I would describe Vertica as a user-friendly product. The documentation part of Vertica is almost the same as the one for MySQL, which we have in our company. The documentation part of Vertica is very friendly enough to use, but I can never say that the documentation area is better.

I don't use ChatGPT to help me with my queries since I try to deal with my issues and queries on my own. I don't want anything improved in the solution as it provides us with everything we require in our IT environment. If I know how to run a query, then the results generated by Vertica turn out to be okay.

The stability offered by Vertica is something that depends on the application and the memory of the product. If you work with Vertica in any of your projects, you will see that it works better for you with much more resources than others. You won't encounter problems with Vertica, especially if your company's architects are better at managing and creating interfaces.

I recommend Vertica to those who plan to use it since it is easy to configure and use. I don't think you would encounter any problems with Vertica. The syntax in Vertica is easy to use and user-friendly, which you can find on Google.

I rate the overall tool a seven out of ten.

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I give the solution a nine out of ten.

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Advice that I would give others who are considering this solution is what I always say for all products: you should heavily test your use cases and data with the product. There are different versions of software and there are always new software solutions. You always should do an exhaustive performance testing from the point of view of any database product. Better to let the product speak for itself. Test several vendor's products if possible. 

To be honest, it is hard to see too many issues with Vertica as a solution. I find a few weaknesses in it, but I think that is the same with all databases. I think when you are doing very large fact table to very large fact table joins pretty much every database will experience problems. Joining two tables that are many multi-million rows is just a large volume to process.  

Honestly, I think if you get a proof-of-concept up, you do your typical load and then keep multiplying concurrency and data volume — see how it works for concurrency, speed.  

Vertica so far has proven to be relatively hassle-free. But, like all databases, you do have to keep an eye on how people use it, particularly when it comes to reporting.  

On a scale from one to ten where one is the worst and ten is the best, I would rate Vertica as a nine.  

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We work with both Snowflake and Vertica solutions.

I am exploring the use of both Snowflake and Vertica for various use cases, and I am now evaluating which one would be the best fit for a new use case based on our requirements.

I would recommend using the Database Designer tool to design projections before actively using Vertica, instead of rushing into it like with other databases. Vertica is designed for different use cases, and designing projections beforehand can make things much easier later on. Vertica offers features that we believe people should take advantage of before going into production.

I would rate Vertica an eight out of ten.

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It’s still not mainstream (especially in the UK) and I would say to some extent still ‘improving’ at each release, but it is enterprise ready and a hugely cheaper option than some.

We did some like-for-like comparisons between HP Vertica and Oracle Exadata (work load / timings) and the two compared favourably, with Vertica being faster than Oracle in all but the biggest and most complex of queries.

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• Understand that its strengths depend on a good data warehouse design using a star schema. It was never intended for high volumes of small, randomly distributed inserts, updates and deletes that are typically found in transactional databases.
• It uses column-oriented architecture. It is important to study aspects of this architecture and to implement them and modify them as the database grows in size and more users access the system. This is especially true for projections, run-length encoding, sorting and column ordering. It is important to understand these aspects in order to truly maximize Vertica's performance.

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I would recommend this solution to others if it fits their use case. If someone is looking for a data warehouse solution with a fast query process Vertica is a good choice. However, it is different than a relational database. If the choice was between Postgres and Vertica, I would recommend Vertica.

I rate Vertica nine out of ten.

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My advice to someone considering Vertica is to go for it. 

Nothing is missing. I would rate it a ten out of ten.

The only issues are support and the price. 

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My only advice is to seriously consider using Vertica for your data warehouse needs. I have normally just gone with the flow and learned whatever tools our company chose. When we switched from Aster Data to Vertica, I made the initial recommendation to do so. I am so happy with this product that I am now an HP ASE Certified Vertica Administrator, and moved to a new job that is also using Vertica. I would not have changed jobs if I were not able to continue using this product. I am also recommending to management that we purchase HPE IDOL for our upcoming audio and video analytics needs. HPE Big Data Solutions is a great product suite, and I have bet my career on its future growth.

