Snowflake Inc. Is a cloud-based data-warehousing startup that was founded in 2012. It has raised more than $900 million in venture capital, and is based in San Mateo, California. It was publicly launched by Bob Magalia in 2014 after two years in stealth mode.
Snowflake offers a cloud-based data storage and analytics service, generally termed “data warehouse-as-a-service”. It allows corporate users to store and analyze data using cloud-based hardware and software.
Snowflake runs on Amazon S3 since 2014, and on Microsoft Azure since 2018 it is being rolled out on Google Cloud Platform in 2019. Its Snowflake Data Exchange allows customers to discover, exchange and securely share data.
The Snowflake data warehouse is a cloud-based tool that supplies companies with flexible and scalable storage while simultaneously hosting solutions for BI. Snowflake’s scalable architecture and lightweight querying make it an ideal tool for companies that are starting their exploration of the data-driven model.
An interview is essentially a structured conversation where one participant asks questions, and the other provides answers. In common parlance, the word “interview” refers to a one-on-one conversation between an interviewer and an interviewee.
The interviewer asks questions to which the interviewee responds, usually so information is offered by the interviewee to interviewer — and that information may be used or provided to other audiences, whether in real time or later.
1. Explain the chameleon method used in Data Warehousing?
Chameleon is a hierarchical clustering algorithm that overcomes the limitations of the existing models and methods present in Data Warehousing. This method operates on the sparse graph having nodes that represent data items and edges which represent the weights of the data items.
This representation allows large datasets to be created and operated successfully. The method finds the clusters that are used in the dataset using the two-phase algorithm.
· The first phase consists of the graph partitioning that allows the clustering of the data items into a large number of sub-clusters.
· The second phase uses an agglomerative hierarchical clustering algorithm to search for the clusters that are genuine and can be combined together with the sub-clusters that are produced.
2. 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 map—which allows the end user for viewing as virtualized.
6. What is Active Data Warehousing?
Data Warehouse represents a single state of a business. Active Data Warehousing considers the analytic perspectives of customers and suppliers. It helps deliver the updated data through reports.