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HPE Vertica Premium - license

Mfg. Part: H7W34AAE | CDW Part: 3923104 | UNSPSC: 43232314
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Product Overview
Main Features
  • License
  • 1 TB capacity
  • electronic
With the ever-growing volume, variety and velocity of data, you can't let analytical query performance get in the way of your most important business decisions. With HP Vertica software you get high-performance data analytics for all of your data to make better business decisions in real time. Packed with powerful features for monetizing all of your data, HP Vertica software manages massive amounts of data quickly and reliably, giving you real-time business intelligence for advanced data analytics.

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Specifications are provided by the manufacturer. Refer to the manufacturer for an explanation of the print speed and other ratings.
Software
Distribution Media: Electronic
License Category: License
License Qty: 1 TB capacity
License Type: License

Header
Brand: HPE
Compatibility: PC
Manufacturer: HP Software
Model: Premium
Packaged Quantity: 1
Product Line: HPE Vertica

General
Category: Online & appliance based services
Installation Type: Locally installed
Subcategory: Online & appliance based services - data analysis

Product Reviews
HPE Vertica Premium - license is rated 3.8 out of 5 by 21.
Rated 4 out of 5 by from Its column-oriented architecture makes it a database specialized for data warehouses. Valuable Features:Vertica is an excellent data warehouse platform. Its column-oriented architecture makes it a powerful database specialized for data warehouses. Data should be designed around a star schema.Data is accessed via SQL, which most developers are already familiar with.Vertica is "catching on" in the software market, so its user knowledge base is gradually increasing.The price seems reasonable, the product is reliable, and it uses SQL, so developers don't need to learn a new language.Improvements to My Organization:It provides very fast results for analysts running reports. These reports are crucial to help our clients strategize their targeted marketing.Room for Improvement: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.Use of Solution:I have 3+ years of experience with Vertica.Deployment Issues:Deployment had only a few minor issues that one finds with most software.Stability Issues:It has been very stable.Technical Support:I would give technical support 8 out of 10. They have been responsive, professional and knowledgeable.Previous Solutions:* I have used traditional, row-oriented relational databases like SQL Server, Oracle and PostgreSQL for data warehousing. They are optimized for handling transactions, not data warehousing. Vertica is optimized for data warehousing and that was very clearly demonstrated in its ability to scan large amounts of data at high speed. It is also very fast at loading data.* Vertica uses a distributed, shared-nothing architecture which allows for nodes to be added (or removed) according to need. This is a very scalable architecture which is very difficult to achieve with traditional row-oriented databases.* Compared to Hadoop, Hive, and Spark, Vertica is much more adept at handling concurrent users.Initial Setup:Installation is recommended for someone familiar with Linux (the only OS available for Vertica). For developers with a Linux background, the issues are very manageable. Documentation is good for the installation, so follow it carefully, step-by-step.Implementation Team:Implementation was in-house. No significant issues were encountered.ROI:ROI is good because Vertica, while not cheap, is a better performer than traditional databases.Other Advice:* 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.Disclaimer: I am a real user, and this review is based on my own experience and opinions.
Date published: 2017-03-03
Rated 4 out of 5 by from Ad-hoc data analysis improved the SLAs for our end clients. Valuable Features:The most valuable feature in the solution is ad hoc data analysis. It improved the SLAs for our end clients.Room for Improvement: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.Stability Issues:The stability is super good, especially when you scale out.Scalability Issues:Before using Vertica, we used to have problems scaling out because we increase our customer base significantly each year. We have more than 20.000 clients now. Since we implemented the Vertica solution, it is much less effort to maintain scalability.Technical Support:I haven’t used technical support, but the IT colleagues definitely have. I think they are rather happy with it. I haven't heard any complaints. It could be quicker sometimes, but that's always the case with big processes.Previous Solutions:Previously, we were basically using an old school setup based on a relational database. I’m not sure which database management system it was.The performance of the previous solution was no longer adequate to support the growth we were seeing in our business. Response times were up to 10-15 seconds on different queries. We needed to get that down to under a second.Now we’ve moved to a real big data analytics solution.Initial Setup:I wasn’t involved with that, but I think that those who did it were happy with the support.Other Advice: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.Disclaimer: I am a real user, and this review is based on my own experience and opinions.
Date published: 2017-01-29
