Compare Apache Hadoop vs. Vertica

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11,500 views|9,516 comparisons
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13,954 views|8,049 comparisons
Most Helpful Review
Find out what your peers are saying about Apache Hadoop vs. Vertica and other solutions. Updated: January 2021.
465,058 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.""The best thing about this solution is that it is very powerful and very cheap.""What comes with the standard setup is what we mostly use, but Ambari is the most important.""The ability to add multiple nodes without any restriction is the solution's most valuable aspect.""It's good for storing historical data and handling analytics on a huge amount of data.""The most valuable feature is the database.""The most valuable features are powerful tools for ingestion, as data is in multiple systems.""The solution is easy to expand. We haven't seen any issues with it in that sense. We've added 10 servers, and we've added two nodes. We've been expanding since we started using it since we started out so small. Companies that need to scale shouldn't have a problem doing so."

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"Eighty percent of the ETL operations have improved since implementing this solution.""The performance is very good and the aggregate records are fast.""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.""For me, It's performance, scalability, low cost, and it's integrated into enterprise and big data environments.""Speed and resiliency are probably the best parts of this product.""It has improved my organization's functionality and performance.""The solution has great capabilities. The tool that instructs the internal database forward is easy to use and is very powerful."

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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.""The upgrade path should be improved because it is not as easy as it should be.""In the next release, I would like to see Hive more responsive for smaller queries and to reduce the latency.""There is a lack of virtualization and presentation layers, so you can't take it and implement it like a radio solution.""The solution could use a better user interface. It needs a more effective GUI in order to create a better user environment.""It would be good to have more advanced analytics tools.""It would be helpful to have more information on how to best apply this solution to smaller organizations, with less data, and grow the data lake.""The solution needs a better tutorial. There are only documents available currently. There's a lot of YouTube videos available. However, in terms of learning, we didn't have great success trying to learn that way. There needs to be better self-paced learning."

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"Fact-to-fact joins on multi-billion record tables perform poorly.""When it is about to reach the maximum storage capacity, it becomes slow.""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.""Some of our small to medium-sized customers would like to see containerization and flexibility from the deployment standpoint.""The geospatial functionality could be designed better.""We are looking for a cheaper deployment for the solution. Although we did a lot of benchmarks, like Redshift. We tried Redshift, it didn't work. It didn't work out for us as well.""They could improve on customer service."

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Pricing and Cost Advice
"This is a low cost and powerful solution."

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"From a cost perspective, the software is less than most of its competitors.""It's difficult today to compete with open-source solutions. In these areas, there is a lot of competition and the price of this solution is a bit pricy."

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Answers from the Community
Morten Calisch
author avatarC Dove
Real User

I haven't used SQream personally. However, if you are only considering GPU based rdbms's please check the following
https://hackernoon.com/which-gpu-database-is-right-for-me-6ceef6a17505

author avatarYuval Klein
Real User

SQreamDB is a GPU DB. It is not suitable for real-time oltp of course.

Cassandra is best suited for OLTP database use cases, when you need a scalable database (instead of SQL server, Postgres)
SQream is a GPU database suited for OLAP purposes. It's the best suite for a very large data warehouse, very large queries needed mass parallel activity since GPU is great in massive parallel workload.

Also, SQream is quite cheap since we need only one server with a GPU card, the best GPU card the better since we will have more CPU activity. It's only for a very big data warehouse, not for small ones.

author avatarTristan Bergh
Real User

Your best DB for 40+ TB is Apache Spark, Drill and the Hadoop stack, in the cloud.

Use the public cloud provider's elastic store (S3, Azure BLOB, google drive) and then stand up Apache Spark on a cluster sized to run your queries within 20 minutes. Based on my experience (Azure BLOB store, Databricks, PySpark) you may need around 500 32GB nodes for reading 40 TB of data.

Costs can be contained by running your own clusters but Databricks manage clusters for you.

I would recommend optimizing your 40TB data store into the Databricks delta format after an initial parse.

Questions from the Community
Top Answer: SQreamDB is a GPU DB. It is not suitable for real-time oltp of course. Cassandra is best suited for OLTP database use cases, when you need a scalable database (instead of SQL server, Postgres)… more »
Top Answer: The performance is very good and the aggregate records are fast.
Top Answer: When it is about to reach the maximum storage capacity, it becomes slow.
Top Answer: The primary use of Vertica is as a data warehouse to perform aggregate and summary reports.
Ranking
3rd
out of 30 in Data Warehouse
Views
11,500
Comparisons
9,516
Reviews
10
Average Words per Review
426
Rating
7.7
4th
out of 30 in Data Warehouse
Views
13,954
Comparisons
8,049
Reviews
6
Average Words per Review
513
Rating
9.0
Popular Comparisons
Also Known As
Micro Focus Vertica, HPE Vertica, HPE Vertica on Demand
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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 Lab
Cerner, Game Show Network Game, Guess by Marciano, Supercell, Etsy, Nascar, Empirix, adMarketplace, and Cardlytics.
Top Industries
VISITORS READING REVIEWS
Computer Software Company32%
Comms Service Provider17%
Financial Services Firm13%
Media Company5%
REVIEWERS
Computer Software Company23%
Media Company19%
Marketing Services Firm16%
Comms Service Provider13%
VISITORS READING REVIEWS
Computer Software Company34%
Comms Service Provider16%
Financial Services Firm8%
Media Company6%
Company Size
REVIEWERS
Small Business37%
Midsize Enterprise21%
Large Enterprise42%
REVIEWERS
Small Business30%
Midsize Enterprise25%
Large Enterprise45%
Find out what your peers are saying about Apache Hadoop vs. Vertica and other solutions. Updated: January 2021.
465,058 professionals have used our research since 2012.

Apache Hadoop is ranked 3rd in Data Warehouse with 10 reviews while Vertica is ranked 4th in Data Warehouse with 7 reviews. Apache Hadoop is rated 7.8, while Vertica is rated 9.0. The top reviewer of Apache Hadoop writes "Great micro-partitions, helpful technical support and quite stable". On the other hand, the top reviewer of Vertica writes "Superior performance in speed and resilience makes this a very good warehousing solution". Apache Hadoop is most compared with Snowflake, VMware Tanzu Greenplum, Oracle Exadata, Microsoft Azure Synapse Analytics and Teradata, whereas Vertica is most compared with Snowflake, Teradata, Amazon Redshift, SQL Server and Oracle Exadata. See our Apache Hadoop vs. Vertica report.

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