Data Lakes and Big Data Systems

Descripción

This quiz covers the core concepts of data lakes and their implementation in big data systems, with a focus on AWS tools like S3, Glue, Athena, and Redshift Spectrum. It explores how to structure, query, and optimize data in distributed file storage systems, emphasizing practical design considerations and performance optimization strategies.
Eladio Rocha
Test por Eladio Rocha, actualizado hace 18 días
Eladio Rocha
Creado por Eladio Rocha hace 18 días
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Resumen del Recurso

Pregunta 1

Pregunta
What is a "data lake"?
Respuesta
  • A formal database for structured data.
  • A distributed file storage system containing raw, unstructured data.
  • A highly redundant server-based database system.
  • A collection of relational database schemas.

Pregunta 2

Pregunta
Which cloud service is commonly used to implement a data lake?
Respuesta
  • Amazon RDS
  • Amazon S3
  • Amazon DynamoDB
  • Amazon EC2

Pregunta 3

Pregunta
What is the primary purpose of AWS Glue in the context of a data lake?
Respuesta
  • To store data redundantly across regions.
  • To provide a SQL interface for querying raw data.
  • To crawl unstructured data and define schemas.
  • To optimize database queries for performance.

Pregunta 4

Pregunta
What tool allows SQL queries directly on data stored in Amazon S3?
Respuesta
  • Amazon DynamoDB
  • Amazon Athena
  • Amazon ElasticSearch
  • Amazon Lambda

Pregunta 5

Pregunta
How does Redshift Spectrum enhance the capabilities of Amazon Redshift?
Respuesta
  • By integrating with Amazon Glue to create schemas.
  • By querying data stored directly in Amazon S3.
  • By offering serverless SQL querying capabilities.
  • By storing all data in highly redundant clusters.

Pregunta 6

Pregunta
Why is partitioning data important in a data lake?
Respuesta
  • To replicate data across regions for redundancy.
  • To organize raw files into predefined schemas.
  • To improve query performance by narrowing data access.
  • To ensure compatibility with Amazon Glue.

Pregunta 7

Pregunta
What is a typical partitioning strategy for storing log data?
Respuesta
  • Partitioning by file size.
  • Partitioning by data source.
  • Partitioning by date.
  • Partitioning by user ID.

Pregunta 8

Pregunta
How should you approach data lake architecture from a system design perspective?
Respuesta
  • Design the data lake structure based on how end-users will query the data.
  • Store all data in a single bucket without structure to maximize flexibility.
  • Focus exclusively on schema design before considering query patterns.
  • Prioritize database migration over partitioning strategies.

Pregunta 9

Pregunta
What is one advantage of using off-the-shelf tools like AWS Glue and Amazon Athena?
Respuesta
  • They allow complete control over low-level data management.
  • They eliminate the need to think about data structure.
  • They enable scalable and reliable big data solutions with minimal custom design.
  • They prevent redundancy in cloud storage systems.
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