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What Is Hive In Big Data?

Vrinda Mathur
Aug 18, 2023

Hive is a data warehouse and ETL tool that provides a SQL-like interface between the user and Hadoop's distributed file system (HDFS). It is developed on the Hadoop platform. It is a software project that allows users to query and analyze data. It makes it easier to read, write, and handle large datasets stored in distributed storage and queried using Structure Query Language (SQL) syntax. It is not designed to handle Online Transaction Processing (OLTP) demands. It is frequently used for data warehousing jobs such as data encapsulation, Ad-hoc Queries, and large dataset analysis. Its input formats are intended to improve scalability, extensibility, performance, fault-tolerance, and loose coupling.

Initially developed by Facebook, Amazon, and Netflix, Hive provides typical SQL functionality for analytics. To execute SQL applications and SQL queries across distributed data, traditional SQL queries are built in the MapReduce Java API. Hive is portable since most data warehouse systems use SQL-based query languages such as NoSQL.

Apache Hive is a data warehouse software project built on the Hadoop platform. It provides a SQL-like interface for querying and analyzing massive datasets stored in Hadoop's distributed file system (HDFS) or other storage systems.

What is Hive?

Hive is a data warehouse system for analyzing structured data. It is developed on the Hadoop platform. It was created by Facebook.

Hive provides the ability to read, write, and manage huge data sets stored in distributed storage. It executes SQL-like queries known as HQL (Hive query language) that are internally transformed to MapReduce jobs. We may avoid the typical technique of building complex MapReduce programmes by using Hive. Hive supports DDL, DML, and UDF.

When it comes to huge data (to be analyzed exponential data), Apache Hive is a highly efficient technology. The concept of hive big data is highly widespread in the technological arena. It is a warehouse data software that enables the data analysis process of big data on a regular basis.

Because data is organized and structured in the Apache Hadoop Distributed File System (HDFS), Apache Hive assists in processing and analyzing this data to provide data-driven patterns and trends. Apache Hive, which is suitable for use by organizations or institutions, is highly useful in big data and its ever-changing growth.

Also Read | What is Hive Big Data and its Benefits?

Components of Hive

The process of interfacing with multiple databases and gathering the necessary data. Understanding Hive's huge data through a data analysis lens can help us discover additional information about the Apache Hive features.

The SQL software, or Standard Language Concept Questionnaire, is used in the process of communicating with different databases and gathering the necessary data. Understanding Hive's huge data through a data analysis lens can help us discover additional information about the Apache Hive features.

Hive provides data analysis in a very straightforward and systematic manner using a bulk processing sequence, and it takes less time than previous methods. HiveQL is a SQL-like language that interacts with the Hive website and analyzes the required data in a structured fashion in diverse organizations.

Some of the components of the hive are:

The HCatalog:

It is a Hive component that serves as both a table and a store management layer for Hadoop. It enables users, in conjunction with other data processing tools such as Pig and MapReduce, to simply read and write on the grid.

WebHCat:

It provides a service that allows users to use an HTTP interface to conduct Hadoop MapReduce (or YARN), Pig, or Hive processes or function Hive metadata operations.

Driver for Hive:

The driver manages the lifespan of a HiveQL query and serves as the entry point for all Hive activities. It is in charge of parsing, compiling, optimizing, and running the query.

Compiler:

Depending on the selected execution engine, the compiler converts Hive queries into a series of MapReduce jobs or Tez tasks. To increase efficiency, it also does query optimisation such as predicate pushdown and join reordering.

Engine of Execution:

The execution engine is in charge of conducting MapReduce jobs or Tez tasks on the Hadoop cluster. It is in charge of managing all data, flow, resource allocation, and task scheduling.

Handler of Inventory:

The storage handler connects Hive to the underlying storage system, such as HDFS or Amazon S3. It is in charge of reading and writing data to and from the storage system in Hive-compatible formats.

Hive in big data is a watershed moment that finally led to large-scale data analysis. Big data is required by large organizations to record the information gathered over time.

Organizations collect data and utilize such software tools to analyze it in order to provide data-driven analysis. With Apache Hive, this data may be utilized for reading, writing, and managing information that has been organized. Storage of data has been a hot topic since the advent of data analytics.