I can't recommend Vertica highly enough. While no solution is perfect, Vertica offered the most right out-of-the-box, and continues to expand on its offerings with every release. I am looking forward to see what changes come as a result of the Micro Focus spin merger.

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We require one person for maintenance for the 100 users we have.

I rate Vertica an eight out of ten.

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I rate this solution a seven out of ten.

Snowflake claims to have many more features than Vertica, but we cannot compare both solutions as we have not used Snowflake. However, if Vertica integrates the use of third-party tools, it would be great and more competitive.

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The implementation itself is excellent with fantastic features, speed and scalability. They lose a point only for the development experience which relies on third party tooling like squirrel, and not having SQL based stored procedures.

Go for it! Try the pre-installed VM which HP offers to have a play with it and get a feel for it. It can certainly scale better than any other RDBMS and pushes the envelope of SQL analysis so you can query/analyze/report “BIG-DATA” without having to resort to the complications associated with Hadoop & unstructured data analysis. If your data is structured and large Vertica is what you need.

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Make sure you understand how much data that you're going to be incorporating into the big data, so you can actually define the amount of storage and redundant storage appropriately.

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Do a good volumetric analysis to manage the storage needed.

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I would advise others to try the solution out.

I rate Vertica a seven out of ten.

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We tried to use data lake kind of stuff for machine learning, but for the key functionality of the data warehouse, it's great. Personally, I feel they are over-marketing the machine learning feature and for something like the semi-structured data. But for the data warehouse, it's truly a good solution. I want to recommend it highly.

I would tell potential users that it's hard to make it slow for small data volumes. For large volumes, it's hard to make it work, make it faster, and make it scale. Depending on your workload and your use case, you need to first purchase the Red Tool. After that, you need to follow the best practices to have an efficient design.

On a scale from one to ten, I would give Vertica a nine.

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From the beginning, work closely with HPE Vertica. There's a great Vertica community and a great network to many other companies in the world using this system. Vertica is the most flexible columnar storage with an outstanding performance for any kind of situation.

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How useful it is depends upon your use case. It’s not a be-all and end-all solution, and it’s great for data that doesn’t change. If you have massive fact and dimension tables, and you need to do analytics on them, this is the Cadillac. If you’re trying to replace your OLTP system, there are better suited solutions out there.

These days, there are lots of alternative solutions in the big data space. Open source vs. Commercial. Every imaginable use case. Just like any project, there is the right tool for the job, but you don’t always know what tools are available. You end up using something because it worked before on a different job, or it’s the cheapest solution. Your best bet is always to closely determine your requirements, then find the best match.

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Just go for it and try it out; you can download the free Community edition from the HP Vertica website.

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It is important for those considering this solution to have some experience in database management solutions because Vertica is a relational database. Knowledge regarding SQL and any other database related skills is very important in order to implement or use this product correctly.

I would rate this solution a nine out of ten. 

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The customers love them. They absolutely love them.

Before implementing this solution, make sure that it is on the list and that you evaluate it.

I would rate Vertica a ten out of ten.

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The pros are, if you have columnar processing, then this is in your top three solutions. I think the con is the software pricing, and licensing needs to start getting more competitive with the open source solutions, or they need to market their stability a lot more.

Test out the solution. Most people who test it buy it. So that's the biggest draw that it has, you can test in a day.

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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.

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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.

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I would rate Vertica an eight out of ten.

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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. 

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Do COMMIT, and enable/enforce constraints because by default they ARE NOT!!!!

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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.

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I would recommend using Vertica for those people/teams having large denormalized fact tables that need to be processed efficiently. I worked around optimizing the query performance dealing with projections, merge joins and groupby pipelines. It paid off at the end.

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See additional functionality above.

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I would recommend that they highly evaluate all their options. If they're just going to run a small data warehouse, it's probably not a bad solution. If it's something they know is going to grow dramatically and unpredictably? I don't know. I would evaluate hard.

