Practice Exam
Hadoop Hive

Hadoop Hive

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Hadoop Hive

 

The Hadoop Hive exam evaluates individuals' proficiency in using Apache Hive, a data warehousing and SQL-like query language tool built on top of Apache Hadoop, for data analysis, querying, and ETL (Extract, Transform, Load) tasks. Hive developers are responsible for writing HiveQL queries, creating and managing tables, and optimizing Hive queries for efficient data processing and analysis. This exam assesses candidates' knowledge of Hive architecture, data modeling, query optimization, and performance tuning techniques.

 

Who should take the exam?

  • Data Engineers: Data engineers, ETL developers, and database developers responsible for building data pipelines and data processing workflows using Apache Hive.
  • SQL Developers: SQL developers and analysts looking to leverage their SQL skills for querying and analyzing large-scale datasets stored in Hadoop clusters.
  • Big Data Engineers: Big data engineers and developers working with Hadoop ecosystem tools and technologies for building scalable and distributed data processing solutions.
  • Data Scientists and Analysts: Data scientists, analysts, and researchers seeking to perform exploratory data analysis, data visualization, and machine learning tasks using HiveQL queries and Hive-based analytics.
  • Database Administrators: Database administrators interested in expanding their skills to include Hive data modeling, query optimization, and performance tuning for managing and analyzing big data on Hadoop.

 

Course Outline

The Hadoop Hive exam covers the following topics :-

 

  • Module 1: Introduction to Apache Hive
  • Module 2: Understanding HiveQL Basics
  • Module 3: Understanding Hive Data Modeling
  • Module 4: Understanding Hive Data Manipulation
  • Module 5: Understanding Hive Query Optimization
  • Module 6: Understanding Hive Partitioning and Bucketing
  • Module 7: Understanding Hive Joins and Subqueries
  • Module 8: Understanding Hive Data Serialization Formats
  • Module 9: Understanding Hive Performance Tuning
  • Module 10: Understanding Hive Integration with Hadoop Ecosystem

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Hadoop Hive FAQs

Hive is built on Hadoop and optimized for batch processing and read-heavy workloads using schema-on-read.

A data warehouse system for querying large datasets stored in Hadoop using a SQL-like language.

No, Hive is not ideal for real-time operations—it’s designed for large-scale batch processing.

Hive supports MapReduce, Tez, and Spark as execution engines.

Partitioning divides tables based on column values for faster query execution and data filtering.

Hive supports ORC, Parquet, Avro, JSON, and plain text formats.

Yes, but it’s limited and not as efficient as in traditional databases.

Not ideal—Hive excels with large volumes of data; for small datasets, traditional RDBMS may be better.

Yes, Hive connects to BI tools via JDBC/ODBC drivers.

Using tools like Apache Ranger or Sentry for access control and auditing.