Also Read | The Main Challenges Faced By Data-Driven Businesses

Benefits of using Hive in Big Data

Hive is an excellent choice for data optimisation and analysis, with numerous benefits that outweigh its few limitations. Hive is incredibly beneficial in Big Data. Despite its drawbacks, Hive specialists provide an unbeatable choice for data processing and analysis.

The benefits of Apache Hive have shown to be quite useful in the analysis of large amounts of data over time. Here are a few advantages that will help you better understand the notion. Among the benefits are:

Benefits of using Hive

Simple to use:

Hive in Big Data is a simple software programme that analyzes massive amounts of data through a gathering procedure. An efficient system that employs ordinary software and HiveQL, a language that is extremely similar to the SQL-programmed query language used to interface with databases.

Such software is usable by both programmers and non-programmers, making it a very accessible and user-friendly application for turning petabytes of data into meaningful data lines. This is one of the most significant advantages of Apache Hive, which has helped it become a preferred data analysis alternative among large data-intensive organizations.

Quicker Experience:

Batch processing is a method of analyzing data in bits and pieces that are then combined.The analyzed data is forwarded to Apache Hadoop. Apache Hive retains the schemas or derived stereotypes.

Fault Tolerance Software:

Error tolerance is a rare feature in many of today's Big Data management applications. However, Apache Hive and the HDFS file system collaborate in a faulty fashion that relies on replica generation. This means that once massive data has been analyzed on Hive, it is instantly reproduced on other servers. This is done to avoid data loss or scheme failure if a machine dies or ceases working.

Tolerance of Hadoop (Hive) defects is one of Hive's most significant advantages, as it outperforms competitors such as Impala and distinguishes Hive in its own right.

Effective Software:

Apache Hive is an excellent data analysis tool since it allows users to access and publish data in an organized manner.Oozie creates data analysis schemas and stores them in the Hadoop Distributed File System (HDFS) for later use. This makes it simple to retrieve and reuse this information anytime it is required.

Cheap option:

Another advantage of Apache Hive is that it is a relatively inexpensive choice. Profit is critical for major corporations. Profit margins can, however, be reduced by new technology and pricey software tools. As a result, organizations must seek low-cost alternatives to achieve the same goals with more cost-effective methods. When it comes to big data research and data analysis,

Apache Hive is one of the greatest tools available. It is quick and simple, it works well, and it relies on tolerance to deliver greater results.

Future of Hive in Big Data

The value of Hive in Big Data is finally fading. With increasing cloud software like Google Bigquery doing well for quick data tracking, Apache Hive takes a back seat as its product gradually deteriorates in the market.

The disadvantages of the hive are as follows:

Hive is designed for batch processing, which means that it may not be the optimal solution for real-time data processing.

Because Hive is built on top of Hadoop, which is optimized for batch processing rather than interactive querying, it can be slower than typical relational databases.

Hive employs a SQL-like language, but it still requires users to be familiar with Hadoop and distributed computing, which might make it challenging for newcomers to use.

Because Hive does not support transactions, it can be challenging to maintain data consistency.

Hive is not as flexible as other data warehousing technologies because it is created primarily for use with Hadoop, which limits its usability in other systems.

The Hive's future in big data predictions does not appear to be bright, yet it remains one of the best software of its time. As massive current data grows in size and distribution, the Hive is a slower process compared to others.With many academics and technology experts labeling Apache Hive "dead," the software's future might be summarized as a decreasing trip.

To summarize, Apache Hive was released in October 2010 with the goal of making large data analysis more accessible across organizations. Hive developed as one of the top big data software solutions of its time, being fast and familiar, efficient and dependable. Even while the software's future looks bleak, it has undoubtedly been a driving force in propelling big data analysis to its pinnacle during the last decade. With more and more competitors appearing, the programme remains distinctive in terms of its highly valued features.

Also Read | How are Big Data and AI Working Together in the Modern World?

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What Is Hive In Big Data?

What is Hive?

Components of Hive

The HCatalog:

WebHCat:

Driver for Hive:

Compiler:

Engine of Execution:

Handler of Inventory:

Benefits of using Hive in Big Data

Simple to use:

Quicker Experience:

Fault Tolerance Software:

Effective Software:

Cheap option:

Future of Hive in Big Data

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