Rated 4 out of 5 by from It's pretty straightforward to get the cluster up and running. Valuable Features:* Speed* Parallelization* SQL language* High AvailabilityImprovements to My Organization:I have seen queries that take over 24 hours on MS SQL Server to complete, complete in less than 10 minutes on Vertica. I have seen queries that take several minutes, up to an hour, on MS SQL Server, complete in less than 10 seconds, sometime less than one second on Vertica. That allows analysts to spend their time analyzing results instead of waiting for results. Certain types of analysis weren’t even possible before, simply because it took too long.Room for Improvement: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.Use of Solution:I've been using it for five years. I started with version 4, which was prior to the HP acquisition.Deployment Issues:It’s a breeze to setup if you’re using hardware and an OS that meet the minimum requirements. If you try straying from the recommendations, you can find yourself in trouble.Stability Issues:If your queries and projections are optimized properly, it’s rare that you’ll run into stability issues. Stability issues are usually caused by improperly configured hardware/OS, or poorly written queries/projections.Scalability Issues:Scalability is great if you size it correctly to start with. Resizing a cluster isn’t for the faint of heart. All the data needs to be redistributed across the cluster when the cluster size changes, and that can take a very long time, depending on how much data you’re storing.Technical Support:The technical support for Vertica specifically is great. They still have lots of the original (pre-HP acquisition) support people working there who know the product inside and out.Initial Setup:It's pretty straightforward to get the cluster up and running - assuming you follow the vendor recommendations closely. Getting your data in, setting up projections, optimizing queries, etc. is not as straightforward. If you’ve never used it before, save yourself hours of frustration and hire a Vertica consultant.Implementation Team:The first time I used Vertica, we tried doing it ourselves in the beginning. We learned a lot from our failures, but still weren’t getting the results we’d hoped for. After getting professional services help, we were pointed in the right direction, and that made a world of difference. I highly recommend bringing in someone who knows what they’re doing to get you started on the right foot.Cost and Licensing Advice:It’s expensive, but it’s good once you get it working properly. Like any complicated software product, you’re paying for years of research and development, support, etc. Everyone’s use case is different, and sometimes it’s difficult to put a price on speed. You pay for the storage, not the number of processors or nodes. They have a community edition that allows up to three nodes with up to one TB of storage. You can try it out for free that way, and once you realize how well it works, you can purchase a commercial license as your storage footprint grows.Other Solutions Considered:At a previous company, we looked at Greenplum as an alternative to Vertica. For our specific use-case, Vertica won the majority of our benchmark tests. If we had a design that required lots of updates and deletes, we may have compromised and gone with Greenplum.Other Advice: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.Disclaimer: I am a real user, and this review is based on my own experience and opinions.
Date published: 2016-11-23
Rated 3 out of 5 by from ​Data Warehouse response times have decreased​. It doesn't support stored procedures in the way we are used to thinking of them. Valuable Features:Speed in query in general and specifically in aggregate functions on multi-million rows tables.Improvements to My Organization:Data Warehouse response times have decreased of one order of magnitude with respect to the previous solution (SQL Server + Oracle).Room for Improvement: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.Use of Solution:One year.Stability Issues:We have a one node cluster on Red Hat and last week the DB went down. The setting to restart the database is not very intuitive and by default the DB does not restart alone.After a reboot, which may be good in some environments, but leaves you with an insecurity feeling.Scalability Issues:Our DB isin in the tens of Gigs, we did not need to scale yet.Technical Support:N/A, not used.Previous Solutions:We had SQL Server, switched for money reasons and space. But we're not sure yet, SQL Server is way more stable and predictable.Initial Setup:No, the documentation is scarce on non standard setups. We had to create a virtual machine locally, set it up and then upload it to AWS.Cost and Licensing Advice:We use the free community license, plenty of space for our environment. If I had unlimited budget I'd buy a preinstalled instance on EC2, much faster, but costly.Other Solutions Considered:Netezza, but I didn't like it. For no particular reason, but the feeling was not right. Redshift - I was not impressed by the performance. Google Big Query - we tried it.Other Advice:Do COMMIT, and enable/enforce constraints because by default they ARE NOT!!!!Disclaimer: I am a real user, and this review is based on my own experience and opinions.
Date published: 2016-11-08
