AWS Certified Data Analytics Specialty 2021 - Hands-On! - Hive on Amazon Elastic MapReduce (EMR)

The video tutorial covers Hive, a tool for executing SQL-like queries on unstructured data in Hadoop and AWS environments. It explains Hive's advantages, such as its familiar SQL syntax and scalability for data warehouse applications. The tutorial also discusses Hive's optimization, extensibility, and the role of the Hive Metastore in structuring data. Additionally, it explores Hive's integration with AWS services like Glue, RDS, and S3, and its use on EMR with DynamoDB. The video emphasizes Hive's suitability for OLAP queries and its limitations for OLTP applications.

What is the primary function of Hive in data processing?

• To manage network traffic

• To execute SQL-like queries on unstructured data

• To provide a graphical user interface for data analysis

• To store data in a structured format

Which processing engine is NOT mentioned as an alternative to MapReduce for Hive?

• Apache Flink

• Tez

• Apache Spark

• Hadoop

What is a key advantage of using Hive for data analysis?

• It requires no setup or configuration

• It is optimized for OLTP applications

• It uses a familiar SQL-like syntax

• It provides real-time data processing

What is the Hive metastore primarily used for?

• Executing SQL queries

• Storing raw data files

• Managing user access and permissions

• Storing metadata for unstructured data

Which AWS service can serve as a Hive metastore for better integration?

• Amazon EC2

• AWS Glue Data Catalog

• AWS Lambda

• Amazon S3

How does Hive on EMR handle data writing to Amazon S3?

• It uses temporary files

• It writes directly without temporary files

• It requires manual data transfer

• It uses a third-party tool for data writing

What capability does Hive on EMR provide for DynamoDB data?

• Real-time data streaming

• Data visualization

• Joint operations between DynamoDB tables

• Automatic data encryption