OpenText Analytics Database (Vertica) is a high performance columnar analytics database designed for data warehousing and advanced analytics. It uses columnar storage, which provides faster query retrieval and saves storage space. Massive parallel processing allows us to load terabytes of data within a few seconds. It allows horizontal scaling as well. High-speed data loading is possible using the copy command, which is a bulk loading capability. Indexing is also available. OpenText Analytics Database (Vertica) uses smart encoding such as run-length encoding, delta encoding, and dictionary encoding for advanced compression. Real-time and batch analysis are both supported. Real-time jobs and batch ETL jobs can be performed, and streaming jobs are also possible. Common use cases include fraud detection, IoT analytics, stream analytics, and telco analytics.

The OpenText Analytics Database (Vertica) feature that I find most valuable in my workspace is projection combined with columnar storage. This is valuable because it provides huge query performance improvements in analytics projects we run, especially for large joins, aggregation, and dashboard queries. Because OpenText Analytics Database (Vertica) stores data column-wise and uses projection storage for frequently used columns, queries become extremely fast without needing traditional indexes. There is no need for managing indexes. In a traditional database, we spend time creating indexes, rebuilding indexes, and troubleshooting slow queries. In OpenText Analytics Database (Vertica), projections act as optimized data sources and structures automatically. The query optimizer chooses the best projection. This saves a lot of maintenance time and simplifies performance tuning. Instead of complex tuning, we design good projections and partition large tables, which has a big impact on reporting and BI since most of our workload is reporting. Sales dashboards, customer analytics, and monthly reports are significantly improved, which improves user experience.

Projection and columnar storage are the most valuable features because they dramatically improve query performance and reduce the need for index management. They simplify performance tuning and make analytic reporting much faster in daily operations.

I work for a company called Nokia. There is a huge amount of data gathered on a daily basis, including Salesforce data, Oracle data, Memotech Novum, and patent-related data. OpenText Analytics Database (Vertica) is highly valued for query performance, user experience, and BI reporting. The highly scalable and parallel architecture means we do not need to spend most of the time on performance improvement. It automatically handles everything. This is the best feature of OpenText Analytics Database (Vertica).

OpenText Analytics Database (Vertica) has had a significant impact on our analytics platform in terms of performance, cost, and operational efficiency. Before OpenText Analytics Database (Vertica), complex reports usually took 30 to 60 minutes to run. After implementing OpenText Analytics Database (Vertica), the same reports run in two to five minutes. The impact is an 80 to 90 percent reduction in report runtime. Business users now get near real-time insights. ETL and query processing time have been reduced. For example, daily ETL processing has been reduced from 4 hours to 1.5 hours. Dashboard refresh has moved from daily to multiple times per day. We have achieved storage cost savings through compression. Maintenance effort has been reduced, and scalability has improved. OpenText Analytics Database (Vertica) has improved our reporting performance by nearly 90 percent, reduced ETL processing time by more than half, and saved around 70 percent in storage through compression. It has also reduced maintenance effort significantly because we no longer have to manage indexes, and the platform scales easily as our data grows.

For the learning curve for new users, it is quite simple. Although OpenText Analytics Database (Vertica) is not very popular compared to other databases such as Oracle, Teradata, and Snowflake, the UI looks great and is very easy to navigate.

OpenText Analytics Database (Vertica) has backup and recovery features. Currently, we have some tools inside OpenText Analytics Database (Vertica) that we use for backup and recovery in case of data failure, node failure, or some access model processor failure. Based on parallel architecture, it uses other resources, which is effective.

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The best features OpenText Analytics Database (Vertica) offers are that it is parallel, with Massively Parallel Processing (MPP), and it is a columnar database. It works in append-only mode, which is ideal for analytics and read queries. Additionally, I can use it in Eon Mode in which I can store the data in cheaper storage such as Amazon S3 and have different compute nodes. That helped me save costs. Beyond that, it is massively scalable, which was quite useful for our fintech use case. The read speed was excellent, and the data compression is good.

OpenText Analytics Database (Vertica) has positively impacted our organization by helping us reduce storage costs and improve reporting efficiency in our fintech company. The analytics team could run reports much faster, and we saved significant costs on compute and storage, especially with Eon mode and compression.

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The best features that OpenText Analytics Database (Vertica) offers are mainly the parallel processing, ETL capabilities, and the multi-cloud features which are very handy to use.

The parallel processing and ETL capabilities have helped us significantly. GCP cloud is a very useful feature to get the data from Azure or GCP. The ETL capabilities include querying the databases from different systems. Also, parallel processing has helped us generate the data source analysis earlier.

OpenText Analytics Database (Vertica) has impacted our organization positively because earlier we used to use Teradata, and it was very slow, creating issues with processing as well. OpenText Analytics Database (Vertica) has helped us a lot in terms of processing capabilities and the speed of querying.

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The most valuable feature is the speed of data retrieval. Compared to Sybase, Vertica processes queries faster by executing them across multiple systems simultaneously.

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In one of our organization's prior investigation reports on Vertica, it was highlighted that the solution was executed quite quickly due to its columnar storage underground, which is the most valuable feature for our company.

