In my opinion, nothing needs improvement in the solution as it is a great product.

The documentation of Vertica is an area with shortcomings where improvements are required.

Vertica needs to increase its sustainability in the future.

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The integration with AI has room for improvement. There is room for better machine learning.

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Every product has room for improvement and Vertica is no different in that way. 

I think the geospatial is quick, but could be quicker.

Continue adding machine learning code to run directly on the database.  

I also think the ability to perhaps directly link to other databases rather than just data sources and files would be another one.  

For new functionality, I think the possibility of adding triggers or programmatic pieces of code might be helpful, depending on the data coming in. It is difficult to say if it would work or cause more issues than it solves. 

W

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In my opinion, Vertica's documentation could be improved. Currently, there is not enough documentation available to gain a comprehensive understanding of the platform. Additionally, the community support for Vertica is limited, making it difficult to find assistance or resources when needed.

When it comes to data sharing or desktop usage, Snowflake has an advantage over Vertica and other platforms such as cash. Snowflake provides reporting capabilities to share data between two different accounts, which is not currently available in Vertica. This feature is particularly helpful when sharing data with third-party vendors or clients. However, Vertica does not provide such capabilities, and it is not possible to set up data sharing between two different accounts with just a few clicks, as it is possible in Snowflake.

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• Performance tuning
• Not much by way of any documentation: The explain plans are very difficult to read / understand. I tried to diagnose some specific queries using the DBD Vertica utility, etc. For one example of using the explain plan, the query was complex with lots of joins and so on (the query took up about three A4 pages), but the explain plan I printed out took up in excess of 32 A4 pages. How on earth would you read that? No visual tools were available that I could find.
• Very little if any training available in the UK: Our company wasn't able to find any on the topic. We found very little if any documentation (from the vendor) that was of much use.
• Cloning / export was not well documented; poor examples.

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Vertica is relatively new and needs some polish and refinement, but its core functionality is excellent.

Documentation overall is fair to good; but lacks continuity or cohesiveness in places.

Although its knowledge base is increasing, it is still relatively small, making some issues difficult to diagnose without consulting Vertica Tech Support.

Vertica does not have native stored procedures or a native scripting language. Instead, external functions (which can be called from within Vertica) using Java, C++, Linux shell scripting, etc., are supported. This is an unpleasant surprise for many developers, but I feel this has not been a big hindrance in my experience. Complex business logic probably does not belong in a high-performance data warehouse platform. Rather, this should be taken care of during ETL.

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They could improve the integration and some of the features in the cloud version.

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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.

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Documentation has become much better, but can always use some improvement. Love the tech support, but hoping Micro Focus will invest in some additional training for the Level 1 responders so they are much more familiar with more areas of the product.

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Vertica can improve automation and documentation.

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We faced some challenges when trying to use the temporary tables feature. First, we installed Vertica on the AWS cloud and tried to read the data from Bitbucket to Vertica. We were able to read the data, but when we were trying to transform the data and load it into Vertica physical tables, we experienced performance issues.

Regarding additional features, we are unsure if Vertica has updated its features, but we've experienced difficulty in integrating with third-party tools. Clients use multiple technologies and value that integration. Sharing data with third parties should also be improved.

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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.

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We’ve built a data ingestion tool to sit alongside Vertica for easy data loading, and I would personally like to see extended developer tooling suited to Vertica – think published PowerDesigner SQL dialect support, IDE with IntelliSense, and stored procedures which we’ve also had to build a work-around module for.

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I guess really the only thing there is if you get a server big enough to handle Vertica, it does just fine. If you're working in a small business, it will tend to overtake most of their budget from a cost perspective because you need so many servers, so much storage, to be able to handle all that stuff.

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• 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.

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It's hard to make it slow for a small data volume. For large volumes, it's hard to make it work. It's also hard to make it faster, and to make it scale.

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I would love to see direct connections to other DMSs. Something like a direct connector to Oracle, MySQL, MS SQL, MongoDB, etc. so that you can copy data between Vertica and other vendors directly and more easily without an ETL tool, dump, transport, or load data.

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While the documentation is very extensive and relatively complete, it’s poorly organized and there are way too few examples. It’s come a long way since the first version I saw, but it still has a long way to go. Plus, there is very little information on the internet. I can find a solution to nearly any MS SQL Server problem using Google. Not so for Vertica.

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We are running our data transformations as an ELT process inside Vertica; we have data at least on the landing area, temporary staging area, and final data model. Data transformations require lots of deletes and updates (which are actually delete/insets in Vertica). Delete in Vertica doesn’t actually delete data from tables, it just marks them as deleted. For us to keep the performance up, purge procedures are needed and a good delete strategy needs to be designed and implemented. This can be time consuming and is a hard task to complete, so more ‘out-of-the-box’ delete strategies would be a nice improvement.

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In a future release, we would like to have artificial intelligence capabilities like neural networks. Customers are demanding this type of analytics.

