Azure SQL Data Warehouse Replicated Tables
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Questions and Answers

Which statement about replicated tables in Azure SQL Data Warehouse is true?

  • They are suitable for large fact tables in a star schema
  • They are recommended for slowly changing dimension tables
  • They should be used for all tables to improve query performance
  • They reduce data movement by making data available across all compute nodes (correct)

    • Why are replicated tables ideal for small star-schema dimension tables?

  • Replicated tables are always faster than distributed tables for any schema
  • The fact table is often distributed on a column incompatible with connected dimension tables (correct)
  • Dimension tables are updated more frequently than fact tables, so replicating them reduces lock contention
  • Dimension tables are typically larger than fact tables, so replicating them improves performance

    • Which type of table distribution should be changed to replicated for improved performance?

  • Replicated fact tables
  • Hash-distributed fact tables
  • Hash-distributed dimension tables
  • Round-robin distributed dimension tables (correct)

    • What is a potential drawback of using replicated tables in Azure SQL Data Warehouse?

    <p>Increased storage requirements due to data duplication</p> Signup and view all the answers

    Which type of queries can benefit from using replicated tables?

    <p>Queries involving large fact tables and small dimension tables</p> Signup and view all the answers

    What is a common misconception about Apache Kafka?

    <p>It is primarily used as a message queue.</p> Signup and view all the answers

    When should Apache Kafka not be used?

    <p>When the needed capabilities exceed its limitations.</p> Signup and view all the answers

    What is a key factor in disqualifying Apache Kafka as the right tool for a job?

    <p>When its limitations do not meet the requirements.</p> Signup and view all the answers

    Why is Apache Kafka often considered the de facto standard for data streaming?

    <p>For its extensive adoption across industries.</p> Signup and view all the answers

    In what scenario would Apache Kafka be wrongly perceived as a message queue?

    <p>When it processes static data only.</p> Signup and view all the answers

    How does the blog post suggest evaluating when Apache Kafka should not be used?

    <p>By understanding its limitations and matching them with project requirements.</p> Signup and view all the answers

    What is the primary reason that Kafka is considered unique and successful?

    <p>Kafka combines characteristics like scalability, reliability, and real-time processing in a single platform.</p> Signup and view all the answers

    What is the main reason why Kafka is considered complementary, not competitive, to other data streaming technologies?

    <p>Kafka can be combined with other technologies such as databases, data lakes, and IoT platforms to address various needs.</p> Signup and view all the answers

    What is the relationship between Apache Kafka and Apache Flink in the data streaming landscape?

    <p>Apache Flink is becoming the de facto standard for stream processing, while Kafka Streams is not going away and is the better choice for specific use cases.</p> Signup and view all the answers

    What is the main recommendation regarding the use of Apache Kafka?

    <p>Apache Kafka should be combined with other technologies, such as databases, data lakes, and IoT platforms, to address various needs.</p> Signup and view all the answers

    What is the current status of Apache Kafka in the data streaming landscape?

    <p>Apache Kafka is the de facto standard used by over 100,000 organizations, and it is the dominant technology in the market.</p> Signup and view all the answers

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