What Do You Mean By Virtual Warehouse
A virtual warehouse is basically a collection of computing resources that customers can access to run queries, load data, and perform other DML and SQL operations.
For example, it provides memory, temporary storage, and CPU resources that can be used for DML operations and SQL execution. You can use this independent compute cluster at any time and then turn it off when not needed. You are charged for each virtual warehouse you run, their size, and how long they run. Virtual warehouses do not share their compute resources with each other, and therefore, their performance is independent of each other.
As shown in the following diagram, different groups of users can be assigned separate and dedicated virtual warehouses. Therefore, ETL processes can continuously load and execute complex transformation procedures on separate warehouses, ensuring no impact on data scientists or finance reports.
- Apache Airflow, etc.
Q: What Is Mfa In Snowflakes Ans:
Snowflake offers Multi-Factor Authentication to provide increased login security for users connecting to Snowflake by requiring two or more verification factors to obtain access to a resource.
MFA supports an integrated Snowflake feature, enabled by the Duo Security service and totally controlled by Snowflake.
What Is Stage In The Snowflake
In Snowflake, stages are data storage locations. If the data to be imported into Snowflake is stored in another cloud area, such as AWS S3, Azure, or GCP, these are referred to as External stages if the data is stored within Snowflake, they are referred to as Internal stages.
Internal Stages are further divided as below
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What Is The Use Of Snowflake Connectors
The Snowflake connector is a piece of software that allows us to connect to the Snowflake data warehouse platform and conduct activities such as Read/Write, Metadata import, and Bulk data loading.
The Snowflake connector can be used to execute the following tasks:
- Read data from or publish data to tables in the Snowflake data warehouse.
- Load data in bulk into a Snowflake data warehouse table.
- You can insert or bulk load data into numerous tables at the same time by using the Numerous input connections functionality.
- To lookup records from a table in the Snowflake data warehouse.
Following are the types of Snowflake Connectors:
- Snowflake Connector for Kafka
- Snowflake Connector for Python
What Are Conformed Dimensions
- Conformed dimensions are the dimensions which can be used across multiple data marts in combination with multiple fact tables accordingly.
- A conformed dimension is a dimension that has exactly the same meaning and content when being referred from different fact tables. It can refer to multiple tables in multiple data marts within the same organization.
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Q23 Briefly Explain The Working Of Data Compression In Snowflake
Ans:In Snowflake, all the data is compressed by default. Snowflake provides compatibility to choose the best compression algorithm and does not and has the end users’ configuration. Snowflake’s most important thing is that the charge will apply to the customers based on the final size of the data after the compression is applied.
What Can I Do To Find Out Whether This Data Science Certification Is Good For Me
It’s always beneficial to learn new talents and broaden your knowledge. This Data Science certification was created in collaboration with Purdue University and is an excellent combination of a world-renowned curriculum and industry-aligned training, making this certification in data science a superb choice.
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What Is The Purpose Of Cluster Analysis And Data Warehousing
One of the purposes of cluster analysis is to achieve scalability, so regardless of the quantity of data system will be able to analyze its ability to deal with different kinds of attributes, so no matter the data type, the attributes present in the data set can deal with its discovery of clusters with attribute shape high dimensionality which have multiple dimensions more than 2d to be precise ability to deal with noise, so any inconsistencies in the data to deal with that and interpretability.
Snowflake Interview Question #20
Question: How to optimize or improve query performance?Answer: Improving query performance would be highly dependent on what type of database we are using. Lets assume that we are using Snowflake as our data warehouse. We can do the following to optimize our query performance:
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What Is The Main Difference Between Inmon And Kimball Philosophies Of Data Warehousing
Both differ in the concept of building the Data Warehouse.
- Kimball views Data Warehousing as a constituency of data marts. Data marts are focused on delivering business objectives for departments in an organization, and the Data Warehouse is a conformed dimension of the data marts. Hence, a unified view of the enterprise can be obtained from the dimension modeling on a local departmental level.
- Inmon explains in creating a Data Warehouse on a subject-by-subject area basis. Hence, the development of the Data Warehouse can start with data from the online store. Other subject areas can be added to the Data Warehouse as their needs arise. Point-of-sale data can be added later if management decides that it is necessary.
- Hence, the process will be as follows:Kimball > First Data Marts > Combined Ways > Data WarehouseInmon > First Data Warehouse > Data marts
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What Are The Roles & Responsibilities Of A Snowflake Developer
A Snowflake Developers specific tasks vary greatly depending on the industry theyre in and the company they work for. Generally speaking, a Snowflake developer might expect to encounter some or all of the tasks and responsibilities listed below.
