Vertica Reviews

The solution is a BI solution that includes machine learning. Our company is involved in the distribution of water and we use it to capturing data for several points and to discover where there might have been a loss of drinkable water. 

There is a problem with the water distribution because the company that I'm working for has an index of 32% of water loss during the process of the distribution. These losses can be from a different source. It can be from leakage, it can be an error or on the meter read which can have many issues, sometimes the problem occurs in different hours depending on the pressure of the water network.  We need to use artificial intelligence to collect millions of the data points to detect where the problem might be coming from.

The solution has great capabilities. The tool that instructs the internal database forward is easy to use and is very powerful. 

The product could be less expensive and could benefit from a better marketing strategy. 
In a future release, I would like to have one application to help create intelligent models.

We have been testing and developing the solution for two years.

We did not have any technical problem with the solution.

The solution has great scalability. We started with one terabyte of compressed data, this is a lot of data and we never had problems with the scalability. You can have hundreds of terabytes with the solution if you want, it all depends on your needs.

The customer service is very good. They could improve on their expertise and knowledge of bigger projects and their support for them. Some of their information about collecting data I was not satisfied with their help. They could improve on customer service a bit.

I rate the technical support an eight out of ten.

We currently use IDOL as well as Vertica.

The product is not easy to set up because you need a lot of training. It has less to do about the product itself, but the knowledge on how to use it. For example, the spreadsheet product Excel, If you don't know mathematics, you will have difficulty to make big Excel model and that's the same with the Vertica. It's just only a tool and depending on your capabilities to design what you need. 

We are the developers of a solution and it is a very sophisticated project. It's something that we spent two years to develop. We already tested it and we are only waiting for the customer to try it and then purchase it. It has taken some time to implement the solution to the way we wanted for our company.

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.

We use another product called IDOL and use them both together as our solution. Sometimes you use both or sometimes you use each one separately. The two products are machine learning products but with different uses. IDOL has a more developed application and is much bigger than in Vertica. 

This solution is used by several big companies such as Bank of America, Uber, and Facebook. Where you need a BI with intelligence. We use the solution because it is very good, you can make interconnections with anything to collect the data, any type of data. I have tried other products and they did not fit as well as this one did, I recommend Vertica.

I rate Vertica a nine out of ten.

The biggest, most valuable feature for us is the clustering aspect with a share-nothing mentality. Most clusters usually require their own shared storage, shared subnet, etc. and this becomes a pain and a nightmare to maintain.

The second most valuable feature is that it's very easy to maintain. It's a breeze once you know how to handle it with your scenario in mind.

Loading raw data and leveraging column store to quickly aggregate the values as well as run a general analysis were the biggest improvements we found. Before, we had to scrub the data or reformat, load it, possibly scrub it some more, and then run the first set of analysis, and so on.

With Vertica, we were able to combine some of these steps, such as loading gzip data directly into the table and leveraging R in Vertica to run all of the analysis.

Developer Tools - Vertica really needs some kind of IDE plugin for a system such as Eclipse or IntelliJ. Developing external functions in Vertica can kind of be like shooting in the dark sometimes. Also, an improved monitor or monitoring with alerting built-in that actually works would be a welcome addition.

They truly need a Python or some script that can handle all of the low-level system changes for you and find out how the customer has heavily modified their nodes before the install. Some automation here would help a lot.

The product overall is a great product, however management tools as well as monitoring tools are lacking. The product does, however, offer a lot of information in the form of system views and tables, but most of the data is hard to translate with out the help of their support team.

I have used HP Vertica in multiple companies over the last four years. We currently have it running on a three-node Centos cluster and a six-node Centos cluster.

There have been no issues with the deployment.

There have been no issues with the stability.

We have had no issues scaling it for our needs.

Like everything else HP has support for, the support is very poor. You normally have to threaten to leave, not buy support renewals, or call your sales rep to talk
to anyone who knows anything about the product. The community normally knows more than support and most of my questions or issues were resolved by searching the old community boards while I wait for overseas support to ask me to send them the logs again for the 50th time.

I have previously tried SQL PDW, Mongo, Cassandra for alternatives. Even though all of those products are in different landscapes, the Vertica column store ended up being the best thing that was needed.

It is straightforward if you read the documents and have mid to senior-level knowledge of Linux. Non-Linux admins will find the setup complex and cumbersome since most are Windows admin and they want point-and-click.

We implemented through our in-house team. You need to read the docs, then read them again, and then make yourself a cheat sheet. Once you have done the setup for a two-node cluster, do some Research and Development before taking the time to do a large production cluster or buy the license.

ROI is great compared to the previous solution, SQL Server.

TCO is much lower given the Linux OS and the fact that Vertica is licensed by data size and not node count. The best advice for licensing is to make sure you have a proper data retention policy in place and well-documented as well as some growth expectations before buying. Following this, it will make sure you don't over or under buy.

If you are not Linux savvy, find a person that is. Make a cheat sheet with the commands and/or steps for your environment. If you are in the cloud, make sure to understand the networking aspect is completely different in AWS from it will be in your local data center. Failure to plan is planning to fail with Vertica implementation, and try not to mess up the spread as it's a pain to fix. If you read the documents, you will see what I am talking about.

