Compare Apache Hadoop vs. Vertica

Apache Hadoop is ranked 5th in Data Warehouse with 6 reviews while Vertica which is ranked 2nd in Data Warehouse with 12 reviews. Apache Hadoop is rated 7.6, while Vertica is rated 7.6. The top reviewer of Apache Hadoop writes "We are able to ingest huge volumes/varieties of data, but it needs a data visualization tool and enhanced Ambari for management". On the other hand, the top reviewer of Vertica writes "The architecture means it can process/ingest data in parallel to reporting and analyzing because of in-memory Write-Optimized Storage alongside the analytics optimized Read-Optimized Storage". Apache Hadoop is most compared with Pivotal Greenplum, Snowflake and Oracle Exadata, whereas Vertica is most compared with Amazon Redshift, Apache Hadoop and Pivotal Greenplum. See our Apache Hadoop vs. Vertica report.
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Apache Hadoop Logo
13,112 views|9,553 comparisons
Vertica Logo
30,201 views|8,296 comparisons
Most Helpful Review
Find out what your peers are saying about Apache Hadoop vs. Vertica and other solutions. Updated: July 2019.
353,345 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
The most valuable features are the ability to process the machine data at a high speed, and to add structure to our data so that we can generate relevant analytics.Two valuable features are its scalability and parallel processing. There are jobs that cannot be done unless you have massively parallel processing.Initially, with RDBMS alone, we had a lot of work and few servers running on-premise and on cloud for the PoC and incubation. With the use of Hadoop and ecosystem components and tools, and managing it in Amazon EC2, we have created a Big Data "lab" which helps us to centralize all our work and solutions into a single repository. This has cut down the time in terms of maintenance, development and, especially, data processing challenges.Since both Apache Hadoop and Amazon EC2 are elastic in nature, we can scale and expand on demand for a specific PoC, and scale down when it's done.Most valuable features are HDFS and Kafka: Ingestion of huge volumes and variety of unstructured/semi-structured data is feasible, and it helps us to quickly onboard a new Big Data analytics prospect.High throughput and low latency. We start with data mashing on Hive and finally use this for KPI visualization.​​Data ingestion: It has rapid speed, if Apache Accumulo is used.As compared to Hive on MapReduce, Impala on MPP returns results of SQL queries in a fairly short amount of time, and is relatively fast when reading data into other platforms like R.

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Eighty percent of the ETL operations have improved since implementing this solution.Vertica's most outstanding features are the compression rates achieved and the speed of access of high volume data.Allows us to take volumes and process them at a very high speed.It maximize cloud economics for mission-critical big data analytical initiatives.It maximizes cloud economics with Eon Mode by scaling cluster size to meet variable workload demands.Bulk loads, batch loads, and micro-batch loads have made it possible for our organization to process near real-time ingestions and faster analytics.Any novice user can tune vertical queries with minimal training (or no training at all).Vertica gives knowledgeable users and DBAs excellent tools for tuning.

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Cons
We would like to have more dynamics in merging this machine data with other internal data to make more meaning out of it.I would like to see more direct integration of visualization applications.Based on our needs, we would like to see a tool for data visualization and enhanced Ambari for management, plus a pre-built IoT hub/model. These would reduce our efforts and the time needed to prove to a customer that this will help them.General installation/dependency issues were there, but were not a major, complex issue. While migrating data from MySQL to Hive, things are a little challenging, but we were able to get through that with support from forums and a little trial and error.It needs better user interface (UI) functionalities.The key shortcoming is its inability to handle queries when there is insufficient memory. This limitation can be bypassed by processing the data in chunks.

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Fact-to-fact joins on multi-billion record tables perform poorly.Support is an area where it could get better.Promotion/marketing must be improved, even though it is a very useful product at very good price, it is not as "popular" as it should be.It needs integration with multiple clouds.It should provide a GUI interface for data management and tuning.Monitoring tools need to be lightweight. They should not take up heavy resources of the main server.If you do not utilize the tuning tools like projections, encoding, partitions, and statistics, then performance and scalability will suffer.It would be great if this were a managed service in AWS.

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Pricing and Cost Advice
​There are no licensing costs involved, hence money is saved on the software infrastructure​.Do take into consider that data storage and compute capacity scale differently and hence purchasing a "boxed" / 'all-in-one" solution (software and hardware) might not be the best idea.

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It is fast to purchase through the AWS Marketplace.The pricing and licensing depend on the size of your environment and the zone where you want to implement.Read the fine print carefully.I think it's starting to get a little expensive. Open source products are starting to get more robust, so I think that's something that they need to start looking at in terms of licensing.The first TB is free and you can use all the Vertica features. After 1TB you have to pay for licensing. The product is worth it, but be aware of this condition, and plan. The compression ratio is explained in the documentation.Start with license per 1TB. Starting from hundreds of TB there is unlimited licensing to be considered. Move historical data to HDFS/S3 which are significantly cheaper or even free.

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Ranking
5th
out of 30 in Data Warehouse
Views
13,112
Comparisons
9,553
Reviews
5
Average Words per Review
450
Avg. Rating
7.2
2nd
out of 30 in Data Warehouse
Views
30,201
Comparisons
8,296
Reviews
11
Average Words per Review
322
Avg. Rating
8.8
Top Comparisons
Compared 33% of the time.
Compared 29% of the time.
Compared 14% of the time.
Compared 12% of the time.
Compared 12% of the time.
Compared 11% of the time.
Also Known As
Micro Focus Vertica, HPE Vertica, HPE Vertica on Demand
Learn
Apache
Micro Focus
Overview
The Apache Hadoop project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

Micro Focus Vertica is the most advanced SQL database analytics portfolio built from the very first line of code to address the most demanding Big Data analytics initiatives. Micro Focus Vertica delivers speed without compromise, scale without limits, and the broadest range of consumption models. Choose Vertica on premise, on demand, in the cloud, or on Hadoop. With support for all leading BI and visualization tools, open source technologies like Hadoop and R, and built-in analytical functions, Vertica helps you derive more value from your Enterprise Data Warehouse and data lakes and get to market faster with your analytics initiatives.

To learn more about Micro Focus Vertica Advanced Analytics, visit our website.

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Sample Customers
Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web LabCerner, Game Show Network Game, Guess by Marciano, Supercell, Etsy, Nascar, Empirix, adMarketplace, and Cardlytics.
Top Industries
No Data Available
REVIEWERS
Media Company21%
Software R&D Company21%
Marketing Services Firm17%
Comms Service Provider14%
VISITORS READING REVIEWS
Comms Service Provider34%
Financial Services Firm26%
Health, Wellness And Fitness Company7%
Marketing Services Firm5%
Company Size
REVIEWERS
Small Business29%
Large Enterprise71%
REVIEWERS
Small Business29%
Midsize Enterprise24%
Large Enterprise47%
VISITORS READING REVIEWS
Small Business39%
Midsize Enterprise16%
Large Enterprise45%
Find out what your peers are saying about Apache Hadoop vs. Vertica and other solutions. Updated: July 2019.
353,345 professionals have used our research since 2012.
We monitor all Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
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