- Snowflake data engineers will be responsible for architecting and implementing large-scale data intelligence solutions based on the Snowflake Data Warehouse.
- They should be familiar with Snowflake utilities including SnowSQL, SnowPipe, Python, Kafka connector, Time travel, stored procedures, and more.
- They should be well-versed in Data Warehouse/ODS principles, ETL concepts, and modelling structure.
- The professional is expected to be familiar enough with Snowflake functions.
- Good understanding of agile and SDLC methodologies
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What Is Data Modeling In Power Bi
One of the features in Power BI is Data modeling. Its a process of connecting data from multiple sources from which we can discover new relationships between separate data variables. Power BI makes it easier to organize, compare, and manipulate the data based on different scenarios. You can also apply filters and create views based on the requirements. You can make use of data modeling to perform advanced calculations on the datasets and channel them to gain a competitive advantage in the business.
Explain In Short About Snowflake Clustering
In Snowflake, clustering is a type of data partitioning, where unique cluster keys are specified for each table. Cluster keys are subsets of a table’s columns that are used to co-locate data within the table. These keys are appropriate for comprehensive tables. The process of managing clustered data in a table is known as re-clustering.
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What Is Virtual Data Warehousing
- A Virtual Data Warehouse provides a collective view of the completed data. A Virtual Data Warehouse has no historic data. It can be considered as a logical data model of the given metadata.
- Virtual Data Warehousing is a de facto information system strategy for supporting analytical decision-making. It is one of the best ways for translating raw data and presenting it in the form that can be used by decision-makers. It provides a semantic mapwhich allows the end user for viewing as virtualized.
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What Is A Snowflake Cloud Data Warehouse
Snowflakeis 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.
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What All Is Covered In The Job Assistance Program
Our job assistance program is aimed at helping you land your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. However, the final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.
Snowflake Interview Question #16
Question: What is Materialized view?Answer: Materialized views in Snowflake are query-specific per-computed data set, that are stored for quick data operations.
Creating these materialized views follows a relatively simpler syntax. Heres an example of creating a materialized view:create materialized view my_viewcomment=’Materialized view for pricing’asselect price_nov, discount_3 from Pricing
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Explain What Is Snowflake Time Travel And Data Retention Period
Time travel is a Snowflake feature that gives you access to historical data present in the Snowflake data warehouse. For example, suppose you accidentally delete a table named Employee. Using time travel, it is possible to go back five minutes in time to retrieve the data you lost. Data that has been altered or deleted can be accessed via Snowflake Time Travel at any point within a defined period. It is capable of performing the following tasks within a specific/defined period of time:
- Analyzing data manipulations and usage over a specified period of time.
- Restoring data-related objects that are accidentally lost /
- Backup and duplication of data at or before specific points in the past.
As soon as the defined/specific period of time expires, the data moves into Snowflake Fail-safe and these actions/tasks cannot be performed.
What Are The Most Common Errors You Can Potentially Face In Data Modeling
These are the errors most likely encountered during data modeling.
- Building overly broad data models: If tables are run higher than 200, the data model becomes increasingly complex, increasing the likelihood of failure
- Unnecessary surrogate keys: Surrogate keys must only be used when the natural key cannot fulfill the role of a primary key
- The purpose is missing: Situations may arise where the user has no clue about the businesss mission or goal. Its difficult, if not impossible, to create a specific business model if the data modeler doesnt have a workable understanding of the companys business model
- Inappropriate denormalization: Users shouldnt use this tactic unless there is an excellent reason to do so. Denormalization improves read performance, but it creates redundant data, which is a challenge to maintain.
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What Are Recursive Relationships And How Do You Rectify Them
Recursive relationships happen when a relationship exists between an entity and itself. For instance, a doctor could be in a health centers database as a care provider, but if the doctor is sick and goes in as a patient, this results in a recursive relationship. You would need to add a foreign key to the health centers number in each patients record.
Explain The Etl Cycle’s 3
The staging layer, the data integration layer, and the access layer are the three layers that are involved in an ETL cycle.
- Staging layer: It is used to store the data extracted from various data structures of the source.
- Data integration layer: Data from the staging layer is transformed and transferred to the database using the integration layer. The data is arranged into hierarchical groups , facts, and aggregates. In a DW system, the combination of facts and dimensions tables is called a schema.
