Azure SQL Data Warehouse Replicated Tables

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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?

Increased storage requirements due to data duplication Signup and view all the answers

Which type of queries can benefit from using replicated tables?

Queries involving large fact tables and small dimension tables Signup and view all the answers

What is a common misconception about Apache Kafka?

It is primarily used as a message queue. Signup and view all the answers

When should Apache Kafka not be used?

When the needed capabilities exceed its limitations. Signup and view all the answers

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

When its limitations do not meet the requirements. Signup and view all the answers

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

For its extensive adoption across industries. Signup and view all the answers

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

When it processes static data only. Signup and view all the answers

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

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

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

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

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

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

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

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. Signup and view all the answers

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

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

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

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