Browse [LATEST] Snowflake Interview Questions and Answers
Snowflake Interview Questions and Answers

Browse [LATEST] Snowflake Interview Questions and Answers

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

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Raj Kumar (Sr. Snowflake Developer )

Raj Kumar is a Sr. Snowflake Developer who has experience with Snowflake utilities such as SnowSQL, SnowPipe, Python, Tasks, Streams, Time travel, Optimizer, Metadata Manager, data sharing, and stored procedures.

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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.

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    1)What is a Snowflake cloud data warehouse?


      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.

    2)Is Snowflake an ETL tool?


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

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


        The full form of ETL is Extract, Transform, and Load. ETL is the process that we use for extracting the data from multiple sources and loading it to a particular database or data warehouse. The sources are third party apps, databases, flat files, etc.

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

      3)How is data stored in Snowflake?


        Snowflakes store 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.

      4)How is Snowflake distinct from AWS?


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

      5)What type of database is 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.

      6)Can AWS glue connect to Snowflake?


        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.

      7)Explain Snowflake editions.


        Snowflake offers multiple editions depending on your usage requirements.

      • Standard edition – Its introductory level offering provides unlimited access to Snowflake’s standard features.
      • Enterprise edition – Along with Standard edition features and services, offers additional features required for large-scale enterprises.
      • Business-critical edition – Also, called Enterprise for Sensitive Data (ESD). It offers high-level data protection for sensitive data to organization needs.
      • Virtual Private Snowflake (VPS) – Provides high-level security for organizations dealing with financial activities.
      • 8)Define the Snowflake Cluster


          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.

        9)Explain Snowflake architecture


          Snowflake is built on an AWS cloud data warehouse and is truly 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.

        • Data storage – In Snowflake, the stored data is reorganized into its internal optimized, columnar, and optimized format.
        • Query processing – Virtual warehouses process the queries in Snowflake.
        • Cloud services – This layer coordinates and handles all activities across the Snowflake. It provides the best results for Authentication, Metadata management, Infrastructure management, Access control, and Query parsing.

        10)What are the features of Snowflake?


          Unique features of the Snowflake data warehouse are listed below:

        • Database and Object Closing
        • Support for XML
        • External tables
        • Hive meta store integration
        • Supports geospatial data
        • Security and data protection
        • Data sharing
        • Search optimization service
        • Table streams on external tables and shared tables
        • Result Caching

        11)Why is Snowflake highly successful?


          Snowflake is highly successful because of the following reasons:

        • It assists a wide variety of technology areas like data integration, business intelligence, advanced analytics, security, and governance.
        • It offers cloud infrastructure and supports advanced design architectures ideal for dynamic and quick usage developments.
        • Snowflake supports predetermined features like data cloning, data sharing, division of computing and storage, and directly scalable computing.
        • Snowflake easeus data processing.
        • Snowflake provides extendable computing power.
        • Snowflake suits various applications like ODS with the staged data, data lakes with data warehouse, raw marts, and data marts with acceptable and modelled data.

        12)Tell me something about Snowflake AWS?


          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.

        13)Describe Snowflake computing.


          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 cloud data warehouse

        14)What is the schema in Snowflake?


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

        15)What are the benefits of the Snowflake Schema?


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

        16)Differentiate Star Schema and Snowflake Schema?


          Both Snowflake and Star Schemas are identical, yet the difference exists in dimensions. In Snowflake, we normalise only a few dimensions, and in a star schema, we denormalise the logical dimensions into tables.

        17)What kind of SQL does Snowflake use?


          Snowflake supports the most common standardized version of SQL, i.e., ANSI for powerful relational database querying.

        18)What are the cloud platforms currently supported by Snowflake?


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

        19)What ETL tools do you use with Snowflake?


          Following are the best ETL tools for Snowflake

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

        20)Explain zero-copy cloning in Snowflake?


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

        21)Explain “Stage” in the Snowflake?


