--- description: ¿Qué piensan los usuarios de Apache Hive? Lee las opiniones y opiniones verificadas, descubre sus características y el nivel de usabilidad, consulta los precios y conoce las ventajas y desventajas de Apache Hive. image: https://gdm-localsites-assets-gfprod.imgix.net/images/capterra/og_logo-e5a8c001ed0bd1bb922639230fcea71a.png?auto=format%2Cenhance%2Ccompress title: Apache Hive - Opiniones, precios y características - Capterra España 2026 --- Breadcrumb: [Inicio](/) > [Herramientas ETL](/directory/31099/etl/software) > [Apache Hive](/software/170238/apache-hive) # Apache Hive Canonical: https://www.capterra.es/software/170238/apache-hive Página: 1 de 2\ Siguiente: [Página siguiente](https://www.capterra.es/software/170238/apache-hive?page=2) > La solución ETL que les permite a los usuarios ejecutar consultas a conjuntos de datos grandes, ubicados en Hadoop, agregarlos y proporcionar análisis de datos. > > Veredicto: 17 usuarios lo han valorado con **4.2/5**. De los mejores valorados en **Probabilidad de recomendación**. ----- ## Estadísticas y valoraciones rápidas | Métrica | Valoración | Detalles | | **En general** | **4.2/5** | 17 Opiniones | | Facilidad de uso | 4.1/5 | Basado en las opiniones generales | | Asistencia al cliente | 4.2/5 | Basado en las opiniones generales | | Relación calidad-precio | 4.5/5 | Basado en las opiniones generales | | Funcionalidades | 4.0/5 | Basado en las opiniones generales | | Porcentaje de recomendaciones | 70% | (7/10 Probabilidad de recomendación) | ## Sobre el proveedor - **Empresa**: Apache Software Foundation - **Constitución**: 2011 ## Contexto comercial - **Público objetivo**: 1.001-5.000, 5.001-10.000, 10.000+ - **Países disponibles**: Alemania ## Categoría - [Herramientas ETL](https://www.capterra.es/directory/31099/etl/software) ## Alternativas 1. [EasyMorph](https://www.capterra.es/software/171372/easymorph) — 4.8/5 (9 reviews) 2. 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[Boomi](https://www.capterra.es/software/5520/boomi) — 4.4/5 (273 reviews) ## Opiniones ### "SQL approach of processing data from a distributed file system" — 4.0/5 > **Monish** | *20 de abril de 2020* | Software informático | Valoración de la recomendación: 8.0/10 > > **Puntos a favor**: Data on a distributed filesystem such as HDFS or S3 can be used directly for processing and modified using SQL with the help of HIVE, No need for writing complex java programs for executing map reduce, HIVE as implemented a SQL way of executing Map Reduce job's Hive has many configuration queries which increases the scope of optimisation for the under ling MR jobs, example sort mb , parallelism, number of reducers, size per reducer etc ... Hive tables data are always stores on files, even if its not a external table, these files can be directly used as a input for a MR job HIVE sql syntaxes are quit similar to that of mysql, HIVE provides good java libraries, such as sqoop which helps data table schema and data transfer from DB to HIVE etc ... > > **Desventajas**: HIVE queries are comparatively slower than the native DB's such as mysql, snowflake or psql, meta data's and caching can be maintained to improve the performances > > I am having a Good Experience, Hive Has been a great help to the big data world, performance is the only problem which is reasonable since it has to deal with distributed file system ----- ### "Hive is the goldmine for data enthusiasts" — 5.0/5 > **Vidya** | *27 de abril de 2025* | Tecnología y servicios de la información | Valoración de la recomendación: 9.0/10 > > **Puntos a favor**: Hive is a great capable data warehouse tool for data analysis and performing etl transformations at large scale > > **Desventajas**: Hive processing is not very efficient in producing faster runs in line with other emerging tools like spark > > Overall Hive emerged as a great query engine with distributed processing and very capable for data analytics ----- ### "Review on Apache Hive" — 3.0/5 > **Mallikarjuna** | *12 de septiembre de 2020* | Tecnología y servicios de la información | Valoración de la recomendación: 2.0/10 > > **Puntos a favor**: As a user I would pass some good things about hive it’s interface for Hadoop and it’s interfaces to different databases along with file system and we can integrate relation between file to file too > > **Desventajas**: It’s very flexible and good at performance while load data from larger files and it’s good interface between homo and heterogeneous databases > > I would strongly recommend to all to use and get an experience with this software ----- ### "A useful Data Warehouse for all the BigData enthusiasts" — 4.0/5 > **Usuario verificado** | *14 de febrero de 2023* | Tecnología y servicios de la información | Valoración de la recomendación: 7.0/10 > > **Puntos a favor**: It is simple to use as it really feels like a Database with its SQL like framework that easily parses queries behind the scenes into Mapreduce capable of supporting both internal and external data tables. It assins data to the machines in a cluster for faster performance with fault-tolerant capability to protect data at all cost. > > **Desventajas**: Hive looks for data in the local machine and not HDFS, so there is no direct way to transfer files/data from local machine to HDFS (need to use external application). Execution time is not so fast as it takes a long time especially when there is a usage of joins in the queries as it relies on external disc space compared to Spark that uses in-memory space leading it to be more faster than hive. When hive is restarted, all the metadata gets erased. > > Basically you can store all the data that is structured in Hive built on top of Hadoop, that enables to store data easily and query them using SQL ----- ### "The Bigdata DatawareHouse that works seamlessly with Spark\!\!" — 5.0/5 > **Diego** | *21 de junio de 2021* | Marketing y publicidad | Valoración de la recomendación: 10.0/10 > > **Puntos a favor**: I love how easy is to integrate Apache Hive with Spark and perform SQL queries as if the tables were stored on Hadoop or S3 or GCP buckets. It is also very familiar to Spark users of tables stored on other file systems since it is based on the same storage (Hadoop HDFS) as regular Spark. And the best feature is that it is open source\!\! So, no extra cost for licensing\!\! > > **Desventajas**: One thing is regarding its limitation of only being able to work with structured data and only being able to query tables, but for the regular use we do on our company it is more than enough (we do not have much unstructured data anyways). > > Apache Hive has solved us the need of doing repetitive transformation over the final clean tables processed by our ETL process for all our analytical and business analytics tasks, now that we have the Data Warehouse in place we no longer have to extract summary extracts or perform any repetitive queries we did in the past, now we have designed a robust star schema with the main KPIs and calculations with all the look up tables we need and all without switching from technology or framework all in the same Apache Spark project\!\! ----- Página: 1 de 2\ Siguiente: [Página siguiente](https://www.capterra.es/software/170238/apache-hive?page=2) ## Enlaces - [Ver en Capterra](https://www.capterra.es/software/170238/apache-hive) ## Esta página se encuentra disponible en los siguientes idiomas | Local | URL | | de | | | de-AT | | | de-CH | | | en | | | en-AE | | | en-AU | | | en-CA | | | en-GB | | | en-IE | | | en-IL | | | en-IN | | | en-NZ | | | en-SG | | | en-ZA | | | es | | | es-AR | | | es-CL | | | es-CO | | | es-CR | | | es-DO | | | es-EC | | | es-MX | | | es-PA | | | es-PE | | | fr | | | fr-BE | | | fr-CA | | | fr-LU | | | nl | | | nl-BE | | | pt | | | pt-PT | | ----- ## Datos estructurados