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Graded Assignment • 30 min
What was the primary reason data warehouses were developed according to Bill Inmon's foundational definition?
Bill Inmon described a data warehouse as “a subject-oriented, integrated, nonvolatile, and time-variant collection of data in support of management’s decisions,” which underscores its role as a central repository for reporting and analysis.
What key feature distinguishes cloud data warehouses like Amazon Redshift, Google BigQuery, and Snowflake from traditional on-premises data warehouse solutions? Select all that apply.
By separating compute from storage, you can scale processing and storage resources independent of each other and pay only for the resources you have used.
Cloud data warehouses, unlike traditional on-premises systems, do not require significant upfront investments. They enable the spinning up of compute and storage clusters based on demand, by scaling up or scaling down.
What was the primary challenge associated with the first-generation data lakes, or "data lake 1.0"?
Data lakes 1.0 became data swamps due to the absence of proper data management practices. This included challenges like the lack of data cataloging and discovery tools, making it difficult to locate or understand data relationships, or ensure data integrity and quality.
You can organize the data stored in data lakes into several data zones. Which of the following statements is true about data zones?
Open file formats such as Parquet store data in a standard format that enables data interoperability and usage for multiple applications. They are also freely available for anyone to use.
Why are data partitioned in a data lake?
Data partitioning involves dividing a dataset based on criteria like time, date, or location. This technique enables faster query performance because the query engine needs to scan only the partitions relevant to the query, making the process more efficient.
What is the primary advantage of the data lakehouse architecture compared to traditional data lakes and data warehouses?
Data lakehouse architecture aims to merge the advantages of data lakes (low-cost storage for large amounts of raw data) with those of data warehouses (superior query performance and strong data management features), providing a unified solution that supports analytics, reporting, machine learning, and big data processing.
Which of the following is NOT True about open table formats?
The data files are stored in the storage layer that is built using object storage. The open table formats store metadata in their own tables that enables the discovery of data in the storage later.
Consider the following two scenarios.
Scenario A: You work at an organization where the main goal is to perform fast analytical queries on large volumes of data collected from multiple departments such as marketing, sales, and product.
Scenario B: You are looking for a cheap and scalable storage solution to store a large volume of videos and image data that you will use to later train deep learning models.
Which of the following choices best represent the suitable storage options for each scenario?
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