Hive Reviews: 4.2/5 — Solid Choice

Leverage sql skills to perform operations on data stored in hadoop.

Works on map reduce algorithm, so the retrieval of data is a little slow.

Allowed business users to query data using sql skills.

The best thing about HIVE is that anyone that is familiar with SQL can take advantage of HIVE's ability to run map reduce jobs. Newer version of HIVE is getting better at supporting windowing functions and fleshing out any inconsistencies. So far the documentation is good enough for getting me through my tasks and there is still on-going support for this product, which is a pretty good sign to me.

Older versions of HIVE sucks. There are lots of limitations that will force you to write HiveQL queries that are not straight forward and, even potentially, inefficient. For example, no support for window functions and no equality comparisons on joins can make your life very difficult so you will need to fall back to using some whacky full joins or self joins to accomplish the same task.

We are using HIVE as a data warehouse. One of the benefits of HIVE is that it can break your SQL queries into a series of map reduce jobs, so its supposed to speed up your queries if given enough compute nodes.

Hive is the best out there for answering ad-hoc queries in parallel paradigm. It works very well with Hadoop Echo system (mainly integrates perfectly with HDFS). - Easy to use as it implements most of SQL functions.

- Needs more optimization for complex queries (like caching, auto-partitioning,etc ...) to speed up the latency of the queries. - Tuning the hive parameters is really challenging for the users. The default settings don't work with the large queries. - Hive is perfect if 90-95% of the queries are read-only. It is not suitable for applications with heavily updates

Get quick insights from big data in case of the customers' data don't fit on one machine. It helps a lot for data preparation (i.e. creating temporary tables), that can be consumed by other machine learning solutions like Spark to build machine learning models that add more business values.

For all its processing power, Pig requires programmers to learn something on top of SQL. It requires learning and mastering something new. Hive statements are remarkably similar to SQL and despite the limitations of Hive Query Language (HQL) in terms of the commands that it understands, it is still very useful. Hive provides an excellent open source implementation of MapReduce. It works well when it comes to processing data stored in a distributed manner, unlike SQL which requires strict adherence to schemas while storing data.

Despite the working differences, once you enter the Hive world from SQL, similarity in language ensures smooth transition but it is important to note the differences in constructs and syntax, else you’re in for frustrating times.

data extracting, processing and analysis. It's fast.

Hive provides an ease to the user who wants to store bulk data, in a tabular manner. It works on the same queries like SQL, making it easy for using the traditional database system. Because of this reason, people need not have to study some new language and can still adapt to the Big Data Culture. Also it has features like partition, and bucketing, helping in segregation of data. Data can directly be loaded into hive, by HDFS, using the CSV files of the same format, or from Hbase by making a pointer to the Hbase table, providing a link within Hadoop.

For small amount of data also, it runs map reduce job, which consumes some time, and thus is not efficient for the same. We do not have a concept of primary key in Hive, so we can have redundant entries. Also till the older version, update and delete were not possible, and now also in the new version, if we want to use the update and delete commands, the performance of the tool gets degraded.

We are using Hive for storing logs, of data, being generated, in our business. Further we will be using these logs for reconciliation purpose, helping in keeping a track of data.

Stable product; Easy to use; Multiple computation engines - Tez, MR; Almost all SQL capabilities;

Delete support is still not there even though they are nearly there.

Primary Querying engine for Data Analytics

Provides quick results based on a hadoop database, easy to use interface with simple set up steps

Some quirks with HiveQL may require referencing the documentation, but there is a lot of similarity with other SQL based languages.

Data analytics, making vast amounts of data available for general BI uses

The Hive is intended to simplify your experience with Hadoop and allows developers and business analyst apply their SQL knowledge to query data, build reports, build etl etc.

As the open source software it has common issues with support. Also Hive doesn't support many features that traditional SQL has.

The main purpouse of using Hive is to building reports and do analysis of data that is stored in the Hadoop file system. As for now it is the only one framework that can be used by all most popular BI tools to read the data from the HDFS.

Ease of use as well as ability to scale. It has proven its reliability. They have continued to add more features and increased its speed at the same time.

Speed is still slower compared to newer distributed warehouses. Also, it still uses mapreduce behind the scene which is very slow in the present days.

