Big Data Ecosystem
What is Hive?
Apache Hive is a data warehouse system built on top of Hadoop.
It provides an SQL-like interface (HiveQL) to query data stored in HDFS (Hadoop Distributed File System) or other compatible storage systems.
Hive itself is not a database — it doesn’t store the data; rather, it stores metadata about the data (schemas, tables, partitions) in a Metastore (usually backed by MySQL/Postgres).
When you run a query in Hive, it translates your SQL-like query into MapReduce/Spark/Tez jobs under the hood.
In short: Hive = SQL on top of Hadoop/Spark for querying big data.
What is S3?
Amazon S3 (Simple Storage Service) is a cloud object storage service provided by AWS.
- It’s one of the most widely used storage systems in the world.
- You can store any type of data: documents, images, videos, backups, logs, analytics datasets, etc.
- It’s designed for durability, scalability, and availability.
Think of S3 as an infinite hard drive in the cloud — you can dump raw files in, and AWS will manage the infrastructure, scaling, and redundancy for you.
Hive vs. S3
| Feature | Hive | S3 |
|---|---|---|
| Type | Data warehouse / query engine (metadata + SQL interface) | Object storage service |
| Storage | Does not store data itself; uses HDFS, S3, or other storage backends | Stores raw files (CSV, Parquet, JSON, ORC, etc.) as objects |
| Querying | SQL-like queries via HiveQL | Cannot query directly (needs Athena, Presto, or Spark) |
| Schema | Provides schema on top of raw files (“schema on read”) | Schema-less — just stores files |
| Use case | Structured queries; analytics on big data | Durable, scalable storage of raw/unstructured/structured data |
You can actually store Hive tables in S3. The data lives in S3, and Hive just defines the schema and provides the SQL query interface.
Hive is not storage → it’s a querying layer (SQL on top of big data).
S3 is storage only → it needs a query engine (Athena, Hive, Spark, Presto, etc.).
Hive is useful when you already have Hadoop/Spark infrastructure and want SQL access.
In modern cloud environments, people often use Athena (AWS), BigQuery (GCP), or Snowflake instead of Hive, because they’re fully managed, faster, and easier.