Apache Hive vs. Snowflake

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Hive
Score 8.0 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
Snowflake
Score 8.9 out of 10
N/A
The Snowflake Cloud Data Platform is the eponymous data warehouse with, from the company in San Mateo, a cloud and SQL based DW that aims to allow users to unify, integrate, analyze, and share previously siloed data in secure, governed, and compliant ways. With it, users can securely access the Data Cloud to share live data with customers and business partners, and connect with other organizations doing business as data consumers, data providers, and data service providers.N/A
Pricing
Apache HiveSnowflake
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache HiveSnowflake
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache HiveSnowflake
Considered Both Products
Apache Hive
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 …
Chose Apache Hive
Apache hive gave more flexible than MS SQL server. ElasticSearch was little complex. GoogleBigQuery cost more.
Chose Apache Hive
Community support and ease of use -not deployment.

It enables querying and analyzing large amounts of data stored in HDFS, on the petabyte scale. It has a query language called HQL that transforms SQL queries into MapReduce jobs that run on Hadoop, and it is wonderful for the …
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 …
Chose Apache Hive
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, …
Chose Apache Hive
Apache Hadoop is built on top of the Hadoop File system so it gives its best when integrated with Hadoop. Data analysis and query optimization become very easy when used with Hadoop to perform Extract transform load operations. As Hadoop is a big data system and handles large …
Chose Apache Hive
We have used the system to migrate data either for new versions or because we will use another operating program, the software helps us to synchronize programs between different operating systems, a history of information can be kept constant, it can be sent to third parties …
Chose Apache Hive
Queries are easy to write and interface is similar to SQL so learning overhead is reduced. Multi user and data type support is provided. Can be easily scaled for very large amount of analytics. It is very flexible in terms of using file formats.
Chose Apache Hive
Snowflake, Splunk Cloud, Talend Open Studio, Azure Data Factory and Apache Spark
Chose Apache Hive
Due to effective queries resolved time and the performance and user-friendly framework compared to other products.
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 …
Chose Apache Hive
Besides Hive, I have used Google BigQuery, which is costly but have very high computation speed.
Amazon Redshift is the another product, I used in my recent organisation.
Both Redshift and BigQuery are managed solution whereas Hive needs to be managed
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 …
Chose Apache Hive
Apache Hive decouples the query layer from the storage layer, it is more flexible and expandable.
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 …
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 …
Chose Apache Hive
I considered Hive because it is the best suited option when it comes to larger data access. Besides, learning HiveQL is comparatively easy.
Chose Apache Hive
I have used Storm for real-time processing, but that only addresses a few data points. But for a larger access to data, Hive is well suited.
Chose Apache Hive
[We selected Apache Hive because] It's from apache and opensource. So it's free.
Chose Apache Hive
  • Faster response time and also can handle complex analytical queries
  • Can able to write custom function using python and hive
  • Able to connect using hadoop components and also using R
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 …

