Apache Hive vs. Apache Spark

Apache Hive

95 Reviews and Ratings

Apache Spark

161 Reviews and Ratings

Overview
Product	Rating	Most Used By	Product Summary	Starting Price
Apache Hive	Score8 out of 10	N/A	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.	N/A
Apache Spark	Score9.2 out of 10	N/A	Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.	N/A

Pricing

Apache Hive

Apache Spark

No answers on this topic

No answers on this topic

Pricing Offerings
Apache Hive	Apache Spark
Free Trial
No	No
Free/Freemium Version
No	No
Premium Consulting/Integration Services
No	No

No setup fee

No setup fee

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Community Pulse
Key User Insights
	Apache Hive	Apache Spark
Considered Both Products	Apache Verified User Chose Apache Hive To query a huge, distributed dataset, Apache Hive was built by Facebook. Unlike Apache Hive, Apache Spark is an in-memory computation engine, which is why it is significantly quicker than Apache Hive at querying large amounts of data. In contrast to Apache HBase, Apache Hive is … Incentivized Helpful? Verified User Chose Apache Hive Apache Spark is similar in the sense that it too can be used to query and process large amounts of data through its Dataframe interface. Hive is better for short-term querying while Spark is better for persistent and long-term analysis. Another product is Impala. For our … Incentivized Helpful? akshay kashyap CONSULTANT in Information Technology at Deloitte Digital (Consumer Goods, 5001-10,000 employees) Chose Apache Hive Apache Hive is a query language developed by Facebook to query over a large distributed dataset. Apache is a query engine that runs on top of HDFS, so it utilizes the resources of HDFS Hadoop setup, while Apache Spark is an in memory compute engine, and that's why [it is] much … Incentivized Helpful? Verified User Chose Apache Hive Hive and Spark have the same parent company hence they share a lot of common features. Hive follows SQL syntax while Spark has support for RDD, DataFrame API. DataFrame API supports both SQL syntax and has custom functions to perform the same functionality. Spark is faster and … Incentivized Helpful? Ananth Gouri Assistant Professor in Engineering at The National Institute of Engineering, Mysuru (Education Management, 501-1000 employees) Chose Apache Hive One of the major advantages of using Presto or the main reason why people use Presto (Teradata) is due to that fact it can support multiple data sources - which is lacking as in the case of Apache Hive. But still, most people who come from a Structured data-based background … Incentivized Helpful? Nicolas Hubert Machine Learning Engineer in Information Technology at Credit Suisse (Banking, 10,001+ employees) Chose Apache Hive Easy to understand, well supported by the community, good documentation. However, it is possible that SAP Business Warehouse could be a good fit, too, even maybe better. I did not have the chance to try it though. We selected Apache Hive because it was far less expensive and … Incentivized Helpful? Bharadwaj (Brad) Chivukula Sr.Technical Manager/Delivery Manager in Information Technology at Nisum Technologies, Inc. (Retail, 10,001+ employees) Chose Apache Hive For storing bulk amount of data in a tabular manner, and where there's no need need of primary key, or just in case, if redundant data is received, it will not cause a problem. For small amounts of data, it does run MR, so beware. If your intention is to use it as a … Incentivized Helpful? Verified User Chose Apache Hive Hive is SQL compliant which makes it easy for the data folks compared to Pig Incentivized Helpful? Verified User Chose Apache Hive Apache Pig is probably the most direct technology to compare to Hive and has several different use cases to Hive. If you want to simplify processing tasks that run using MapReduce then Apache Pig may be a better tool for the job. However if you are going to be running many … Incentivized Helpful? Jordan Moore Staff Consultant in Information Technology at Avalon Consulting, LLC (Information Technology and Services, 51-200 employees) Chose Apache Hive Hive was one of the first SQL on Hadoop technologies, and it comes bundled with the main Hadoop distributions of HDP and CDH. Since its release, it has gained good improvements, but selecting the right SQL on Hadoop technology requires a good understanding of the strengths and … Incentivized Helpful? Verified User Chose Apache Hive All are improvements over the Hive tooling and are very much inspired by Hive. Hive was selected before they were on the market. Incentivized Helpful?	Apache Riyaz Khan Staff Engineer in Information Technology at Nagarro (Information Technology & Services, 10,001+ employees) Chose Apache Spark Apache Spark is a fast-processing in-memory computing framework. It is 10 times faster than Apache Hadoop. Earlier we were using Apache Hadoop for processing data on the disk but now we are shifted to Apache Spark because of its in-memory computation capability. Also in SAP … Incentivized Helpful? Verified User Chose Apache Spark Apache Spark has much more better performance and features if we compare with Hive or map/reduce kind of solutions. Spark has many other features for machine learning, streaming. Incentivized Helpful? Nitin Pasumarthy Software Engineer in Engineering at LinkedIn (Internet, 5001-10,000 employees) Chose Apache Spark All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional … Incentivized Helpful? Kartik Chavan Data Analyst in Information Technology at The University of Texas at Arlington (Electrical/Electronic Manufacturing, 1001-5000 employees) Chose Apache Spark Even with Python, MapReduce is lengthy coding. Combination of Python with Apache Spark will not only shorten the code, but it will effectively increase the speed of algorithms. Occasionally, I use MapReduce, but Apache Spark will replace MapReduce very soon. It has many … Incentivized Helpful? Verified User Chose Apache Spark Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and … Incentivized Helpful? Kamesh Emani Software Developer Intern in Information Technology at Louisiana Tech University (Higher Education, 1001-5000 employees) Chose Apache Spark Apache Pig and Apache Hive provide most of the things spark provide but apache spark has more features like actions and transformations which are easy to code. Spark uses optimization technique as we can select driver program and manipulate DAG (Directed Acyclic Graph) Python … Incentivized Helpful? Jordan Moore Staff Consultant in Information Technology at Avalon Consulting, LLC (Information Technology and Services, 51-200 employees) Chose Apache Spark Spark has primarily replaced my use of writing pure Hadoop MapReduce or Apache Pig jobs for processing data. I like the fact that I can alternate between the main programming languages that I know - Java and Python - and use those to learn the Scala API. Spark also can be … Incentivized Helpful?
	Would buy again
	No answers on this topic	No answers on this topic
	Delivers good value for the price
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	Happy with the feature set
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	Lived up to sales and marketing promises
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	Implementation went as expected
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User Ratings
	Apache Hive	Apache Spark
Likelihood to Recommend	8.0 (35 ratings)	9.0 (24 ratings)
Likelihood to Renew	10.0 (1 ratings)	10.0 (1 ratings)
Usability	8.5 (7 ratings)	8.0 (4 ratings)
Support Rating	7.0 (6 ratings)	8.7 (4 ratings)