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Some of our small to medium-sized customers would like to see containerization and flexibility from the deployment standpoint. They don't currently offer this as a platform as a service. Snowflake is offering this capability.

They're available in the cloud. They're available on every cloud, but they're not available as a managed platform as a service offering.

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Stability, scalability (3 node Community Edition) and backup/restore all need to be worked on. Without proper work load management and resource pool allocation, any batch/ETL or streaming jobs which refreshes data frequently will impair OLAP query performance.

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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.

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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.

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

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The biggest problem is the cost of cloud deployment.

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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.

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Sadly, it does not support stored procedures in the way we are used to thinking of them. There is the possibility to code plug-in in C++, but that's out of our reach. Correlated sub-queries are another point where we'd love to see enhancements, plus the overall choice of functions available. ETL with SSIS was not as easy as one we had expected (must remember to COMMIT and we had some issues with datetime + timezone, but that's was probably our fault).

OleDB and .NET providers need some touches; and another great improvement would be support for Entity Framework, which so far I haven't seen.

There is no serious graphical IDE for HPE Vertica, that's frustrating. One free option available is DbVisualizer for Vertica, but it's a bit basic.

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The integration of this solution with ODI could be improved. 

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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.

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Speed: It's already doing what it is supposed to do in terms of speed but still, as a user, I hope it gets even faster.

Specific to our company, we do store the data both in AWS S3 and Vertica. For some batch jobs, we decided to create a Spark job rather than Vertica operations for speed and/or scalability concerns. Maybe this is just due to the computation efficiency between SQL operations vs. a programmatic approach. Even with some optimization (adding projections for merge joins and grouped by pipelined), it's still taking a longer time than a Spark job in some cases.

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More ML, both data prep, models, evaluation and workflow.  Improved support for deep analytics/ predictive modelling with machine learning algorithms. This area of analytics need a stack of functionality in order to support the scenario. The needed functionality includes:

• Data preparation. Scaling, centering, removing skewness, gap filling, pivoting, feature selection and feature generation
• Algorithms/models. Non-linear models in general. More specifically, penalized models, tree/rule-based models (incl. ensambles), SVM, MARS, Neural networks, K-nearest neighbours, Naïve bayes, etc.
• Support the concept of a “data processing pipeline” with data prep. + model. One would typically use “a pipeline” as the overall logical unit used to produce predictions/scoring.
• Automatic model evaluation/tuning. With algorithms requiring tuning, support for automated testing of different settings/tuning parameters is very useful. Should include (k fold) cross validation and bootstrap for model evaluation
• Some sort of hooks to use external models in a pipeline i.e. data prep in Vertica + model from Spark/R. 
• Parity functionality for the Java SDK compared to C++. Today the C++ SDK is the most feature rich. The request is to bring (and keep) the Java SDK up to feature parity with C++.
• Streaming data and notifications/alerts. Streaming data is starting to get well supported with the Kafka integration. Now we just need a hook to issue notifications on streaming data. That is, running some sort of evaluation on incoming records (as they arrive to the Vertica tables) and possibly raising a notification.

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I just expect it to work and be serviceable. When we ran into issues, there seemed to be a lot of different opinions for how to resolve the issues and that was the feedback I gave to them. You talked to one tech, you talk to a different tech they had a much different approach. That was a big frustration point for us.

The upgrade path and which way we should go. So at the end it created a lot of confusion for us, so I wouldn't upgrade it again lightly. We're going to remain on it for the next year, but we'll probably re-evaluate at that point if we want to continue with Vertica or something else.

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We've had issues with query time taking longer than expected for our volume of data. However, this is due to not understanding the characteristics of the database and how to better tune its performance.

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Projections take up a lot of space and hence, compression can be improved. Installation can be more intuitive via a simple, lightweight web client instead of the command line.

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We would like to see better documentation and examples as well as further simplicity in creating clusters, adding nodes, etc. I understand the GUI is very simple but sometimes more insight into what the product is doing and where errors are occurring would help debugging.

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Performance of management of metadata layer (database catalog) needs improvement. We still have to have smaller customers on PostgreSQL; Vertica cannot manage thousands of schemata.

Query performance: Improve either Database Designer (automation of projection design) or performance of queries using suboptimal projection design.

Scaling of execution independently on storage: Upcoming Eon Mode (now Beta in Amazon) will hopefully solves this.

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Limitations in group by projections is where I would like to see an improvement. 

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• Loading times for “real time” sources - for example, loading from Spark creates a load on the DB at high scale
• Connectors to other sources such as Kafka or AWS Kinesis
• Better monitoring tools
• Better integration with cloud providers - we were missing some documentation regarding running Vertica on AWS

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Ability to use analytic functions in where clauses, being able to use aliases in the where and order by clauses will make query writing/reading a lot easier.

