Apache Hive Reviews

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Reviews (1-16 of 16)

Kartik Chavan | TrustRadius Reviewer
August 29, 2018

My Apache Hive Review

Score 8 out of 10
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Apache Hive is being used in our company mainly for big data analysis. It has greatly helps us with data processing & analysis. It is being used across the whole organization. The business problem addressed by it is that it has been helping our organization in storing large data sets and easily accessing them.
  • Querying in Apache Hive is very simple because it is very similar to SQL.
  • Hive produces good ad hoc queries required for data analysis.
  • Another advantage of Hive is that it is scalable.
  • Apache Hive isn't designed for and doesn't support online processing of data.
  • Sub queries not supported.
  • Updating the data can be a problematic task.
It is perfectly suited for analytics.
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Jordan Moore | TrustRadius Reviewer
February 17, 2018

One of the first SQL on Hadoop tools. Perhaps not the best.

Score 7 out of 10
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Hive allows us to run SQL queries against data sitting in Hadoop.
  • One of the standard SQL on Hadoop implementations. Comes installed in both HDP and CDH Hadoop distributions.
  • Hive Live Long and Process has made recent significant improvement on long-running queries.
  • Allows BI tools to run analysis over Hadoop data.
  • Allows various relational databases for its metastore. These include MySQL, Postgres, Derby, or Oracle.
  • Needs to keep up with execution engine improvements. Spark or Tez on Hive, then LLAP are good starts.
  • Overall speed of ad-hoc querying could be improved.
Hive is well-suited for providing an SQL engine on Hadoop, but there are alternative SQL on Hadoop projects that claim to have improvements over Hive.
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Tejaswar Rao | TrustRadius Reviewer
December 05, 2017

Apache Hive Faster and Can handle large sets of data

Score 9 out of 10
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We use hive for analyzing big sets of data and for developing rule-based applications. And also for visualization tools and where we query on large sets of data using hive for desired visualization. Hive is fast and also can be imported/exported using other hadoop components. We can use SQL to access data in hive and with no need to learn a new language.
  • Can query on large sets of data and fast when compared to RDBMS
  • Can use SQL for data access and no need to learn new language
  • Can write custom functions (UDF) with python and also Java
  • Security roles for different users should be implemented
  • All the functionalities of SQL should be available
  1. To query on large sets of data
  2. Faster access compared to traditional Databases
  3. OLAP projects
  4. Data Warehousing project
  5. To get insights from GigaByte's or TeraByte's of data
  6. Rule based projects and also to identify the patterns in data
  7. For applying transformations on large sets of data
  8. Faster response time than traditional databases
  9. Also able to get connected with hadoop components
  10. For complex analytical and different types of data formats
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Bharadwaj (Brad) Chivukula | TrustRadius Reviewer
October 25, 2017

Bringing Structure to your Unstructured Data

Score 9 out of 10
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1. In Retail, the business partners are more comfortable querying their own data instead of relying on Engineers. Hive solves one of those problems. The main purpose of using Hive is to building reports and do analysis of data that is stored in the Hadoop file system.
2. 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.

  • Hive syntax is almost like SQL, so for someone already familiar with SQL it takes almost no effort to pick up Hive.
  • To be able to run map reduce jobs using json parsing and generate dynamic partitions in parquet file format.
  • Simplifies your experience with Hadoop especially for non-technical/coding partners.
  • Hive doesn't support many features that traditional RDBMS SQL has; so it may not be an easier transformation as one would presume.
  • Being OpenSource, it has its share of problems and lack of support; need to explore community groups to get some clarifications if you are not using any of the big distribution providers like Cloudera or HW.
  • Hive is comparatively slower than its competitors. It's 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.

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. Makes it simpler for your BA's as they are familiar with SQL, and can adapt to Hive without too much of technical knowhow.