Chose Apache Hive
I wasn't part of the evaluation process for Apache Hive. This was already implemented when I joined the company. I have worked with other big data plaftforms and I personally thinks most of them are quite comporable to one another. It really depends on what the company is going …
Chose Apache Hive
Hive is SQL compliant which makes it easy for the data folks compared to Pig
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 …
Snowflake
Chose Snowflake
Snowflake provides various features, such as integration with Python using Snowpark. The reporting feature that caters to your small reporting needs is Snowsight. The Snowflake data marketplace is where you can get multiple data for free and even some of the data which you can …
Chose Snowflake
These are comparable products that can make sense depending on the specific needs of your organization. All are certainly serviceable and have varying pros and cons. Snowflake seems to provide the greatest degree of flexibility and easy scalability as new data gets brought into …
Chose Snowflake
Snowflake stacks up well against many of the other players in the space. We used Teradata before and it seems to perform better.
Chose Snowflake
We needed scalability and a new way of organizing our data; Snowflake allowed us to have a clearer view of our data warehouses and schemas. Snowflake is also way superior in terms of speed and quick insights from the raw data you query, which is very valuable to us.
Chose Snowflake
Snowflake has an attractive pricing model with auto-suspend and auto-resume and pay per use. AWS Redshift requires higher administrative efforts to maintain and scale the platform whereas with Snowflake those admin tasks are not needed or automatically taken care of.
Chose Snowflake
Cloud -Native architecture, Separation of Storage and Compute provides flexibility, Scalable Compute, Cost and Data Security
Chose Snowflake
We had a MS SQL server with over 2 TB of ram & 51 processors that we were using, that could no longer handle our workload. Snowflake can handle 3 times that workload with ease and efficiency.
Chose Snowflake
Snowflake is much faster and easier to write queries and pull data. But the visualization part of Snowflake is not as good as them. Also, Snowflake only supports SQL queries but not python or other languages. So basically Snowflake is the expert in its field but not suitable …
Chose Snowflake
We particularly liked Snowflake's security model as well as its unique storage (whereby everything is essentially a pointer to immutable micro-partitions, which is the key behind its zero-copy cloning, its secure sharing, its time travel, etc.). and also how it separates …
Chose Snowflake
While Snowflake is more open to cloud eco system, SAP integrated well with SAP eco system products like SAP ECC or SAP S/4. So for people who have invested heavily in SAP eco system including SAP ECC or S/4, it makes sense to go with SAP DWC which is also evolving very rapidly. …
Chose Snowflake
In my opinion, the other tools have similar and some different features; however, when I ran proof of technologies between Synapse and Snowflake. Snowflake did things better or just had functionality that the other tools did not. One that stuck out at the time was scale up …
Chose Snowflake
Each of the other solutions were cloud vendor specific, Snowflake can ride on either Amazon Web Services, Microsoft Azure, or Google Cloud. The fact that they are ANSI-sql compliant and have an effective means of offloading data makes them portable and easy to sell to teams …
Chose Snowflake
Azure and Snowflake compared very similarly, but Snowflake provided more options to integrate and connect with tools/companies that were not partners. It seemed to be a more flexible environment. The barrier for entry on Oracle and Google we just too complicated. In particular, …
Chose Snowflake
I have had the experience of using one more database management system at my previous workplace. What Snowflake provides is better user-friendly consoles, suggestions while writing a query, ease of access to connect to various BI platforms to analyze, [and a] more robust system …
Chose Snowflake
Snowflake has won the match because it is giving an excellent performance with its efficient features and reliable results. This is a totally secure program for our precious and important data.
Chose Snowflake
Our initial data warehousing solution was Treasure Data. We had issues with the costly pricing model, which would be exhorbitant if we want to hold our data in memory and query using Presto. As a result, some heavy lifting was done in Hive (managed by Treasure Data); …
Chose Snowflake
Snowflake beats these other products in every category it was rated against
Chose Snowflake
In my experience running the data management practice at InterWorks, we believe that cloud data warehouse products will eventually serve the majority of data warehousing use cases and power data analytics at most companies. Of this cohort, we believe that Snowflake is the best …
Chose Snowflake
Redshift compute and storage can be scaled up/down together (though they added some features recently, they don't quite add up). I haven't tried Avalanche or Firebolt but would love to in the near future, due to their pedigree or revolutionary billing methods.
Chose Snowflake
- Cost was the main aspect on the decision.
- Performance was in par or better compared to other tools in the market.
- Snowflake in my opinion stacks better than other tools I have used in the past.
Chose Snowflake
Accommodates future data types such as JSON and XML. Scalability is another advantage. Pay per use is beneficial for organizations like yours. Direct connectors with AWS help us to go with it. No limit on user creation and clone data not eating up extra disk space are a few …
Chose Snowflake
Since we switch from amazon redshift to Snowflake, we found Snowflake is much better than redshift in many ways, including the data integrate and data pull. However, comparing directly pull data from amazon s3, Snowflake is quite slow in terms of data pull speed and the more …
Chose Snowflake