          In Snowflake, the Stage acts as the middle area that we use for uploading the files. Snowpipe detects the files once they arrive at the staging area and systematically loads them into the Snowflake. Following are the stages supported by the snowflake:

        • Table Stage
        • User Stage
        • Internal Named Stage

        22)Explain data compression in Snowflake?


          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.

        23)How do we secure the data in the Snowflake?


          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.

        24)Explain Snowflake Time Travel?


          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:

          Restore the data-associated objects that may have lost unintentionally.

          For examining the data utilization and changes done to the data in a specific time period.

          Duplicating and backing up the data from the essential points in history.

        25)What is the database storage layer?


          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.

        26)Explain Fail-safe in Snowflake?


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

        27)Explain Virtual warehouse?


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

        28)Explain Data Shares


          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.

        Data Shares

        29)What are the various ways to access the Snowflake Cloud data warehouse?


          We can access the Snowflake data warehouse through:

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

        30)What are the advantages of Snowflake Compression?


          Following are the advantages of the Snowflake Compression:

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

        31)Differentiate Fail-Safe and Time-Travel in Snowflake?


        • Time-Travel
        • Fail-Safe
        • 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.

        32)Explain Snowpipe in Snowflake?


        Snowpipe is a cost-efficient and constant service that we use for loading the data into the Snowflake. Snowpipe systematically loads data from the files as soon as they are attainable on the stage. Snowpipe eases the data loading process by loading the data into the micro-batches and shapes data for analysis.

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      33)What are the advantages of the Snowpipe?


        Following are the Snowpipe advantages:

      • Live insights
      • User-friendly
      • Cost-efficient
      • Resilience

      34)Explain Micro Partitions?


        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.

      35)Explain Columnar database?


        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 off

      36)How to create a Snowflake task?


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

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

      37)How do we create temporary tables?


        To create temporary tables, we have to use the following syntax:

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

      38)Where do we store data in Snowflake?


        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.

      39)Does Snowflake use Indexes?


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

      41)How is Snowflake distinct from AWS?


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

      42)How do we execute the Snowflake procedure?


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

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

      43)Does Snowflake maintain stored procedures?


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

      44)Is Snowflake OLTP or OLAP?


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

      45)How is Snowflake distinct from Redshift?


        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.

      46)What is the use of the Cloud Services layer in Snowflake?


        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.

      47)What is the use of the Compute layer in 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.

      48)What is unique about Snowflake Architecture?


        Snowflake has come up with an advanced and unique architecture that is a combination of shared-disk and shared-nothing architectures. It uses a central data repository to store data consistently and makes it available to access from all compute nodes in the platform. Similar to shared-nothing architecture, Snowflake also executes queries by using MPP (massively parallel processing) compute clusters where every node in the cluster stores a certain amount of the whole data set locally.

        This architecture simplifies data management with shared-disk architecture and adds performance and scalability advantages with shared-nothing architecture. Snowflake unique architecture consists of three layers which are database storage, Query processing, and Cloud services.

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      Snowflake Architecture

      49)What is the Query Processing layer in Snowflake architecture?


        All the query executions are performed in this processing layer. Snowflake uses “virtual warehouses to process queries. Each virtual warehouse is an MPP (massively parallel processing) compute cluster which consists of multiple nodes allotted by snowflake from a cloud provider.

        Each virtual warehouse in the query processing layer is independent and does not share its computational resources with any other virtual warehouses. This makes each virtual warehouse independent and shows no impact on the other virtual warehouses in case of any failover.

      50)What is the Cloud Services layer in Snowflake architecture?


        The Cloud Services layer consists of a set of services that coordinates multiple tasks across the Snowflake platform. All these services tie together and work with great co-ordination to process user requests, from login to query dispatch. This layer also executes computer instances assigned by Snowflake from the cloud manager. Following are the various services managed under this layer:

        • Authentication
        • Metadata management
        • Infrastructure management
        • Access control
        • Optimization and query parsing

      51)What are the advantages of a Snowflake database?


        Snowflake is natively built for the cloud and addresses many issues that are not solved by the traditional warehouse systems. Following are the five core advantages that we gain by using the Snowflake data platform:

      • High-speed performance
      • Supports both structured and unstructured data
      • Concurrency and accessible
      • Seamless data sharing
      • High availability
      • High security

      52)Name a few advantages that arise out of data compression in Snowflake?