Read Bharadwaj (Brad) Chivukula's full review
Sameer Gupta | TrustRadius Reviewer
September 13, 2017

Apache Hive Review

Score 8 out of 10
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Hive is currently being used across the entire analytics organization at SurveyMonkey. The business problem that we solve through it is, accessing/storing large data sets(typically logs), in a scalable and accessible place.
  • SQL like query engine, allows easy ramp up from a standard RDBMS
  • Scalability is great
  • If properly configured the data retreival is fantastic
  • The way we currently have it implemented is quite slow, but I believe that's more of our implementation
  • Joins tend to be slow
I think Apache hive is great for a company just stepping into the big data realm. I think the fact that it's open source allows for a variety of tools to be integrated. The fact that it has HiveQL makes for a great transition from a standard RDMS to a big data tool. This can be very nice in terms of cost savings as the ramp up time for an analyst will be quite low.
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Praveen Murugesan | TrustRadius Reviewer
February 27, 2017

Hive Away, but not for everything!

Score 6 out of 10
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We use apache hive across the whole organization. We built our own in-house hadoop cluster for data warehousing purposes complementary to HP Vertica which we were using. Vertica is limited to scale, and to achieve true scalability and process trillions of records we had to invest in a new solution. Enter Apache Hive. We are very data driven as an organization and hence to satisfy to appetite of people and also stick to something familiar to query data (SQL) we decided to invest in Apache Hive as a starting point in our new data infrastructure.
  • Hive which leverages traditional MapReduce at the core, can be used to process a large amount of data without a problem. Any problem that can be solved with MapReduce can now be simply expressed in SQL.
  • Hive leverages the disk in the case of processing large data and is not limited by physical memory of any one machine (which is a limitation for systems like Presto). Hence it even allows reasonable fact-fact cross joins.
  • Hive is extensible with UDFs. For any common patterns you can quickly write your own function set and it can be leveraged by everyone.
  • Compute Speed - Hive will be my last option to query vs. something like Presto, which has a much smarter query engine. Hive is slow, and I'd use it only if we cannot use something like Presto/Impala.
  • SQL syntax of hive is unique and does not conform to ANSI SQL. This is quite painful for beginners.
  • The ability to upsert records would be nice to have. Hive is cumbersome for mutable data where partitions require them to be rewritten. No one has solved this really well. If this is solved - it could be leveraged by many systems.
Process large datasets (especially joins of two large datasets, cross joins etc). Hive is not well suited for generic queries on one table and it can still be very slow. There are better solutions for that (Presto, Impala).
Read Praveen Murugesan's full review
Venkata Mallepudi | TrustRadius Reviewer
September 13, 2016

As sweet as Honey - Apache Hive

Score 9 out of 10
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Apache Hive is used for data processing and analysis in the company that I am working for. Apache Hive is being used by the IT department and the results it produces are shared across the whole organization. Performing operations on terabytes of data has become easy without worrying much about the complexity involved. Similarity with SQL related tools has increased the difficulty in looking for employees with big-data skills.
  • Apache Hive works extremely well with large data sets. Analysis over a large data set (Example: 1PB of data) is made easy with hive.
  • User-defined functions gives flexibility to users to define operations that are used frequently as functions.
  • String functions that are available in hive has been extensively used for analysis.
  • Joins (especially left join and right join) are very complex, space consuming and time consuming. Improvement in this area would be of great help!
  • Having more descriptive errors help in resolving issues that arise when configuring and running Apache Hive.
Apache Hive is well suited in situations where doing aggregations would be very time consuming. Apache Hive returns results faster than many other applications.

Latency that exists when working with small data sets is a situation that needs to be looked at. Apache Hive is less appropriate in that scenario.
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Tom Thomas | TrustRadius Reviewer
May 25, 2016

Hive brings the power of SQL to Hadoop

Score 9 out of 10
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I have used Hive at an enterprise company where I interned. It was being used by the IT department to improve analysis of large datasets stored in the company's Hadoop HDFS. It was also being used because of its support for HiveQL which is a SQL like language enabling queries on large datasets. It also reduced the learning curve for handling big data because of HiveQL's similarity to SQL.
  • Supports SQL like queries
  • Various storage types including RCFile, HBase, ORC, etc.
  • Supports indexing for acceleration
  • HiveQL does not have all the features of SQL
  • No support for transactions
Hive is very well suited for large enterprise businesses that rely on Hadoop for efficient processing of big data in a distributed cluster. HiveQL also brings familiarity of SQL which speeds up the learning process for new users. However, Hive is not an ideal option for a business where data is frequently changing and dynamic.
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Anonymous | TrustRadius Reviewer
June 07, 2018