Storing large amount of data that could not fit in to any relational database system. Being able to derive valuable insight into our data by running mapreduce jobs on data stored in Hive.

The best part about hive is that it easy to master because of its SQL like interfacing. Also very handy tool for ETL and DW functions.

Not too good with setting up optimization parameters. Need to remember a lot of console settings. Index doesnt turn out to be very useful. CRUD functions have many pre-requisites like the table must be bucketed, etc.

we are trying to create a standard ETL pipelining tool that would support standard BI/Reporting utilities.

The best part is being able to use a familiar syntax.

Doesn't support all MYSQL use-cases (understandably).

Ad-hoc queries on ETL'd production data.

To be able to run map reduce jobs using json parsing and generate dynamic partitions in parquet file format.

It is slow compared to Spark/Impala for most operations. Also, it throws Out of Memory if multiple partitions are updated containing many parquet files.

Events are gathered in HDFS by flume and needs to be processed into parquet files for fast querying. The input data contains variable attributes in the json payload as each customer could define custom attributes. It is part of the ETL pipeline, where hive jobs read json data and generates parquet files that would be queried using impala/spark. Using views, each customer queries only the relevant data.

performing SQL-like queries, Partitioning Tables, De-normalizing data, Compress map/reduce output are best benefits

For some cases you cannot do complicated operations using Hive e.g. when output of one job acts as input to the other job (SequenceFileFormat file) or writing query on an image file, Hive is not useful.

Hive helps in resolving big data problems

Hadoop does not have native query language, but Hive is a great addition to use on top of hadoop. I could point to a data stored in hadoop to a specific table and could use normal queries like I usually do in SQL. We can join and do aggregations etc. Makes life pretty simple.

It can be very slow as it runs on map reduce jobs underneath. Data cannot be updated but we will have to do a rewrite.

For people who are used to write SQL queries would have a very good time using Hive on top of hadoop for files stored in HDFS.

-> Easy to configure/create a table for Big data or Streaming data -> Fast and easy to Query. Business/non-technical folks can use HUE for more interactive Querying on Hive tables -> HUE can have saved results and old queries along with exporting results in Excell.CSV

-> Joining and parsing multiple tables with huge sizes still remain a challenge. -> Some of SQL operations doesn't work in hive like non equality joins,

Dumping Site activity Big Data streaming data as well as data logs in Hive

Open source framework allows to read and write and manage tha data like sql , HQl which makes it easy to use.

The latency in Apache hive is very high.

It provides SQL like query language called HQl with schema on read and transparently convert queries to map reduce.

I like the most in Apache Hive supports partitioning and bucketing for fast data retrieval. We can create custom UDF as per the requirements to perform data cleansing and filtering. It supports HQL similar to SQL which gives easy for the people who comes from SQL background.

Doesn't support OLTP and also doesn't support delete or update actions.

We have created a semantic layer in Hive that helps us to process the terabytes of data and generate the reports faster. it also helped us fault tolerance and high availability of the data

Flexible and easy to understand loved it

Not compatible with multiple platforms hence mostly plotform depend

Works best with ETL related works or tasks

It is easy to run query in hive as hive uses hql which is very similar to sql. Hive has hivemetastore service to save the metadata and hiveserver2 to serve the client requests so the segregation here helps in proper resource distribution. Hive is also fault tolerant which makes it ideal to run ETL long running batches

Hive has a problem of cold start and since it used mapreduce algorithm at the backend, it is way slower than spark which made us move to spark from hive as the job completion time after switching to spark got reduced by 70-80%

Informatica data ingestion Abinitio data ingestion and modifications Data formatting (as it provides option such as csv,parauet etc) Data transformation using hive query Data pipelines

The friendliness of the data warehouse tool for the database developers

Not inclusion of acid properties, it doesn't have the acid properties as in the databases

I usually use hive for my big data [data migration problems], the speed at which the query operates, and the option to choose various engines

If you are data analyst and expert in SQL then use Hive. Hive is very easy to work with especially if you are a SQL person. I use both hive and pig at work. I use hive mainly for ad hoc quires and reports. For BI reports Hive is the best since you can reuse all the SQL that you have done for traditional data warehouses. Also with Hive Server2 you get a real JDBC support so you can plug your BI tools to it. Many more SQL features like cubes, rollups, windowing, lag, lead, etc are being added to Hive through Hortonworks Stinger initiative. Hive also produces very compact code, which is always good for reading and debugging.