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Add geospatial indexes (sounds like they have done it in version 8.0)

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There is serious performance degradation for large datasets. Fact-to-fact joins on multi-billion record tables perform poorly. Star schema joins also perform poorly if the fact tables reach more than one billion records and the dimension tables reach more than one million records.

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It would be hugely beneficial if HP Vertica offered stored procedures.

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I would like to see integration with the latest Hadoop ecosystem.

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vbr.py needs to be improve to support diff no of nodes source to target.

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Faster deletes!

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The time that the mediation process takes and historical information that I can store.

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Debug custom functions in r.

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I have found that coding support could be simplified.

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Whatever's out, the core is not always as great as the engine, especially their first version. That's true, for example, for the Kafka or Hadoop integration.
But they're getting better release after release.

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There are some predictive analytics features that we might be using which we thought were part of IDOL, but it seems some are also already part of Vertica.

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In the versions I worked with, if a majority of the nodes were being loaded under heavy, sustained rates the nodes would see some dramatic decreases in performance due to the data shuffling that needed to occur between all the nodes. To work around that we ended up doing most of the loading in one or two nodes and that helped significantly.

The synchronizations problems occurred when loading about 10 billion events, at a rate of about 100k tuples/second/node across 5 nodes. One of the suggestions from Vertica engineering was to increase the number of nodes to offset how much data was being sync'd per node.

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Some GUI Tools out of the box, better python integration. I would love to see some nice query engine, tooled specifically to Vertica extensions of SQL (with IntelliSense). We currently use TOAD, but it has a lot of bugs and does not provide full support of all Vertica features. For example with the "copy to" command it is buggy - extremely hard to debug.

Other issues would be better query plan display and better management tools like command line admin tools. (MC would probably solve a bit here, I saw a demo on the conference). We have several avid Python users in the company, but this how maybe lower priority after I reviewed my conference materials and found that Vertica now has native Python driver.

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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.

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Profiling, query optimize, management.

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Improve Vertica logging and messages to Vertica startup commands.

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I think they need an easy client so that you can write queries easily, but it's not necessarily a weak point. I think some users would need them.

In other versions of Vertica, there is a lack of integrations in some machine learning models. But today, the latest version has it all. I don't think they need too much improvement in that area. For me, it's great considering the use case I work with.

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More frequent updates.

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Documentation, there are functions that are not documented. UDF SDK, I'd like to see a step by step simulator example in a manual. The read-me code is good, however, an example would be great for starters.

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Data velocity and manageability.

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The documentation could be improved with more examples of commands and step-by-step scenarios.

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I'm concerned that HP Enterprise has sold their software business, and worry about future investment to enhance predictive/machine-learning capabilities.

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• We can’t use vertica as a transactional database because it is unable to handle lot of transactions. • While copying tables straight across from MYSQL, we observe poor performance. Queries take more time for execution. • Our team is challenged during peak loads because of a lack of work load management options. • It doesn’t offer us any configuration options for reducing the size of our database system and limiting our resource usage. View full review »

It needs integration with multiple clouds.

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• Machine learning implementations
• Support for Cloud based environments like Google Compute Engine

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The internal documentation. As a DBA, I really want to understand how its internals works. It needs to handle high concurrency short queries better as Vertica is not handling these well, and we have had to develop our own tool to help us with our dashboard.

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When it is about to reach the maximum storage capacity, it becomes slow.

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I believe the installation process could be streamlined.

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• 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.

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There are a lot of limitations within this product and it makes things extremely hard for developers. It lacks Stored Procedure, packages, and triggers like other RDBMs.

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More analytical functions. Optimization around DML operations such that we be able to use it more. I understand that Vertica is not meant for that, but I would really love it if it has capability to support procedural processing.

Optimization around DML refers to fast DELETE and UPDATE statement such that we can leverage Vertica around those operations. I do understand that Vertica is not meant as an OLTP system, neither I'm asking to have it similar but if DML operations can be optimized, that would be admirable.

Regarding, my comment on the capability to support procedural processing - Vertica as of now is mainly used via SQL only. If we have to use any procedure based operation, we do it via User Defined Functions. If within Vertica itself, we have the capability to create & execute procedure similar to that for functions, it would be a plus. Again, I do understand that this may be against the architecture of Vertica but if there is anything that can be revised to get these supported, that would be preferred.

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Performance tuning; user community is needed.

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More Machine Learning algorithms--Random Forest for sure!

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I really would like to see Vertica able to use heterogeneous storage (RAM, SSD, HDD). Another issue I have seen is that the SQL optimizer fails to make optimizations that competing products are able to do. That’s something that should be improved as well.

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• Active-Active clusters with online replication.
• Greater query concurrency.
• Better documentation/white papers as there arte lots of undocumented issues.

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Vertica’s resource demands for RAM and I/O during load and storage were challenging for our platform. They recommend reserving 40% of storage for Vertica’s internal usage. Lower I/O usage during load is also highly desirable.

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