Compared to Amazon Redshift, Snowflake is slightly easier and faster to achieve ROI but based on the user's perspective, the two tools have very little difference since both are leveraging SQL to pull data from AWS S3. Snowflake is also working with Microsoft Azure but it is …
Chose Snowflake
Our issue with Redshift was that it was very expensive. On top of that, queries were still slow and if we used more of Redshift's memory, then it would have cost even more. Snowflake is not cheap, but less costly for us. Plus, the performance was much better. Also, we got to …
Chose Snowflake
Snowflake is a lot faster, feels more lightweight, and is overall easier for our BI team to manage and grant warehouse accesses.
Best Alternatives
Apache HiveSnowflake
Small Businesses
Google BigQuery
Google BigQuery
Score 8.4 out of 10
Google BigQuery
Google BigQuery
Score 8.4 out of 10
Medium-sized Companies
Cloudera Enterprise Data Hub
Cloudera Enterprise Data Hub
Score 9.0 out of 10
Google BigQuery
Google BigQuery
Score 8.4 out of 10
Enterprises
Oracle Exadata
Oracle Exadata
Score 10.0 out of 10
Google BigQuery
Google BigQuery
Score 8.4 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache HiveSnowflake
Likelihood to Recommend
8.0
(0 ratings)
9.2
(0 ratings)
Likelihood to Renew
10.0
(0 ratings)
10.0
(0 ratings)
Usability
8.5
(0 ratings)
9.3
(0 ratings)
Support Rating
7.0
(0 ratings)
9.9
(0 ratings)
User Testimonials
Apache HiveSnowflake
Likelihood to Recommend
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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If you need a quick query, snowflake is the way to go. It's super simple and scalable; we were struggling before with Azure, and with Snowflake, everything runs smoothly, and we have more control over our schemas and warehouses. Snowflake, in my opinion, is the next step when you want to scale your business and manage data. If your company is still small, there may be cheaper options.
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Pros
  • 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.
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  • Snowflake scales appropriately allowing you to manage expense for peak and off peak times for pulling and data retrieval and data centric processing jobs
  • Snowflake offers a marketplace solution that allows you to sell and subscribe to different data sources
  • Snowflake manages concurrency better in our trials than other premium competitors
  • Snowflake has little to no setup and ramp up time
  • Snowflake offers online training for various employee types
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Cons
  • 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.
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  • Add constraints for views and not just for tables
  • Do not force customers to renew for same or higher amount to avoid loosing unused credits. Already paid credits should not expire (at least within a reasonable time frame), independent of renewal deal size.
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Likelihood to Renew
Since I do not know the second data warehouse solution that integrate with HDFS as well as Hive.
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SnowFlake is very cost effective and we also like the fact we can stop, start and spin up additional processing engines as we need to. We also like the fact that it's easy to connect our SQL IDEs to Snowflake and write our queries in the environment that we are used to
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Usability
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.
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The interface is similar to other SQL query systems I've used and is fairly easy to use. My only complaint is the syntax issues. Another thing is that the error messages are not always the easiest thing to understand, especially when you incorporate temp tables. Some of that is to be expected with any new database.
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Support Rating
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.
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We have had terrific experiences with Snowflake support. They have drilled into queries and given us tremendous detail and helpful answers. In one case they even figured out how a particular product was interacting with Snowflake, via its queries, and gave us detail to go back to that product's vendor because the Snowflake support team identified a fault in its operation. We got it solved without lots of back-and-forth or finger-pointing because the Snowflake team gave such detailed information.
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Alternatives Considered
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.
Read full review
Snowflake provides various features, such as integration with Python using Snowpark. The reporting feature that caters to your small reporting needs is Snowsight. The Snowflake data marketplace is where you can get multiple data for free and even some of the data which you can buy according to your needs. And the integration options with various tools like Sigma are add-ons.
Read full review
Return on Investment
  • Good ROI for being able to access data easily across the network, we have large amounts of data and this is a good system to access it
  • Good ROI for being easy to learn how to use for new employees, not much time spent which saves costs
  • Good ROI for being able to integrate with Spark and other applications, hence data can be analyzed through programs
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  • With separate compute and storage feature, the queries get executed quickly and it improves our overall productivity.
  • Earlier we were using a different product for analytical purposes, but with Snowflake's in-built analytical feature we are now able to save money.
  • Snowflake is cost efficient, features like auto suspend for compute resources helped to control the costs.
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ScreenShots

Snowflake Screenshots

Screenshot of Snowflake Installation