Hive Reviews & Product Details

File formats for optimizations, external

cannot analyse unstructured data, not much faster when it comes to complex operations on very huge data

adhoc analytics and batch analytics on big data

The data distribution and data processing in hive is very good in hive. DDL and DML functions in hive are better than conventional sql databases. Hive is better known for fault resistance.

The data output on hive is very slow. The data processing is very slow and the output is delayed due to this slow processing. Also the syntax is bit complex than conventional sql language.

Fault tolerance is very good feature which helps in data storing. Incase one database is lost, there isna backup created and this database can retrieved easily and intact.

the ease of use and its interface is best

running of queries in slow mode of map-reduced

storing the large data and retirving it through queries

Caso contrário, se você escrevê-lo como um SQL normal, pode levar horas para processar Mas é um pouco diferente do padrão SQL Pessoalmente falando, eu uso hive principalmente para ad hoc quires e relatórios é um software de armazenamento de dados que facilita a consulta e gerenciamento de grandes conjuntos de dados residindo em armazenamento distribuído

O ajuste de desempenho é difícil e torna-se difícil para consultas complexas, ele ainda tem alguns bugs, como todos os dados que vão para um único redutor, o que pode levar a retardar os resultados da consulta. -> Algumas das operações SQL não funcionam na colméia, como as associações de não igualdade, os dados não podem ser atualizados, mas teremos que reescrever

Estamos desenvolvendo o Hive Para pessoas que estão acostumadas a escrever consultas SQL, seria muito bom usar o Hive em cima do hadoop para arquivos armazenados no HDFS. Atividade do Dumping Site Dados de streaming de Big Data, bem como logs de dados no Hive Estamos desenvolvendo o Hive

- Easy to use interface - multiple clients (CLIs) - easy to debug issues with the help of fully descriptive logs - constantly the product is being improved to meet all the DB developer requirements - can be accessed from multiple applications - access through knox for additional security - no indexing - multiple file formats - the tez architecture

- authentication gaps - issues when routing through zookeeper - not as matured tool as the regular database tools

- BI team is helping all the enterprise users to ingest and access data from hadoop - most of the users are well versed with standard sql tools - to make hadoop enterprise wide solution we are training all users with hive

Ease to get started. Leverages sql knowledge. Has reasonable documentation. Fast to write queries.

Documentation sparse in some areas such as datetime formats. Queries run slowly and often fail to complete.

Preprocessing for machine learning pipeline. Running ad hoc queries on customer databases to generate high level summaries.

Its very user friendly. Easy to install and use. I like the interface very much .

Nothing I can think of. I had a great experience working on HIVE and was satisfied with all the features as it met all my requirements.

I was working on a school project as a part of Big Data course and executing queries with HIVE made the whole project lot more simpler.

Hive has a simple and intuitive interface and gets the job done.

So far Hive has met and exceeded all my expectations.

Working on a Hadoop system to determine recruiters that are spamming members too much.

The syntax of hive! Its almost SQL so its easy to use. External tables, partitions, buckets, UDFs all the features I like to use with hive. ORC data format occupying lesser space and retrieving the data much faster. Learning curve looks easier as it is similar to SQL but hold on! you must learn all the features of hive before writing a big hql to join multiple hundreds GBs tables and fetch results. Otherwise if you write it like a regular SQL it may take hours to process. So hive is always at its best when you set the optimization parameters before you run your scripts. Also its complex datatypes make hive more useful than other RDBMS.

Hive is comparatively slower than its competitors. Its easy to use but that comes with the cost of processing, If you are using it just for batch processing then hive is well and fine.

Generating datasets from huge files for reporting purposes.

It's performance using distributed computation

Limited options for query performance optimization

It is very good for OLAP related tasks