I would suggest to use hive for large projects, where you want to implement SQL-like data access, schemas, metadata, partitions, server-based deployment, jdbc, etc. Pig is a good language and can be very handy for immediate tasks or small projects. i would recommend PIG for small projects .

Hive Hadoop provides the users with strong and powerful statistics functions. Hive Hadoop is like SQL, so for any SQL developer the learning curve for Hive will almost be negligible. Hive Hadoop can be integrated with HBase for querying the data in HBase whereas this is not possible with Pig. In case of Pig, a function named HbaseStorage () will be used for loading the data from HBase. Hive Hadoop has gained popularity as it is supported by Hue. Hive Hadoop has various user groups such as CNET, Facebook, and Digg and so on.

If you know how to write sql statements you can write hiveql and it doesn't require you to learn anything new,its pretty straightforward

Performance tuning is difficult and becomes hard for complex queries, it still has a few bugs like all the data going to single reducer, which might lead to slow down the query results.

For developing reports for business analysts, lot of them know sql statements so its easy to write and pull information for analysis

- Easy to learn - Can query complex data including nested structures. - Flexible (wrt data schema) - With ORC SerDe, I/O can be reduced drastically - by reading only what is required (columnar formats).

- Needs schema to be defined in prior. - Not ANSI SQL compliant. - Not suitable for fast interactive queries, even on moderate size datasets. - Works only with Hadoop (not an independent query-processing tool) - Not enterprise grade w.r.t quality of documentation, error messages, support

Exploring ways to store and process semantic datasets

Hive can tell us the detailed progress of a query, and can incorporate UDFs in different languages

The query speed is way to slow, and it does not support positional arguments in GROUP BY and ORDER BY

We use Hive to run our nightly workflows on HDFS in batch for data aggregation and analysis.

Hive syntax is almost exactly like sql, so for someone already familiar with sql it takes almost no effort to pick up hive. It can perform a wide variety of analyses over very large sets of data and requires very little tuning if you are willing to wait a while for the results.

Hive can be a bit slow in comparison to other languages like Pig. It also does not have as rich of a scripting language. This is what makes it the second choice language for most data analysis jobs at LinkedIn.

We are trying to mine data from massive data sets for a wide variety of purposes (debugging production issues, creating business metrics, models, and forecasts among other things). We have been able to do this very easily using our data warehouse and a combo of hive and Pig.

Hive its a data warehousing infrastructure built on top of Hadoop to provide data grouping, querying, and analysis.Apache Hive soporta el análisis de grandes conjuntos de datos almacenados bajo HDFS de Hadoop y en sistemas compatibles como el sistema de archivos Amazon.It offers a SQL-based query language called HiveQL5 with schemas to transparently read and convert queries in MapReduce, Apache Tez6, and Spark tasks. All three execution engines can run under YARN. To speed up queries, Hive provides indexes, which include bitmap indexes.

Offers many tools, has great growth potential

Possibility of storing metadata in an organized and easily accessible way.

Hive syntax is almost like sql, so for someone already familiar with sql it takes almost no effort to pick up hive. But there are other tools that can do the same thing faster these days. Hive initially was really good to have; but more and more projects are now available to do SQL like operations on Big Data (like Drill).

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. It also does not have as rich of a scripting language.

In Retail, the business partners are more comfortable querying their own data instead of relying on Engineers. Hive solves one of that problems. The main purpouse of using Hive is to building reports and do analysis of data that is stored in the Hadoop file system.

nothing in particular. helps us with big data and allows all users to have unrestricted bandwidth, but we already ran into issues with that, so now one of the servers has limitations.

. at my company it was fairly troublesome getting access since it's underlying warehouseing is in hadoop, then have to connect through hive

data insights with big browser data through mapreduce

Easy SQL like syntax for very short and simple queries

No alias for relation. No flow controls as well.

I build machine learning model for online advertising system. Hive to me is more like a ad-hoc query engine rather than a platform where I can develop complex algorithm on