Super-fast aggregated results from massive data.

We can process vast amounts of data, fast and with a high degree of reliability.

Better feedback from installation. I would like to see more meaningful errors returned and more graceful handling of those. Thankfully, we don't often hit error conditions.

4 years.

No

No

Depends on the environment. Generally pretty good. If you have a large catalog, you can get timeouts adding nodes. Large catalog issues have been dealt with it recent releases so this should make scaling up even more robust.

Excellent. It can take some time to get to the right people but generally our issues are all addressed in an acceptable timeframe.

Greenplum. It was less stable.

Vertica is very robust and recovers predictably from unexpected infrastructure failures.

Great overall solution.

I use Vertica for traditional data warehouse reporting and some advanced analytics as well as real-time data processing or real-time analytics. I sell Vertica, so my clients' use cases vary. There are several types of users, and each company is going to have different requirements. And the number of users is not necessarily a great measure of usage if you're doing clickstream-type data on a website with massive numbers of queries coming in very quickly. But in other cases, you have people hammering it for reporting and various kinds of business use cases.

Vertica has a few features that I like. From an architecture standpoint, they have separated compute and storage. So you have low-cost object storage for primary storage and the ability to have several sub-clusters working off the same ObjectStore. So it provides workload isolation. Vertica handles analytics and large queries extremely well — very high performance —and on top of all of these features, Vertica has a very low total cost ownership.

Vertica offers a platform-as-a-service version, but their software-as-a-service solution is only available on AWS. They need to get a SaaS version on Azure and GCP as fast as possible. I know that's in their roadmap, but I can't wait.

I've been using Vertica for almost three years.

Vertica is rock-solid.

Vertica is massively scalable. My customers' workloads vary. While the number of users is not necessarily the ideal performance measure, the user bases range from a few dozen in smaller setups to thousands in the largest setups. And some of my customers have several hundred users on the platform concurrently.

I deal directly with Vertica's engineers, so I don't have much experience with their frontline tech support. However, the more senior tech support on the backline is just world-class. These guys are amazing. And they are exceedingly committed to their customers. I've seen them do crazy stuff to support a customer.

Setting up Vertica is pretty straightforward. There's nothing super fancy about deploying Vertica. Depending on what you're doing, deployment can take anywhere from 10 to 15 minutes to several hours. It's quick if you want to use the automatic tools, which work very well. And if you want to do your own thing or do it on-prem, then it's going to take you a little bit longer. And you don't need large teams to administer it afterward.

I rate Vertica 10 out of 10. Anyone thinking about using Vertica should try to understand more than its basic functionality. They should look at its more powerful features like advanced, predictive, and real-time analytics. And just to add, that in the world of data warehousing, it's more accurate to say "near real-time" rather than "real-time" analytics. 

We push both raw and modeled data into a Vertica cluster. It is used mainly for internal analysis and Tableau reports by data scientists and analysts.

It is tremendously scalability, with excellent performance. Vertica gives knowledgeable users and DBAs excellent tools for tuning.

• Its projections and encoding are excellent tools for tuning large volumes.
• The product is simple and elegant.
• It has excellent written documentation. I am able to answer any question by querying on Google.

You need to know what you are doing to get the most out of Vertica. 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.

Speed of query response time for complicated queries on tables with billions of rows including joins on varchar columns. There is no limitation on which columns can be queried or joined on and we see query times in the milliseconds for a lot of queries that just won't return at all from other products.

Ease of administration. The Management Console we thought was a nice to have turns out to give us insight on what is happening behind the scenes so easily it has sped up query tuning, insight as to what jobs are running, and resource use on the boxes the product sits on.

Style of deployment. We were able to build out a server farm exactly as we are accustomed to. We did not have to buy fancy hardware. Our first cluster was deployed on servers we had sitting around from other migrations and replaced products. As we grow also the growth is native to how we do business.

We can have insight into data we never had before. We can provide that insight to internal users so we do not have to generate reports for them all the time. With response times like these there is no concern of having them wait for results to return and so they do not think things are broken.

Getting the Management Console up and running as expected was a bit of a challenge.

We've been using it for one and a half years.

We have amazing stability. We even had to migrate the databases to other boxes and found it moved the data without much intervention from us and no down time. It worked exactly as a cluster should. We joke here all the time that we would love to say we like Vertica support but since we never need them, we actually do not know!

Scalability is one of the huge strengths of this product, and scalable in a way, as I said before, that is native to how we do business.

We've never had to contact them.

We switched off of Infobright because it was not performant at all at the scale we needed. The number of limitations on Infobright are too many to list in a small review like this.

Initial setup of the database was straightforward.

We did need support though for the initial installation. They were incredibly responsive and helpful and deployment was completed in a very reasonable amount of time despite issues initially getting the Management Console up and running.

Pricing is more than fair. This is very reasonably priced and since it is a perpetual license you are not stuck paying it again and again.

We evaluated Netezza and Teradata alongside Vertica.

Do you want to stand up a data warehouse in a reasonable amount of time using the in-house skills accustomed to dealing with an RDBMS? If that is the case, nothing beats Vertica, hands down.