views.
• Materialized views can provide updated data.

66. What is use of SQL in Snowflake?

Ans:

SQL stands for the Structured Query Language and is common language used for data communication. Within SQL, common operators are the clubbed into DML (Data Manipulation Language) & DDL (Data Definition Language) to perform the various statements such as SELECT, UPDATE, INSERT, CREATE, ALTER, DROP, etc. Snowflake is the data warehouse platform and supports standard version of SQL. Using the SQL in Snowflake, can perform the typical data warehousing operations like a create, insert, alter, update, delete, etc.

67. What are ETL tools supported by Snowflake?

Ans:

• Matillion
• Infromatica
• Tableau
• Talend, etc.

68. Where metadata gets stored in Snowflake?

Ans:

In snowflake, the Metadata is stored in the virtual columns that can be easily queried using the SELECT statement and loaded into the table using the COPY INTO command.

69. What is Auto-scaling in Snowflake?

Ans:

Autoscaling is the advanced feature in Snowflake that starts and stops the clusters based on the requirement to support the workloads on the warehouse.

70. What are advantages of stored procedures in Snowflake?

Ans:

• Supports the procedural logic
• Allows the dynamic creation and execution of SQL statements
• Helps in an error handling
• Allows Stored procedure owner to delegate power to users
• Eliminates the need for the multiple SQL statements to perform a task.

71. What are internal and external stages in Snowflake?

Ans:

Internal Stage : Here files are stored within the Snowflake account.

External Stage : Here files are stored in an external location. For the instance AWS S3.

72. What is Continuous Data Protection in Snowflake?

Ans:

Continuous Data Protection (CDP) is the essential feature offered by the Snowflake to protect data stored in the snowflake from events like malicious attacks, human error, and software or hardware failovers. This CDP feature makes data accessible and recoverable at all stages of the data life cycle even if lost it accidentally.

73. What is Query Processing layer in Snowflake architecture?

Ans:

All query executions are performed in this processing layer. Snowflake uses virtual warehouses to the process queries. Every virtual warehouse is an MPP (massively parallel processing) compute the cluster which consists of the multiple nodes allotted by snowflake from cloud provider. Each virtual warehouse in query processing layer is independent and does not share its computational resources with the any other virtual warehouses. This makes every virtual warehouse independent and shows no impact on other virtual warehouses in case of any failover.

74. What is Data Retention Period in Snowflake?

Ans:

When data in the table is modified, such as deletion or discarding the object holding data, Snowflake saves a data’s previous state. The data retention period determines the number of days that this historical data is keep and, as result, Time Travel operations (SELECT, CREATE… CLONE, UNDROP) can be performed on it. The standard retention period is a one day (24 hours) and is enabled by a default for all Snowflake accounts.

75. What is use of Snowflake Connectors?

Ans:

The Snowflake connector is the piece of software that allows us to connect to the Snowflake data warehouse platform and conduct the activities such as Read/Write, Metadata import, and a Bulk data loading.

76. What are types of Snowflake Connectors?

Ans:

• Snowflake Connector for Kafka
• Snowflake Connector for Spark
• Snowflake Connector for Python

77. What are Snowflake views?

Ans:

Views are the useful for displaying a certain rows and columns in one or more tables. A view makes it possible to obtain result of a query as if it were a table. The CREATE VIEW statement defines query. Snowflake supports the two different types of views:

• Non-materialized views (often referred to as “views”) – The results of non-materialized view are obtained by executing query at the moment the view is referenced in a query. When compared to the materialised views, performance is slower.
• Materialized views – Although named as type of view, a materialised view behaves more like a table in the many aspects. The results of a materialised view are saved in the similar way to that of table. This allows for the faster access, but it necessitates the storage space and active maintenance, both of which incur an extra expenses.

78. Does Snowflake maintain stored procedures?

Ans:

Yes, Snowflake maintains a stored procedures. The stored procedure is a same as a function; it is created once and used several times. Through CREATE PROCEDURE command, = can create it and through the “CALL” command, can execute it. In Snowflake, stored procedures are the developed in Javascript API. These APIs enable to stored procedures for executing database operations like a SELECT, UPDATE, and CREATE.