- Access layer: For analytical reporting, end-users use the access layer to retrieve the data.
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What Are The Different Types Of Scd
There are six sorts of Slowly Changing Dimensions that are commonly used. They are as follows:
Type 0 Dimension never changes here, dimension is fixed, and no changes are permissible.
Type 1 No History Update record directly. Theres no record of historical values, only the current state. A kind 1 SCD always reflects the newest values, and the dimension table is overwritten when changes in source data are detected.
Type 2 Row Versioning Track changes as version records which will be identified by the current flag & active dates, and other metadata. If the source system doesnt store versions, the info warehouse load process usually detects changes and appropriately manages them during a dimension table.
Type 3 Previous Value column Track change to a selected attribute, and add a column to point out the previous value, which is updated as further changes occur.
Type 4 History Table shows the current value in the dimension table, and all changes are tracked and stored in a separate table.
Hybrid SCD Hybrid SDC utilizes techniques from SCD Types 1, 2, and three to trace change.
Only types 0, 1, and a couple of are widely used, while the others are applied for specific requirements.
Define Staging In Snowflake
Loading data into a stage in Snowflake is known as staging. Snowflake uses other cloud services like AWS S3, GCP , Azure, or any location directly within itself to store data that has to be loaded or saved. When data is kept in another cloud region, it goes through an external stage when it is kept inside a snowflake, it goes through an internal stage.
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How Does Snowflake Store Data
Snowflake stores the data in several internal optimized and compressed micro partitions. The data is organized in a columnar format in the Snowflake cloud storage. Users cannot access or view the data objects that Snowflake stores. You can access them by conducting SQL query operations in Snowflake,
Snowflake Interview Question #13
Question: Can you tell us what is the difference between Snowflake and Redshift?Answer: While AWS Redshift has always promoted itself as an Infrastructure as a Service platform that can make data warehousing really simple for users, Redshift wont work well for you, if, lets say if you were to clone 10 TB of data in less than 5 minutes. Snowflakes capabilities to separate compute and storage would appear to be a differentiator here.
In the case of AWS Redshift, a scenario such as the one mentioned above would require over-provisioning.
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What Is The Difference Between Data Warehousing And Data Mining
A data warehouse is for storing data from different transactional databases through the process of extraction, transformation, and loading. Data is stored periodically, and it stores a vast amount of data. Some use cases for data warehouses are product management and development, marketing, finance, banking, etc. It is used for improving operational efficiency and for MIS report generation and analysis purposes.
Whereas Data Mining is a process of discovering patterns in large datasets by using machine learning methodology, statistics, and database systems. Data is regularly analyzed here and is analyzed mainly on a sample of data. Some use cases are Market Analysis and management, identifying anomaly transactions, corporate analysis, risk management, etc. It is used for improving the business and making better decisions.
Q12 Explain Snowflake Architecture
Ans. Snowflake supports a high-level architecture, as depicted in the diagram below.
- Snowflakes architecture is a hybrid of traditional shared-nothing and shared-disk database architectures. Like shared-nothing architecture, Snowflake processes the queries with massively parallel processing compute clusters, and each clusters node stores locally a part of the complete data set. Like shared-disk architecture, it uses a central data repository to create data accessible in the platform from all compute nodes.
- On the whole, snowflake architecture offers the scale-out and performance of a shared-nothing architecture, along with the data management of a shared-disk architecture.
- Snowflake architecture consists of three layers Database Storage, Cloud Services, and Query processing.
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What Is The Difference Between Database Vs Data Lake Vs Warehouse Vs Data Mart
The difference between the 3 is as follows:
Database
A database is typically structured with a defined schema so structured data can fit in a database items are organized as tables with columns, columns indicate attributes and rows indicate an object or entity. It has to be structured and filled in here within all these rows and columns. Columns represent attributes, and rows refer to an object or entity. The database is transactional and generally not designed to perform data analytics. Some examples are Oracle, MySQL, SQL Server, PostgreSQL, MS SQL Server, MongoDB, Cassandra, etc. It is generally used to store and perform business functional or transactional data. You can also take an oracle SQL course to help you learn more.
Data Warehouse
A data warehouse exists on several databases and is used for business intelligence. The data warehouse gathers the data from all these databases and creates a layer to optimize data for analytics. It mainly stores processed, refined, highly modeled, highly standardized, and cleansed data.
Data Lake
Data Mart
Data Mart lies between the data warehouse and Data Lake. Its a subset of filtered and structured essential data of a specific domain or area for a specific business need.
