What Is Apache Hive?

Apache Hive is a distributed data warehouse system built on Apache Hadoop that supports scalable batch processing and analysis of large data sets using an SQL-like query language called HiveQL.

Meta launched Apache Hive in 2010 to juggle large data workloads as social media popularity and operational demand increased.

Today, Apache Hive is widely used by social media outlets and corporations alike. 

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How Does Apache Hive Work?

With a specialty in OLAP (online analytical processing), Apache Hive is one of the best platforms for SQL users to create queries and engage with large data sets. As part of Hive’s OLAP, users can segment and query data from multiple database systems with ease. 

Hive is not a database engine, but acts as a data warehouse system that enables querying of data stored in Apache Hadoop in a relational, table-like format.

Hive organizes tables into partitions based on column values (e.g., date or region), improving query performance by scanning only relevant partitions. Bucketing further divides data within partitions into fixed-size files, enabling more efficient joins and sampling.

Apache Hive Benefits

Scalable Batch Processing

Apache Hive is optimized for batch processing at scale. Compiling large volumes of data into smaller batches allows the system to effectively read data input and write a well-informed output in response, without the system becoming overwhelmed by other competing data sets.

Integration With Apache Hadoop

Additionally, the system is closely integrated with Apache Hadoop, Apache’s open-source framework. Hadoop contains data sets ranging in size. Hive’s close proximity to Hadoop allows Hive to speedily analyze larger data sets than its competitors, which makes Hive a high-volume data center. 

Apache Hive Challenges

There are a few things that Apache Hive doesn’t do as well as its competitors.

Not Compatible With OLTP Actions

Apache Hive is not compatible with OLTP (online transaction processing) actions. Tasks like online banking, shopping and instant messaging features are unavailable to users working with Hive. Hive is designed for batch analytics, not real-time or low-latency operations, and is better suited for large-scale, periodic data processing tasks.

Slower Queries Due to “Schema on Read” Model

Apache Hive uses a “schema on read” approach, which allows users to load raw data without defining a schema upfront, speeding up data ingestion and enabling faster access to queryable data. While this can save time in early stages of analysis, it may come at the cost of slower query performance compared to “schema on write” systems. Hive is best suited for batch queries, not real-time updates.

Apache Hive vs. Apache HBase

While both Apache Hive and Apache HBase operate within the Apache Hadoop ecosystem, Hive is built for batch analytics using SQL-like queries, whereas HBase is a NoSQL database optimized for low-latency, random read/write access to large unstructured data sets.

HBase also isn’t closely integrated to Hadoop like Hive. Since HBase was designed to store and process unstructured data, there are structural limitations that inhibit the system from processing data as quickly as Hive.

If you’re interested in analytical data querying, Apache Hive may be the better system. 

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Is Apache Hive Still Used?

Aside from Meta, there are many companies that use Apache Hive for its speed and trustworthiness.

For example, Airbnb prefers to use Apache Hive when processing its vacation rental data to keep their millions of clients satisfied.

Similarly, Vanguard, an investment management company, uses Apache Hive to manage their data pertaining to their global assets.

Ultimately, Apache Hive is best suited for large companies with heavy data loads that require daily completion. 

Frequently Asked Questions