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Loading into HP Vertica is straightforward, similar to other data warehouse appliance databases such as Netezza. However, tuning it for querying requires a lot more thought. It uses projections that are similar to indexes. Knowing how to properly use projections does take time. One thing to be mindful of with columnar databases is that the fewer the columns in your query, the faster the performance. The number of rows impacts query time less.

My advice would be to try out the database connecting to your ETL tools and perform time studies on the load and query times. It's a good database. It works similar to Netezza from my experience but it is a lot cheaper. Pricing is based on the size of the database.

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Make sure the data and table structures are compact. Vertica will create many versions of the same data as a projection and isolated tables will increase size, increasing licensing cost.

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The product is great to use, but there is a steep learning curve initially. Also, we found limited resources for basic operations such as setup and deployment. Most tutorials and documentation were regarding how to run queries and use external tools such as Pentaho, which we weren’t using. We just wanted good explanations of how to optimize using projections, etc. I think it can be a great product if used correctly and implemented by a team who is familiar with the product.

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It is easy to implement this solution for one customer. By tuning the model (projection design) you get incredible performance. You won’t face issues with metadata (catalog) layer up to tens of thousands of tables.

It can be a challenge to operate clusters for many customers with varied data pipelines. Consider using Database Designer.

Don't hesitate to push Vertica (through support/product management) to improve it.

Consider implementing your own tools to automate performance tuning tasks.

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It is a good database that can be used for ad hoc queries as well as analytical queries.

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Don't plan a production usage on high-scale straight on Vertica, use caching or other buffers between the users and the DB. Get yourself familiar with the DB architecture before planing your model (specifically, make sure you know ROS/WOS and projections). Try to avoid LAP before your schema gets stabilized.

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It is worth a try if you are looking to provide a high-performance, big data analytics database.

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I would like HP to help me to do more uses cases with Vertica. We are very interested in becoming a Business Partner in order to offer Analytics As A Service to our customers.

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I would rate this solution a ten out of ten. 

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I rate Vertica an eight out of ten.

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If you plan to use Vertica for different workloads (in term of IO patterns, query frequency, dataset structure) plan to split your clusters: the mother of all cluster patterns becomes quite difficult to manage at some point. We today have around 20 clusters for different usages.

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When we chose a solution, we were looking at scalability, maintenance, and ease of use. With Vertica, we can access big data using regular SQL queries.

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My advice is to clearly define your expectations, and benchmark performance in real-world-like environments. If you expect to be executing 100 queries per second and loading 10 million tuples per minute, then test that, and test several times that so you collect measurements about where the system is liable to break down.

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Great overall solution.

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I would rate this solution 8 out of 10. 

People should be careful with the way they are loading their data because Vertica is very fast at copying data. There are some operations that are slower than Oracle or SQL Server, but normally you wouldn't use that.

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Great product, very mature and robust. Vertica is able to scale to meet our demands as we scale our business 10x.

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I like the new things they are introducing. I want to see more with Python.

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It looks promising.

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First, analyze your business requirements and if the analytics, scalability, and lower maintenance are your requirements then go for HPE Vertica.

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It's a solid product that keeps its promises. I do worry about HP Enterprise's sale of Vertica to Micro-Focus

Rating: 8/10 - it works very well, but some customers worry about 'Vendor lock-in'.

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We are using Vertica because it is a fast and reliable data warehousing tool and provides us a very good data compression ratio. It is cheap in terms of overall cost per TB and also requires less human resource for maintenance and tuning. Vertica I have been told has improved cloud deployment capabilities on any infrastructure. View full review »

It is a complete solution and a also good solution for EC2 instances.

I have not tried to integrate it with other products.

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It's a great tool but to get the most out of it you will have to design your models to fit it.

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The technical requirements for the product are really important. The design tool for vertica is the core of the database for performance. Never forget to use it to create projections to optimize the storage compression and response times. Better compression means the 1TB mark takes longer to be reached.

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My advice regarding this product is a definite "no", due to bad support.

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It is very easy to start using Vertica, however getting the maximum performance from it is a fine art.

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If your product has lots of concurrent queries this solution is not suitable for you, or you need to implement a cache layer.

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