        Following are the advantages of Data compression:

      • Lowers storage costs
      • Less disk space
      • Near zero storage overhead for data sharing or data cloning
      • Byte order-independent

      53)What is Snowflake Caching?


        Snowflake stores data cache in SSD in addition to a result cache to improve SQL query performance. It catches the result of every query that you run and wherever a new query is submitted it checks with the previously executed queries to find if any match. If any matching query exists then it uses a cached result set instead of executing the query. This process brings down the time taken for the queries and retrieves results directly from the cache. Following are the different cache layers in Snowflake:

      • Result Cache
      • Local Disk Cache
      • Remote Disk Cache

      54)Name the types of catches in Snowflake?


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

      55)What is Fail-safe in Snowflake?


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

      56)Why fail-safe instead of Backup?


        To minimize the risk factor, DBA’s traditionally execute full and incremental data backups at regular intervals. This process occupies more storage space, sometimes it may be double or triple. Moreover, the data recovery process is costly, takes time, requires business downtime, and more.

        Snowflake comes with a multi-datacenter, redundant architecture that has the capability to minimize the need for traditional data backup. Fail-safe features in Snowflake is an efficient and cost-effective way that substitutes the traditional data backup and eliminates the risks and scales along with your data.

      57)What is the Data retention period in Snowflake?


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

      58)What are the different Snowflake editions?


        Following are the various Snowflake editions available:

      • Standard Edition
      • Enterprise Edition
      • Business Critical Edition
      • Virtual Private Snowflake (VPS) Edition.
      • 59)What are the different Connectors and Drivers available in Snowflake?


          Below mentioned are the various connectors and drivers available in Snowflake:

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

        60)What is “Stage” in Snowflake?


          A stage in Snowflake is defined as an intermediate area used to upload files. Snowpipe Identifies the files as soon as they enter the staging area and automatically loads them into a snowflake. Following are the three different stages supported by Snowflake:

        • User Stage
        • Table Stage
        • Internal Named Stage

        61)What are the programming languages supported by Snowflake?


          Snowflake supports different programming languages like Go, Java, .NET, Python, C, Node.js, etc.

        62)What is Amazon S3?


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

        63)What is Auto-scaling in Snowflake?


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

        Auto-scaling in Snowflake

        64)Snowflake Stored procedures are written in?


          Snowflake Stored procedures are written in JavaScript.

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        65) Do you know how many days of time travel history is preserved in Snowflake? Also, is there any cost that is associated with time travel in Snowflake?


          Time Travel will be available between 1 to 90 days, based on the Snowflake edition you are using or signing up for. There will be a cost associated with the time travel in Snowflake. There will be storage charges that will be incurred, which are specifically for maintaining the historical data during the failed Safe And The Time Travel periods.

        66)Is Snowflake an MPP database?


          MPP stands for Massively Parallel Processing, and is a database architecture successfully deployed by Teradata and Netezza. Unlike traditional Symmetric Multi-Processing (SMP) hardware which runs a number of CPUs in a single machine, the MPP architecture deploys a cluster of independently running machines, with data distributed across the system. In addition to the ability to handle massive data volumes, this means it supports a scale out architecture, as additional nodes can be added to the cluster, although this can take from hours to days to deploy.

          EPP stands for Elastic Parallel Processing, and was pioneered by Snowflake Computing. This uses a number of independently running MPP clusters connected to a shared data pool. This architecture has the advantage that new clusters can be started within seconds, to elastically grow or shrink resources as needed.[/vc_column_text][vc_column_text css_animation=”left-to-right”]

        67)Which Snowflake edition should you use if you want to enable time travel for up to 90 days?

        Enterprise Edition, Business Critical Edition and VPS Edition have the option to enable time travel for up to 90 days.[/vc_column_text][vc_column_text css_animation=”left-to-right”]

      68)Can you create Transient Views in Snowflake?


        In Snowflake, you can create the below views.