79. How do execute Snowflake procedure?

Ans:

Stored procedures allow us to create the modular code comprising complicated the business logic by adding the various SQL statements with procedural logic. For executing Snowflake procedure, carry out below steps:

• Run SQL statement
• Extract a query results
• Extract a result set metadata

80. Explain Snowflake Compression?

Ans:

All data enter into the Snowflake gets compacted to systematically. Snowflake utilizes the modern data compression algorithms for the compressing and storing the data. Customers have to pay for packed data, not exact data.

Following are advantages of Snowflake Compression:

• Storage expenses are the lesser than a original cloud storage because of compression.
• No storage expenditure for the on-disk caches.
• Approximately zero storage expenses for a data sharing or data cloning.

81. Explain Snowflake data loading process?

Ans:

The Snowflake data loading process involves the importing data into the Snowflake data warehouse from the various sources such as files, databases, and cloud storage services. The data loading process can be performed using the variety of methods, including the bulk loading, automated data ingestion, and real-time data streaming.

82. How does Snowflake handle concurrency and multi-user access?

Ans:

Snowflake is designed to handle the concurrent access by the multiple users, and it uses a unique architecture that separates a storage and computing resources. In Snowflake, the multiple virtual warehouses can run on a same data at the same time, providing each user with the private, isolated compute environment.

83. How does Snowflake handle data integration and data management?

Ans:

Snowflake handles the data integration and management in unique way that separates storage from computation. The data is stored in the columnar format and optimized for a data warehousing use cases. Snowflake supports the variety of data sources, including the structured and semi-structured data, and can load data into warehouse using the several methods including the bulk loading, stream loading, and file upload.

84. Explain Snowflake collaboration features and how work?

Ans:

An important collaboration feature is a Snowflake Worksheets, which allow the users to share queries and results with the other Snowflake users. Worksheets can be created and shared through Snowflake web interface, and provide a collaborative way to work with the data, with the real-time updates and commenting capabilities.

85. How does Snowflake handle scalability and reliability?

Ans:

Snowflake is designed to the handle scalability and reliability seamlessly and without any manual intervention. Snowflake uses the multi-cluster, shared-data architecture to provide a high availability and scalability. This means that data is automatically and transparently distributed across the multiple storage clusters, providing an automatic failover and resiliency.

86. Explain Snowflake Query Pushdown feature and how it works?

Ans:

Snowflake’s Query Pushdown feature allows for the filtering and processing of data at a source before it is brought into Snowflake. This results in the improved performance, as only relevant data is loaded into Snowflake warehouse, reducing the amount of the data that needs to be processed and store.

87. How to load and unload data in Snowflake?

Ans:

Loading data into the Snowflake can be done using following methods:

Bulk loading : Use COPY INTO command to a load large volumes of data from an external sources, such as cloud storage services or on-premises file systems.

Continuous loading : Use the Snowpipe, a serverless data ingestion service, to load data continuously as it becomes more available.

88. How does Snowflake handle big data and analytics?

Ans:

Snowflake’s architecture is designed to handle the big data and analytics by providing customers with elastic and scalable platform. Snowflake separates a compute and storage, allowing the customers to scale up or down compute resources based on requirements. Snowflake also supports the parallel processing and automatic query optimization, providing fast and can efficient data processing capabilities.

89. How does Snowflake support real-time data processing?

Ans:

Snowflake supports the real-time data processing through integration with Kafka, a distributed streaming platform. Snowflake provides the Kafka connector that allows the customers to stream data in a real-time from Kafka to Snowflake. Snowflake also supports the continuous data ingestion through its Snowpipe feature, which allows the data to be ingested in a real-time as it becomes available.

90. How to handle complex data transformation process in Snowflake?

Ans:

Snowflake is the cloud-based data warehousing platform that enables the efficient handling of complex data transformation processes. Here are some steps to handle the complex data transformation processes in Snowflake:

Understand a data transformation requirements : Before starting transformation process, it is essential to understand data and the requirements for transforming it. Identify source of data, target data format, and data transformation process needed.

Design transformation process : Once requirements are clear, design a data transformation process in Snowflake. Consider factors are data volume, complexity of the transformations, and level of automation needed.