Hive is solid data analytical tool

Score 9 out of 10
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Hive is currently used in our Data Warehouse in our company. It helps us give more structure to our data and as Hive sits on top of Hadoop, the MR engine. It is a big plus when you want to run a complex query and get faster results. This helps us facilitate the Business Intelligence team to use Hive as a self-querying tool.
  • It's Fast!
  • You can store a different kind of data structures here other than the standard ones
  • Good scalability
  • Good redundancy too
  • It's not as ACID compliant as an RDBMS. It's a recently added feature and still needs work.
  • This is not the tool to go for online data processing.
  • It does not support sub-queries.
  • It can't process data in real time.
This is best suited for data analysts and scientists, it's not a programmers tool. You may still need an RDBMS to read data from as updates and deletes can get a bit more complicated, you can run batch jobs, this will have to be facilitated by additional tools.
Its good for fast query processing, for storing large amounts of data.
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Yinghua Hu | TrustRadius Reviewer
December 22, 2014

Hive, a very powerful open source data warehouse solution.

Score 10 out of 10
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Hive is used by data team to store the largest datasets of the company. Data is partitioned in Hive and can be queried by Impala.
  • Partition to increase query efficiency.
  • Serde to support different data storage format.
  • Integrate well with Impala and data can be queried by Impala.
  • Support of parquet compression format
  • Speed is slower compared to Impala since it uses map reduce
Hive is a data warehouse and it does not allow for updates and deletions. If data needs to be updated frequently, it might not be the best storage solution for that purpose.
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Anonymous | TrustRadius Reviewer
March 01, 2018

Hive - SQL-like query engine for big data platform

Score 9 out of 10
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Hive is not used across whole organization but used by certain teams which require querying data from our big data store infrastructure like HDFS. It provides an interface to interact with and directly query HDFS, similar to the way we do it with any relational databases. It is a powerful tool for querying big data.
  • Querying, joining and aggregating data
  • In built-in and user-defined functions
  • Speed
  • Support for other big data frameworks like Spark
  • Need better user interfaces for browsing datastores and querying
[Well suited for] Enterprises who want to create data warehouses on top of Hadoop ecosystem for reporting purpose or get summaries or aggregation from big data. In short, if you have implemented Hadoop then you need Hive.
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Anonymous | TrustRadius Reviewer
September 11, 2017

Apache Hive for ETL workloads

Score 5 out of 10
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Apache Hive is being using across our organisation for analytical workloads. We use Hive along with Hortonworks distribution and it's a great SQL on Hadoop tool.
  • Hive is good for ETL workloads on Hadoop.
  • HiveQL translates SQL like queries into map reduce jobs.It supports custom map reduce scripts to plugged in.
  • Hive has two kinds of tables- Hive managed tables and external tables.
  • Use external table when other applications like pig, sqoop or mapareduce also using the file in hdfs. Once we delete the external table from Hive, it just deletes the metadata from Hive and original file in hdfs stays.
  • Use Hive for analytical work loads. Write once and read many scenarios. Do not prefer updates and deletes.
  • Behind scenes Hive creates map reduce jobs. Hive performance is slow compared to Apache Spark.
  • Map reduce writes the intermediate outputs to dial whereas Spark operates in in-memory and uses DAG.
Use it for ETL workloads. I prefer repeat the same workload with Spark and decide the better performance
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Anonymous | TrustRadius Reviewer
April 26, 2017

Apache Hive - Querying Big Data Made Easy!

Score 8 out of 10
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We use Apache Hive for two main use cases, analyzing our ever growing data volume insights and reports, and as part of our ETL pipeline where we found writing in SQL like syntax to allow for more rapid development with low complexity to the overall system.