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Vertica is easy to use and provides really high performance, stability, and scalability.

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The best thing about Vertica is that it is a supportive database performance software. Generally, the tool is used for performance and KPIs.

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Vertica uses advanced Azure technologies to compress raw data using indexing, allowing a large amount of data to be stored with minimal physical space. Advanced algorithms are employed in data compression.

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I appreciate the flexibility offered by Vertica's projections. It allows for modifying the primary projection without altering the tables, which helps to optimize queries without the need to modify the underlying data. 

This feature enables us to fine-tune our queries by modifying different projections, which is highly beneficial.

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The solution is quick, has good compression data, and is not expensive.

Vertica is user-friendly.

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The most valuable feature is Vertica's performance and the ease of using the database. Also, it is great for partnering with data.

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The most valuable feature of Vertica is the unmatchable database performance at a fraction of cost compared to other similar databases.

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The most valuable feature of Vertica is the ability to receive large aggregations at a very quick pace. The use case of subclusters is very good.

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Vertica is a columnar database where the query performance is extremely fast and it can be used for real-time integrations for API and other applications. The solution requires zero maintenance which is helpful.

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I think the most valuable thing about the product is the speed and resilience. I would say the strong, well-featured SQL engine with many built-in features — including machine-learning — is also a strong point. 

When testing alternative columnar or in-memory databases the queries their speed may quicker for one or two users, but their concurrency is not as good as Vertica. I would have to say Vertica returns the queries pretty much all the time reliably while other products can sometimes run for 10 minutes and still fail.  

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Vertica's most outstanding features are the compression rates achieved and the speed of access of high volume data.

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I am highly trained on Vertica and I am resistant to using other products because I do not have experience with those products. Some of the most valuable features are cybersecurity and backup.

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It's the fastest database I have ever tested. That's the most important feature of Vertica. It has a lot of contents and machine learning functions. Today we have a lot of options for deploying. It has a lot of integrations.

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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.

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The feature I like best is performance. We use Red Tool and Red Job for the data warehouse and reporting. It's perfect. Performance is good, and it can return ad hoc queries very quickly. Of course, it's a cluster, so it's easy to scale.

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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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You don't have any more dependency on the local store. In order to build the on-prem process for Vertica, you need to make a lot of changes and be picky about choosing your hardware for it. On Eon, you just put it on S3, so you're not paying a penny for the stores anymore. That's the good thing about Eon. 

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The data warehouse has exceedingly high performance and has the ability to do large scale queries very effectively. It fits well in large enterprises.

All features are valuable. It's a combination of capabilities that's all in one place, which is incredibly powerful.

For me, it's performance, scalability, low cost, and it's integrated into enterprise and big data environments.

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The performance is very good and the aggregate records are fast.

Vertica Auto-Projections reduce the knowledge required by the DBA for tuning performance.

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This most valuable feature is the database designer, which helps significantly improve our storage footprint.

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It maximize cloud economics for mission-critical big data analytical initiatives.

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• The tool for performance tuning and recommendations
• Any novice user can tune vertical queries with minimal training (or no training at all).

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• 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.

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HPE Vertica is a unique solution as it handles a huge magnitude of data with matchless speed and simplicity. One feature, which has really benefited us, is the scalability offered by Vertica as it has enabled Pythian's clients to manage data with agility.

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The feature of the product that is most important is the speed. I needed a columnar database, and its speed is what it's built to do, and so that's what really does differentiate Vertica from its competitors.

 I think what also draws me to it is that I don't need any special hardware. So I can use commodity hardware, which is nice to have in a commercial solution.

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Vertica is a columnar database, this support our developments in analytics, advanced analytics, and our ETL process with large sets of data.

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Partition and join back to node are easy and simple for DBAs.

DBAs don’t need to add a partition every month/quarter like with other DBs.

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• Clustered database
• Horizontal scaling
• Disaster recovery
• Columnar Storage
• Compression (you read only columns you need)
• Immutable storage
• Fast ingesting

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• I found the columnar storage, which increases performance of sequential record access, to be the most valuable feature. 
• I also like the projection feature, which increases query performance.

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Vertica’s analytic capabilities are its key strength. It can aggregate and analyze data at massive scale and neatly bring the calculation logic to the data with external procedures in C, Java and R.

The Vertica architecture means it can process/ingest data in parallel to reporting and analyzing because of its in-memory Write-Optimized Storage sitting alongside the analytics optimized Read-Optimized Storage.

Which brings us to projections and the DB designer which intelligently structures how data is actually stored on disk to improve the queries you actually run against it. So tables are a logical construct which are operated on as per other DBMS systems, but there’s a whole next level of intelligence in optimization for querying that puts Vertica in another league.

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Two of them:

• The core feature, meaning their highly efficient columnar file format and execution engine along with a great coverage of ANSI SQL, provides our analysts with a highly expressive and performing platform.
• The extensibility and efficiency provided by their C++ SDK.

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• Quick retrieval of data
• Fast upload of data

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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.

