AWS Certified Data Analytics Specialty 2021 – Hands-On - Hive on 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 SQL syntax, scalability, and use in data warehousing. The tutorial delves into the Hive metastore, which structures unstructured data, and discusses integration with AWS services like Glue and RDS. It also highlights Hive's capabilities with S3 for data partitioning and advanced features on EMR, including DynamoDB integration.

What is the primary advantage of using Hive for data warehouse applications?

• It is faster than Apache Spark.

• It provides a familiar SQL-like syntax.

• It requires no setup on Hadoop clusters.

• It supports real-time OLTP transactions.

Which engine can Hive use as an alternative to MapReduce for processing?

• Apache Storm

• Apache Kafka

• Apache Flink

• Apache Tez

What is the role of the Hive metastore?

• To store raw data files

• To manage user access permissions

• To store metadata about data structure

• To execute SQL queries

Where is the Hive metastore stored by default?

• In a PostgreSQL database

• In a MySQL database on the master node

• In an Amazon S3 bucket

• In a DynamoDB table

How can the Hive metastore be centralized for better integration?

• By using AWS Glue Data Catalog

• By using Apache Kafka

• By integrating with Amazon CloudWatch

• By storing it in Amazon S3

What feature does Hive on EMR provide for handling data in Amazon S3?

• Automatic table partitioning

• Data compression

• Real-time data streaming

• Data encryption

How can Hive on EMR interact with DynamoDB?

• By exporting data to Amazon RDS

• By directly modifying DynamoDB tables

• By using HiveQL to perform join operations

• By using Hive to encrypt data