Apache Hive solves a few issues for us but the main one being the ability to analyze large volumes of data on S3 directly with overall strong performance. We have been able to analyze billions of records in a matter of minutes with relatively small EC2 cluster using Apache Hive. It also allows for our Data Analysts to simply write SQL and avoids the ramp up to use other tools such as Apache Pig.
  • Apache Hive allows use to write expressive solutions to complex problems thanks to its SQL-like syntax.
  • Relatively easy to set up and start using.
  • Very little ramp-up to start using the actual product, documentation is very thorough, there is an active community, and the code base is constantly being improved.
  • Debugging can be messy with ambiguous return codes and large jobs can fail without much explanation as to why.
  • Hive is only SQL-like, while more features are being added we have found that some things do not translate over (for example outer joins, inserts, columns can only be referenced once in a select, etc.).
  • For out ETL jobs it does not seem to be the optimal tool due to tunings and performance being difficult, Apache Pig may be better for heavy processing jobs.
Apache Hive shines for ad-hoc analysis and plugging into BI tools. Its SQL-like syntax allows for ease of use not for only for engineers but also for data analysts. Through our experience, there are probably more desirable tools to use if you are planning on integrating Hive into your processing pipeline.
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Anonymous | TrustRadius Reviewer
February 14, 2017

Easy access to data in Hadoop

Score 8 out of 10
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Apache Hive is primarily used by data analysts and data engineers at our company. We store most of our data in Hadoop and Apache Hive allows us to access the data faster than by writing MapReduce jobs.
  • Faster than writing MapReduce or scalding jobs to access data in Hadoop.
  • Syntax is essentially the same as that of SQL, making the barriers for entry to start using data low.
  • Apache Hive can be quite slow and is not suitable for interactive querying. Simple queries will take many minutes and more complex queries can take a very long time to finish running.
Apache Hive is suitable for allowing easy access to data stored in Hadoop via a familiar SQL syntax. It is more suitable for one-off data pulls and less suitable for interactive querying due to its speed. For a better interactive querying experience, a solution like Presto would be more suitable.
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Anonymous | TrustRadius Reviewer
September 16, 2016

Hive, last generation tooling but revolutionary for it's time.

Score 5 out of 10
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Hive was once a part of our platform but it never lived up to the promise of performant SQL on HDFS and thus was only truly useful for the users who didn't have the expertise or time to write MapReduce. With the advent of Spark, Hive's time is numbered and I would not invest in learning it specifically but instead use SparkSQL which has some of the better parts of Hive under the covers along with Spark's better execution engine.
  • Connect BI tools to non relational data stores
  • Simplify writing legacy MapReduce
  • Speed needs to be a lot better
  • Concurrency is not up to snuff
Hive is mostly useful in HDFS environments where legacy BI tools need to access the data. This is ok if there is a low concurrency of users but will fall over with any significant multi-user environment.
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Anonymous | TrustRadius Reviewer
April 20, 2016

HiveQL, Almost SQL, but not quite.

Score 8 out of 10
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Hive is being used to put an SQL interface to our Hadoop cluster. This works well because most of our organization is very SQL friendly, so when introducing a new technology, such as Hadoop, the technical users are easily able to adapt to the new technology with no problem.
  • Run SQL queries to an Hadoop cluster.
  • Many different consoles can use it.
  • Users don't have to write map reduce.
  • Hive needs more SQL support.
  • Enabling more date functions.
  • Enabling more SQL table functions, such as inserting into a temp table.
Apache Hive is well suited for pulling data for reporting environments or ad-hoc querying analysis to an Hadoop cluster. I believe Apache Hive is not well suited for running large big data jobs when needing fast performance. It can be best utilized on scheduled jobs where fast performance is not required. However, this can greatly depend on how the Hadoop cluster is set up.
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About Apache Hive

Apache Hive is database/data warehouse software that supports data querying and analysis of large datasets stored in the Hadoop distributed file system (HDFS) and other compatible systems, and is distributed under an open source license.

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