Chapter 4 Building a Modern Cloud Data Platform with Databricks

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Questions and Answers

What makes it challenging to combine appends and reads, and batch and streaming jobs?

  • The lack of consistency and isolation of data lakes (correct)
  • The optimization of data warehouses for structured data
  • The need for flexibility in data management systems
  • The requirement for real-time monitoring and SQL analytics

What has been a consequence of the limitations of data lakes?

  • The development of more specialized systems for diverse data applications
  • The loss of benefits of data warehouses (correct)
  • The reduction of the need for high-performance data management systems
  • The materialization of all the promises of data lakes

What is the primary drawback of the data warehouse approach?

  • High operational cost and scalability
  • Vendor lock-in and limited data formats (correct)
  • Inability to handle large amounts of data
  • Lack of competitiveness and reputation

What type of data are recent advances in AI primarily focused on processing?

<p>Unstructured data (C)</p>
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What is a common approach to address the increasing needs of data management?

<p>Using multiple systems, including data lakes, data warehouses, and specialized systems (B)</p>
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What is the main advantage of the lakehouse approach?

<p>Cost-effective and scalable (B)</p>
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Why is scalability essential in DM solutions?

<p>It contributes to competitiveness, efficiency, reputation, and quality (B)</p>
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What is a drawback of using multiple systems for data management?

<p>Additional complexity and delays in data movement (B)</p>
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What is the primary benefit of using a single, unified data management system?

<p>Reduced complexity and delays in data movement (B)</p>
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What is the primary reason for companies being forced to create multiple data copies?

<p>To make data accessible to other third-party systems (B)</p>
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What is the characteristic of data storage in a lakehouse?

<p>Stored in open data formats (D)</p>
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Why is the data warehouse approach less cost-efficient?

<p>Due to high operational cost and vendor lock-in (D)</p>
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What is a key benefit of using a lakehouse for data analytics and machine learning?

<p>Unifying all data and running analytics and ML in a single place (B)</p>
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What is one of the challenges that a lakehouse approach can overcome?

<p>Data becoming stale (B)</p>
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What does a lakehouse approach provide in terms of data access?

<p>Complete and firm copy of all data in a centralized location (B)</p>
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What is a benefit of using open formats and open standards in a lakehouse approach?

<p>Easy movement of data to a different vendor or technology (A)</p>
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What is one of the benefits of unifying data teams in a lakehouse approach?

<p>All data teams can work together on one architecture (C)</p>
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What type of data can be managed in a lakehouse approach?

<p>Both structured and unstructured data (A)</p>
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What benefits does the lakehouse approach provide in terms of data reliability?

<p>ACID transaction with atomicity, consistency, isolation, and durability guarantees (A)</p>
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What is a characteristic of the lakehouse approach to data pipelines?

<p>It enables real-time analytics with streaming data (C)</p>
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What is the purpose of Delta Lake in the lakehouse approach?

<p>To bring data reliability to an existing data lake (D)</p>
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What is a benefit of the streamlined data pipeline setup in the lakehouse approach?

<p>Reduced compute times and costs with scalable cloud runtime (D)</p>
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What is a characteristic of the Spark clusters used in the lakehouse approach?

<p>They are highly optimized (D)</p>
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What is the purpose of the lakehouse approach?

<p>To provide modern data engineering best practices for improved productivity, system stability, and data reliability (D)</p>
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What type of transactions are necessary to ensure that multiple data pipelines can read and write data reliably on the same table?

<p>ACID transactions (C)</p>
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What type of data processing is enabled by Delta Lake across batch and streaming?

<p>Unified streaming and batch data processing (B)</p>
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What is the primary goal of creating a central source of truth for business intelligence applications?

<p>To have a single source of truth for BI applications (A)</p>
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What is a common challenge faced by companies in business intelligence?

<p>Data is incomplete and stale in a data warehouse (A)</p>
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What is the benefit of using Delta Lake for data reliability?

<p>It enables data reliability across batch and streaming (B)</p>
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What is the result of having a central source of truth for business intelligence applications?

<p>End-users receive complete, reliable, and up-to-date data (A)</p>
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Chapter 4 Building a Modern Cloud Data Platform with Databricks.pdf

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