Top 10 Frequently Asked Questions (FAQs) in Hive Tutorial

Top 10 Frequently Asked Questions (FAQs) in Hive Tutorial

Top frequently asked tech questions cover a wide range, including technical concepts (like what is a foreign key or hive , troubleshooting scenarios (e.g., how to debug a crashing program), system design (e.g., how would you design a social media app?), and behavioral and situational aspects (e.g., what's the most challenging project you've worked on? 

1. What is Apache Hive?

Answer: Apache Hive is a data warehouse infrastructure built on top of Hadoop. It provides an SQL-like interface (HiveQL) to query and manage large datasets stored in HDFS.

2. What is the difference between a Managed Table and an External Table in Hive?

Answer:

• Managed Table: Hive manages both the table data and metadata. Dropping the table deletes the data.

• External Table: Hive only manages the metadata. Dropping the table does not delete the data.

3. What file formats does Hive support?

Answer: Hive supports various file formats including Text, ORC, Parquet, Avro, SequenceFile, and RCFile. ORC and Parquet are preferred for performance.

4. What is Partitioning in Hive and why is it used?

Answer: Partitioning divides data into subdirectories based on column values (e.g., dt=2025-09-10) to reduce scan size and improve query performance.

5. What is Bucketing in Hive?

Answer: Bucketing distributes data into a fixed number of files (buckets) based on the hash of a column value. It improves join performance and parallelism.

6. What is the difference between Hive and HBase?

Answer:

• Hive: SQL-like query engine for batch processing on HDFS.

• HBase: NoSQL key-value store for real-time read/write access.
  Hive is for analytical queries, while HBase is for real-time access.

7. How can you improve Hive query performance?

Answer: Use ORC/Parquet, partitioning, bucketing, Tez engine, vectorization, statistics (ANALYZE), and optimize joins (map join or bucket join).

8. What are User-Defined Functions (UDFs) in Hive?

Answer: UDFs are custom functions created by users to extend Hive’s functionality when built-in functions are not sufficient.

9. What is the role of the Hive Metastore?

Answer: The Hive Metastore stores metadata about tables, partitions, and schemas. It is typically backed by a relational database like MySQL or PostgreSQL.

10. Can Hive work with other big data tools?

Answer: Yes. Hive integrates with Apache Spark, Pig, HBase, Flume, and Sqoop to enable powerful ETL pipelines and analytics on Hadoop ecosystems.

These FAQs help beginners quickly understand the most essential Hive concepts and are useful for interviews, exams, and practical learning.