What is Apache Hive?
Apache Hive is a data warehouse software project developed by Apache Software Foundation. It facilitates reading, writing, and managing large datasets in distributed storage and offers an SQL interface for querying data stored in a Hadoop cluster.
History
Built originally by Facebook, Apache Hive evolved as an Apache open-source project from 2010. It was designed to enable easy data summarization, ad-hoc querying, and the analysis of large volumes of data.
Functionality and Features
Apache Hive supports data stored in Hadoop Distributed File System (HDFS) and compatible file systems like Amazon S3 filesystem. It enables users to do data mining with SQL-like queries (Hive QL) while also supporting traditional map/reduce. Hive's key features include:Data summarizationAd-hoc queryingSchema on readBuilt-in User Defined Functions (UDFs)
Architecture
Apache Hive follows a three-tier architecture consisting of the User Interface, the Hive Driver (Compiler, Optimizer, Executor), and the Metadata storage in RDBMS.
Benefits and Use Cases
Hive is well-suited for batch jobs, data summarization, and data analysis. Its SQL-like interface makes it user-friendly for those familiar with SQL, enabling quick querying of data. It's often used in industries with large datasets like finance, marketing, and sales.
Challenges and Limitations
While Apache Hive offers many advantages, it has some limitations. For example, it's not designed for real-time queries or row-level updates. Also, it has limited subquery support.
Integration with Data Lakehouse
In a data lakehouse environment, Apache Hive's role is to provide SQL-like access and processing of the structured and semi-structured data, making it a critical component for preparing and transforming data.
Security Aspects
Hive provides a range of security features, including Hadoop's inherent security via Kerberos as well as role-based access control and column-level access control with Apache Ranger.
Performance
Apache Hive may not be the fastest option for data querying. However, it's designed for scalability and can handle large amounts of data efficiently by distributing the workload among different nodes in the cluster.
Comparisons
In comparison to similar data processing tools like Pig and Spark, Hive stands out for its SQL-like query language, which makes it user-friendly for SQL developers. However, it's less performant than Spark for complex data processing tasks.
FAQs
Is Apache Hive suitable for real-time processing? No, Apache Hive is not designed for real-time processing. It's more suitable for batch processing, data analysis, and reporting.
What kind of data can Apache Hive handle? Apache Hive can handle structured and semi-structured data stored in a Hadoop cluster.
Does Apache Hive replace Hadoop? No, Apache Hive is a component of the Hadoop ecosystem and works on top of Hadoop to provide a SQL interface for data querying.
How does Apache Hive work with a data lakehouse? Apache Hive can be used in a data lakehouse setup to provide SQL-like data querying and processing.
Can Apache Hive work with unstructured data? It's not ideal. While it can work with semi-structured data like JSON and XML, Hive is best suited for structured data.
Glossary
Hadoop Distributed File System (HDFS): A distributed and scalable file system for the Hadoop framework.HiveQL: A SQL-like scripting language for querying data in Hive.
Data lakehouse: A hybrid data management platform that combines the features of a data warehouse and a data lake.
Apache Ranger: A framework to enable, monitor and manage comprehensive data security across the Hadoop platform.
Batch Processing: The processing of data blocks where a group of transactions is processed all at once.
