Apache Hive is a data warehouse system designed to operate on a large scale. Facebook (now Meta) launched Hive in 2010 to juggle large data workloads as social media popularity and operational demand skyrocketed. With a primary focus on fast data analysis and rapid online insights, Hive granted Facebook the ease, flexibility and comfort to function efficiently on a widespread basis. Since then, Hive has become part of the Apache-Hadoop project. Apache Hive is widely used by social media outlets and corporations alike.
What Is Apache Hive Used For?
How Does Apache Hive Work?
With a specialty in OLAP (online analytical processing), 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.
It’s important to note Hive is not a relational database, a type of database that organizes data into tables based on related points. Rather, Hive organizes data into similar tables based on unit size. These tables are made of separate partitions to divide tables into different parts based on data information. These partitions can be broken down even further with a process called bucketing that breaks data down for fast data queries.
Apache Hive Benefits
Apache Hive is unmatched when it comes to speed, and that’s because it uses batch processing to interpret data. 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.
Additionally, the system is closely integrated with 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. 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. Because Hive is used for real-time operations, its response time to data queries must be prioritized over other functions, like core OLTP actions.
Additionally, Hive is not a “schema on write” database, which means it doesn’t create a schema (or structure) for data when you enter it into the database. Instead, Hive is a “schema on read” database that loads data without the need to create a schema and allows you to begin working with data right away. While many love schema on write databases for query speed and precision, schema on read saves valuable time. Furthermore, Hive is purely an online query answering system with functional benefits that can be used for real-time queries or updates.
Apache Hive vs. Apache HBase
Be careful not to confuse Apache Hive with Apache HBase. While the two systems have similar names, they differ wildly when it comes to quality and performance. While Apache Hive gives you multiple supported file formats, reliable batch processing and structured and unstructured data support, Apache HBase falls short in all of these areas.
HBase 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 is the better system.
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 $7 trillion in global assets. Ultimately, Apache Hive is best suited for large companies with heavy data loads that require daily completion.