      • Standard View
      • Secure View
      • Materialized View
      • Transient View is an Invalid view type.
      • You can create transient tables in Snowflake but not transient views.

      69)How do I copy a fraction of staged data into a snowflake?


        These options enable you to copy a fraction of the staged data into Snowflake with a single command. This allows you to execute concurrent COPY statements that match a subset of files, taking advantage of parallel operations. The COPY INTO command includes a FILES parameter to load files by specific name.

      70)explain about the difference between the transient and temporary tables?


      71)By default, clustering keys are created for every table, how can you disable this option?


        Clustering keys are not created by default for every table. Clustering key can be defined

      • 1) when a table is created by appending a CLUSTER BY clause to CREATE TABLE:
      • create or replace table t1 (c1 date, c2 string, c3 number) cluster by (c1, c2);
      • 2) You can add a clustering key to an existing table or change the existing clustering key for a table using ALTER TABLE alter table t1 cluster by (c1, c3);
      • 3) At any time, you can drop the clustering key for a table using ALTER TABLE: ALTER TABLE DROP CLUSTERING KEY
      • 72)What are the services included in Snowflake?


          Services included in this are authentication, metadata management, infrastructure management, access control, and Query parsing & optimization. Q14) Who are the competitors to Snowflake? Ans. Top Snowflake competitors are AWS, Google, Microsoft, Cloudera, IBM, SAP, and Teradata.

        73)How does Snowflake protect customer data?


          Data security is a top priority of every organization. Snowflake meets the industry’s highest safety standards for encrypting and securing data stored and customer accounts in Snowflake. It provides the best key management features without additional charge. The following are the security measures that Snowflake uses to protect client data:

        74)What’s new with Snowflake and Databricks?


          Snowflake partnered with Databricks to allow heavy data science and other complex workloads to run against your data. The recent partnership with Microsoft will ensure Azure services continue to expand their Snowflake native integrations – expect to see a barrage of new partnership announcements during the next 12 months.

        75)Is snowflake right for your deployment?


          The answer to both of these questions is “yes.” This blog originally appeared as a section of our eBook, “Snowflake Deployment Best Practices: A CTO’s Guide to a Modern Data Platform.”

        76)What is a snowflake data model?


          Dimensional Data Modeling – Snowflake schema About. A snowflake schema is a star schema with fully normalised ( 3NF) dimensions. It gets its name from the fact that it has a similar shape than a snowflake. Case where you need a snowflake schema Management of change. Consider the following example below. … Conclusion. Building a snowflake of a 3NF model does not guarantee that it’s immune to business change. …

        snowflake data model

        77)Can obiee use the Snowflake data model?


          With OBIEE, you could use a physical layer to record this complex snowflake model, But for the business model and mapping layer (BMM), we could only store star relationships. That’s why we have to use star schema over snowflake data models.

        78)Does Snowflake data warehouse work with Oracle Business Intelligence Enterprise Edition?


          Congratulations! You just configured Oracle Business Intelligence Enterprise Edition (OBIEE) to connect to your freshly loaded Snowflake Data Warehouse instance and you’re feeling pretty good.

        79)Which connection pool does obiee use for each session?


          When running queries against a data source, OBIEE will use the first valid connection pool in the physical layer of the RPD that a user has access to. Consequently, we can control which connection pool OBIEE uses for each session by selectively granting access for application roles.

        80)Is it possible to convert snowflakes to star schema?


          Now your RPD is ready to be deployed and used for analysis. This method applies to any other scenario where 2 or more tables need to be joined together to create a single dimension: In other words, to convert snow-flake to star schema.

        81)Why do I need a star schema?


          The star schema is the simplest form of a dimensional model used in business intelligence and data warehousing wherein data is arranged in dimensions and facts.

        82)Is star schema normalized or denormalized?


          Strictly speaking, Star schema is likely to be denormalized, but it doesn’t have to be. It depends on the complexity of your underlying data model. The denormalization usually happens at the dimension tables, since many real-world dimensions often have some hierarchical relationship

        83)What is the denormalization of a star schema?