Rated 4 out of 5 by from We built a custom analytical tools on top of Vertica. Valuable Features:* HA Clustering* Speed / PerformanceImprovements to My Organization:We're able to retrieve queries nearly instantaneous for our custom analytical tools we built on top of Vertica.Room for Improvement:More frequent updates.Use of Solution:1 yearStability Issues:None.Scalability Issues:None.Technical Support:Very knowledgable team which has provided excellent documentation for every issue we've had to troubleshoot.Previous Solutions:MonetDB -- unstable, frequent crashes.Initial Setup:Straightforward, was able to get the database up fairly quickly with minimal effort.Cost and Licensing Advice:We're still using the Community Edition (CE).Other Solutions Considered:MonetDB, Cassandra, Amazon RedShift.Other Advice:Great product, very mature and robust. Vertica is able to scale to meet our demands as we scale our business 10x.Disclaimer: I am a real user, and this review is based on my own experience and opinions.
Date published: 2016-11-08
Rated 4 out of 5 by from Columnar storage makes 'hot data' available much faster than a traditional RDBMS solution. Valuable Features:Columnar storage makes 'hot data' available much faster than a traditional RDBMS solution. Also, Vertica scales up quickly and maintains good performance.Improvements to My Organization:Performance management of high-traffic sites - Vertica's ease of scaling has been invaluable for one of our main customers.Room for Improvement:I'm concerned that HP Enterprise has sold their software business, and worry about future investment to enhance predictive/machine-learning capabilities.Use of Solution:3 years.Stability Issues:Not really.... Vertica shines on stability.Scalability Issues:No, scalability is also a strength of the solution.Technical Support:9 out of 10. HPE has some excellent engineers who are eager to help us make Vertica work well.Previous Solutions:I've been a 'full stack' data expert for years, started on Oracle and SQL Server, moved to Hadoop, Mongo, etc, but Vertica was the right fit for large enterprises with high performance demands and ease of scalability.Initial Setup:Initial setup is a bit clunky, like most complex, tunable products can be.Cost and Licensing Advice:Negotiate when their fiscal year is about to close :)Other Advice:It's a solid product that keeps its promises. I do worry about HP Enterprise's sale of Vertica to Micro-FocusRating: 8/10 - it works very well, but some customers worry about 'Vendor lock-in'.Disclaimer: My company has a business relationship with this vendor other than being a customer:We are a Certified Vertica/IDOL (HAVEN) Big Data partner with HP Enterprise.
Date published: 2016-11-06
Rated 4 out of 5 by from The concurrency got better in this version and we are able to run more queries and load concurrently. Valuable Features:The compute and processing engine returns the queries fast and let us use our analysis resources in a better utilization.The concurrency got better in this version and we are able to run more queries and load concurrently.Improvements to My Organization:We built an internal dashboard using the MicroStrategy to increase visibility to our management and our employees. Also, we built tool to expose the data to our selected partners and users to create better engagement with our platform.Room for Improvement:* 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 AWSUse of Solution:We've been using Vertica for a year.Stability Issues:In case of one HD failure in the cluster, the entire cluster got slower. We feel that it should be able to handle such issues.Scalability Issues:No.Technical Support:The support was slow and didn’t provide a solution in most cases. The community proved to be the better source for knowledge and problem solving.Initial Setup:Pretty straightforward, the installation was simple and we added more nodes easily as we grew.Cost and Licensing Advice:Vertica is pretty expensive, take into account the servers and network costs before committing.Other Solutions Considered:We evaluated both AWS Redshift and Google BigQuery.Redshift didn’t fulfill our expectations regarding query latency at high scale (over 60 TB). Regarding BigQuery, we found the pricing structure pretty complex (payment per query and data processed) and harder to control.Other Advice: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.Disclaimer: I am a real user, and this review is based on my own experience and opinions.
Date published: 2016-11-06
Rated 4 out of 5 by from The fact that it is a columnar database is valuable. Columnar storage has its own benefit with a large amount of data. Valuable Features:The fact that it is a columnar database is valuable. Columnar storage has its own benefit with a large amount of data. It's superior to most traditional relational DB when dealing with a large amount of data. We believe that Vertica is one of the best players in this realm.Improvements to My Organization:Large-volume queries are executed in a relatively short amount of time, so that we could develop reports that consume data in Vertica.Room for Improvement: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 ( https://www.itcentralstation.com/products/apache-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.Use of Solution:I have personally used it for about 2.5 years.Stability Issues:I have not recently encountered any stability issues; we have good health checks/monitoring around Vertica now.Scalability Issues:I have not encountered any scalability issues; I think it's scalable.Technical Support:N/A; don't have much experience on this.Previous Solutions:We do have some pipelines accessing raw data directly and process it as a batch Spark job. Why? I guess it's because the type of operations we do can be done easily in code vs. SQL.Other Advice: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.Disclaimer: I am a real user, and this review is based on my own experience and opinions.
Date published: 2016-10-25
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