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It has a very good design with high query performance. It provides the scale out capability by adding additional servers instead of scaling up the servers.

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The most valuable feature in the solution is ad hoc data analysis. It improved the SLAs for our end clients.

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We are evaluating storage and database solutions for an OLAP application with following requirements:

• Extract, transform and load high velocity and volume of a numerical data stream on a distributed system.
• Interactive (less than 20 sec latency) query performance for critical group-bys.

Vertica is superior to other solutions in query performance.

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The auto-distribution to all nodes for fault tolerance and query performance was pretty amazing.

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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.

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Columnar storage makes 'hot data' available much faster than a traditional RDBMS solution. Also, Vertica scales up quickly and maintains good performance.

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Speed in query in general and specifically in aggregate functions on multi-million rows tables.

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• HA Clustering
• Speed / Performance

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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.

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Speed and performance: Vertica stands top among its peers in the MPP world, delivering unparalleled speed and performance in query response time. Its distributed architecture and use of projection (materialized version of data) beats most of its competitors.

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• Compression / speed with highly complex queries

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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.

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Analytical features are amazing, the integration is wonderful.

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Fast inserts, queries way faster than in SQL Server.

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Scale-out, analytical functions, ML.

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Super-fast aggregated results from massive data.

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Query Performance.

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Replication

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Speed of columnar data.

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Scalability, query speed.

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Bulk loading data using copy

Fast analytical functions

Ability to extend functionality

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Analytic functions.

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Geospatial

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Manage big data fast and easy.

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User Defined Extensions

Analytic Functions

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Group by performance

Analytic functions

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Recovery by table

Copy cluster

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Columnar data store

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• Massive data ingestion performance
• Performance
• SQL standard query engine

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We're just now getting into Vertica, but it allows us to store and access big data very quickly. It comes down to being able to quickly identify where the root cause analysis is and where trends are, so you can actually try to almost predict where problems are before they really become a problem.

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We use Vertica as our primary data warehouse. It works well, relatively, most of the time.

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Storage abstraction through projections. It gives you the possibility to react to any kind of query with an optimal performance.

The Workload Analyzer helps you easily to analyze your database workloads and recommends tuning opportunities to maximize the database performance. This in turn reduces your operational costs.

I love the hybrid storage model and due to that the full control of load and query behavior. I also like the ability to read semistructured data with FlexTables for DataExploration.

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• Speed
• Parallelization
• SQL language
• High Availability

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I have found great use out of many features, most notably the Management Console and the Database Designer. Many people with lots of experience creating table projections can get frustrated trying to optimize some complex queries, however, in Vertica, the Database Designer is normally a big help in these situations. You can feed it your problem queries and it will make projection design suggestions for you. The ability to have multiple projections on a table to work with different queries is a big bonus.

The Management Console is an invaluable tool for monitoring the health of our Production and DR clusters. Copy Cluster and Cluster Replication help us keep both easily in sync on a daily basis. Integrated R and geospatial functions are helping us improve efficiency and explore new revenue streams.  

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Fast query processing for historical data analytics. Write Optimized Store (WOS) continuous data loading without drastically impacting performance of OLAP queries. It's one of the few columnar databases that has the capability to provide near real time data delivery for analytics with minimal delay sourcing data from traditional databases or NoSQL data stores or any unstructured data sources.

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MPP

Analytical functions

HDFS Copy

Resource management

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The speed of Vertica out of the box with the ability it has to perform complex analytics queries. In other databases, information will return in hours or even days while in Vertica it will be finished in minutes or even seconds. This is the best feature it has.

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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.

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Having projections as a parallel for indexes in a simple MySQL helped keep our data access fast and optimized.

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The most valuable feature is the merge function, which is essentially the upsert function. It's become our ELT pattern. Previously, when we used the ETL tool to manage upserts, the load time was significantly longer. The merge function load time is pretty much flat relative to the volume of records processed.

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Ability to get top performance for in-advance known aggregative SQL queries.

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It provides very fast query performance after good designs of projections.

It's easy to implement for 24/7 data load and usage because you don’t have to worry about “load time slots” since you can load data into reporting tables at all times without worrying about their query load.

It just keeps up and running all the time.

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Easy Installation, Easy to add and quit nodes... View full review »

• As it can load a lot of data very quickly, it is very helpful for our organization in managing our VLDB. • It has the lowest cost per TB compared to other DW solutions. • Provides us clustering for our commodity hardware and a good data compression ratio compared to other DW tools I've looked into. • It is very scalable. We didn’t face any problems in terms of installation. • Allows concurrent users access. Our staff can process and send query requests simultaneously -- it responds to all requests very efficiently. • It is very easy to use! It also is good in providing OLAP support to our database system. We can also use traditional SQL queries and tables. View full review »

Vertica is a great product because customers can compress and code data. The infrastructure that data warehouse solutions need is a commodity server so that customers don't have to invest in infrastructure. Vertica is a column oriented database so the response of queries is very fast. 

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The hardware usage and speed has been the most valuable feature of this solution. It is very fast and has saved us a lot of money.

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