          The denormalization is in the dimension tables in a star schema: E. g. in a product table, you explicitly have many columns like several levels of product category in this one table, instead of having one table for each level, and using foreign keys referencing those values.

        84)What is the difference between a normalized data model and star schema?


          In a normalized data model, you will have separate employee and department tables and a foreign key relationship that links them. In a pure star schema, you will only have the employee table, and repeat the department data for each employee.

        85)What is the difference between a fact table and star schema?


          The most consistent table you’ll find in a star schema is a date dimension table. A dimension table contains a key column (or columns) that acts as a unique identifier, and descriptive columns. Fact tables store observations or events, and can be sales orders, stock balances, exchange rates, temperatures, etc.

        86)How many fact tables will there be in the star schema?


          There is only one fact table in the Star schema if you have to use more than one then use the link tables in between them which is used as the reference table.

        87)What is an example of a star schema?


          The star schema separates business process data into facts, which hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes related to fact data. Examples of fact data include sales price, sale quantity, and time, distance, speed, and weight measurements.

        88)What is the difference between a normalized data model and star schema?


          In a normalized data model, you will have separate employee and department tables and a foreign key relationship that links them. In a pure star schema, you will only have the employee table, and repeat the department data for each employee.

        89)Is it possible to read data from a normalized schema?


          Much overhead is involved when reading data from a normalized table scheme. In order to read in all the data needed for a report, for example, not only would all the tables have to be read, each row would also have to be joined to its partner. So for reporting purposes, this normalized schema is not optimal.

        90)Why don’t we use normalized data models for reporting?


          If we had put all the data in one table, all revenue records of this one office would have to be updated and get the new name. So normalized data models are good for updates and single row operations in general, but not for reporting across all records. For reporting purposes, we have to look at different design alternatives.

        91)Why is the data in the table below not normalized?


          The data in the table below is not normalized because it contains repeating attributes (contact1, contact2,…). Not normalized customer data. Not normalized (0NF) table/entity in a data model. 1NF: No Repeating Groups

        92)Why do we normalize the data in machine learning?


          Because different features do not have similar ranges of values and hence gradients may end up taking a long time and can oscillate back and forth and take a long time before it can finally find its way to the global/local minimum. To overcome the model learning problem, we normalize the data.

        93)What do you learn in Snowflake architecture and coaching?


          Snowflake architecture and caching. Learning Outcomes: At the end of this module, you will get a real-time experience of using AWS S3 Storage, Azure Blob Storage, and GCP Bucket Storage in the Snowflake Data warehouse platform. You will also acquire an in-depth understanding of Snowflake Architecture and Caching.

        94)How is a data lake different from a data warehouse?


          Data: Unlike a data lake, a database and a data warehouse can only store data that has been structured. A data lake, on the other hand, does not respect data like a data warehouse and a database. It stores all types of data be it structured, semi-structured, or unstructured.

        95)Can the data lake replace the data warehouse?


          Data lakes most likely will not replace the data warehouse, Rather the two options are complements to one another. This means that data, once loaded, can be used for a variety of purposes, and across different business applications.

        96)What is data mart vs data warehouse?


          The data mart is a subset of the data warehouse and is usually oriented to a specific business line or team. Whereas data warehouses have an enterprise-wide depth, the information in data marts pertains to a single department.

        97)What is the analytical data store layer in azure?


          The data could also be stored by the data warehouse itself or in a relational database such as Azure SQL Database. The purpose of the analytical data store layer is to satisfy queries issued by analytics and reporting tools against the data warehouse.

        98)Which Snowflake features load data continuously into a staging table?


          One of the following Snowflake features or a third-party data integration tool (not shown) loads data continuously into a staging table: Snowpipe continuously loads micro-batches of data from an external stage location (Amazon S3, Google Cloud Storage, or Microsoft Azure) into a staging table.

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        99)Why Snowflake for machine learning and Ai?


          “With Snowflake, we are much faster in developing and recalibrating models and algorithms, and we are able to leverage all of our data with state-of-the-art ML/AI tools to address business challenges and realize new opportunities.”

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