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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Presto
Score 2.6 out of 10
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Presto is an open source SQL query engine designed to run queries on data stored in Hadoop or in traditional databases.
Teradata supported development of Presto followed the acquisition of Hadapt and Revelytix.
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Pricing
Apache Hive
Presto
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Apache Hive
Presto
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Free/Freemium Version
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Premium Consulting/Integration Services
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Apache Hive
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Highlights
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Apache Hive and Presto are both analytics engines that businesses can use to generate insights and enable data analytics. Apache Hive is a data warehousing tool designed to easily output analytics results to Hadoop. In contrast, Presto is built to process SQL queries of any size at high speeds. Both tools are most popular with mid sized businesses and larger enterprises that perform a large volume of SQL queries.
Features
Apache Hive and Presto both enable organizations to perform queries on business data, but they also have some standout features that set them apart from each other.
Apache Hive is designed to facilitate analytics on large amounts of data, while also providing storage for the results in the form of tables. Businesses using Hadoop will appreciate that Apache Hive is built on top of the Hadoop File System, making it easy to integrate Apache Hive into their existing infrastructure. Businesses will get the most out of Apache Hive if they are performing ad-hoc queries on large datasets.
Presto is an open source sql query engine that can manage and run both simple, small queries, as well as large, complex queries. Businesses will appreciate that Presto can run queries at high speeds, making it a good choice for businesses that want to run a lot of queries without being delayed. It is worth noting, that for businesses using Hadoop that want the high query speed offered by Presto, it does include an integration with Apache Hive.
Limitations
Apache Hive and Presto are both popular choices for businesses seeking analytics engines, with some even using both, but they also have some limitations that are important to consider.
Apache Hive provides excellent support for large datasets and businesses that use Hadoop, but it can’t run SQL queries as fast as Presto. Businesses looking for the fastest option available may need to consider other options. Additionally, Apache Hive includes built in support for Hadoop, but businesses using other tools will not be able to take advantage of those benefits.
Presto provides fast support for SQL queries, but it doesn’t include built in support for the Hadoop File System, and requires other tools to function for that use case. Businesses looking for a quick solution that works with Hadoop out of the box may prefer Apache Hive. Additionally, businesses less concerned with scalability and maximum query speed may prefer the support for large datasets provided by Apache Hive.
Pricing
Apache Hive and Presto are both open source tools, so the source code for each one is available for free.
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.
Simple stories & templates work nicely - like for our Insider program. Stories that include a lot of images may be challenging to create & have look appealing.
Linking, embedding links and adding images is easy enough.
Once you have become familiar with the interface, Presto becomes very quick & easy to use (but, you have to practice & repeat to know what you are doing - it is not as intuitive as one would hope).
Organizing & design is fairly simple with click & drag parameters.
Presto was not designed for large fact fact joins. This is by design as presto does not leverage disk and used memory for processing which in turn makes it fast.. However, this is a tradeoff..in an ideal world, people would like to use one system for all their use cases, and presto should get exhaustive by solving this problem.
Resource allocation is not similar to YARN and presto has a priority queue based query resource allocation..so a query that takes long takes longer...this might be alleviated by giving some more control back to the user to define priority/override.
UDF Support is not available in presto. You will have to write your own functions..while this is good for performance, it comes at a huge overhead of building exclusively for presto and not being interoperable with other systems like Hive, SparkSQL etc.
Hive is a very good big data analysis and ad-hoc query platform, which supports scaling also. The BI processes can be easily integrated with Hadoop via the Hive. It can deal with a much larger data set that traditional RDBMS can not. It is a "must-have" component of the big data domain.
Apache Hive is a FOSS project and its open source. We need not definitely comment on anything about the support of open source and its developer community. But, it has got tremendous developer support, awesome documentation. I would justify the fact that much support can be gathered from the community backup.
We have used a simple but necessary function such as merging certain data tables, which although they may be from different areas, complement each other or are necessary, you can use metadata if what you need is to validate the origin of your information and what impact it has, is also feasible.
I think Presto is one of the best solutions out there today at the cutting edge for interactive query analysis. One of the challenges is presto is a niche tool for the interactive query use case and doesn't have the knobs and whistles as much as Spark. In the foreseeable future if they are able to make presto work without the need for Hive, solving all the gaps it could be game changing and can be a direct threat to spark
