OpenText Analytics Database (Vertica) Reviews

We primarily focus on integration of data from different source systems to warehouse loading. OpenText Analytics Database (Vertica) serves as our warehouse solution.

Our on-premises database is OpenText Analytics Database (Vertica). Data from different source systems such as Oracle and Salesforce is dumped into our warehouse solution. After that, we pull the data from data ingestion. For data ingestion, files are received from OpenText MFT into a loading zone. Then Oracle and Salesforce data are extracted using ETL tools such as ADF. We then load the data into OpenText Analytics Database (Vertica) staging. Our data integration and transformation follows, which is used to build the unified customer table. At the final stage, we use star schema modeling with fact tables and dimension tables loading. After that, we perform performance optimization in OpenText Analytics Database (Vertica).

For end user purposes, we use Tableau. We connect to Tableau and Power BI. When somebody wants to generate monthly sales dashboards, customer 360 analysis, or campaign effectiveness reports, we use OpenText Analytics Database (Vertica) for those purposes.

OpenText Analytics Database (Vertica) has delivered significant improvements to our organization. The platform has enhanced our analytics capabilities, improved reporting performance, and enabled cost savings through better resource utilization and storage compression.

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.

OpenText Analytics Database (Vertica) is a very powerful analytic database, but like any platform, there are areas where it can improve to make daily work even smoother. Better cloud-native experience is one area for improvement. OpenText Analytics Database (Vertica) was originally designed as an on-premises analytic database and later moved to cloud. Improvement opportunities include more seamless cloud-native features such as auto-scaling, serverless options, and easier cluster management. Competitors such as Snowflake and BigQuery provide more fully managed experiences. Easier UI is another area for improvement. Most administration is currently done by SQL and command line tools. An improvement opportunity would be a more modern web UI for monitoring, workload management, and troubleshooting. Faster ecosystem and community growth is needed. In short, OpenText Analytics Database (Vertica) could improve in areas such as cloud-native capability, modern UI for administration, stronger real-time streaming integration, and growing its ecosystem and community. These enhancements would make it easier to manage and adopt compared to newer cloud-first analytic platforms.

From a day-to-day operational perspective, there are a few areas where OpenText Analytics Database (Vertica) could improve to make our work smoother. Smarter automatic projection management is needed with more intelligence, auto projection creation, automatic optimization, and reduced manual testing with better workload management. Right now, monitoring queries often requires system tables and manual analysis. Troubleshooting slow queries takes time. A modern real-time dashboard showing query bottlenecks and resource users would enable quick detection. The impact could be faster issue resolution and less time spent debugging performance. Storage native interaction with modern data tools is also important. In short, from a day-to-day perspective, improvements in automatic projection optimization, better workload monitoring dashboard, easier schema evolution, and stronger modern tool integration would significantly reduce manual tuning effort and improve developer productivity. While OpenText Analytics Database (Vertica) is very powerful, these enhancements would make it more efficient for the analytics team.

I have been using OpenText Analytics Database (Vertica) for almost six years.

There is no challenge integrating data with OpenText Analytics Database (Vertica) from different data sources. Direct one-to-one loading from different source systems is straightforward.

OpenText Analytics Database (Vertica) has been highly scalable for our organization's growing data analytics needs. We have experienced easy horizontal scaling, consistent query performance as data grew, and the ability to handle large analytic workloads. Storage and compression help us scale effectively. OpenText Analytics Database (Vertica) has scaled very well for our analytic needs. As our data volume grew, we were able to add nodes and maintain consistency in query performance. Its MPP architecture and compression have helped us handle large data sets and increasing user workload efficiently.

Overall, our experience with OpenText Analytics Database (Vertica) customer support has been good and reliable.

Negative

I recommend that others explore OpenText Analytics Database (Vertica) more thoroughly as it is already a highly effective solution. They should consider OpenText Analytics Database (Vertica) instead of other databases. My overall rating for this solution is 8 out of 10.

I used OpenText Analytics Database (Vertica) in my previous company for three years. The main use case was reporting. I worked for a fintech company that had a massive amount of data, and we had this use case for reporting, so we used OpenText Analytics Database (Vertica).

A specific example of how I used OpenText Analytics Database (Vertica) for reporting in my fintech company is that we had an ETL pipeline in which we processed the data and removed all PII and PCI data as part of the pipeline, then dumped all the data in S3. We used OpenText Analytics Database (Vertica) in Eon Mode, so the data was in S3 and we had compute nodes in AWS. We had a license from AWS Marketplace and were using the enterprise version. The consumers were the data analytics team, and our job was to make all the data available in OpenText Analytics Database (Vertica). In some use cases, we were creating projections to make queries faster because we had predictable reports to generate.

OpenText Analytics Database (Vertica) does not support good write, update, or insert queries, but from a read perspective, which was ideal for reporting, it has a strong use case.

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.

OpenText Analytics Database (Vertica) does not support hard delete, and they perform soft delete, which is the case with all columnar databases. If they could support or periodically clean up the data so that the data volume does not grow as much, that would be one suggestion. Beyond that, I think it is solid overall. Projections could be made more dynamic, and if they could find a faster way to update, insert, and delete data, that would also be helpful.

I have been working in my current field for seven years.

OpenText Analytics Database (Vertica) is very stable. The migrations were smooth, we did not lose any data, and we only had to pause the pipeline.

The scalability of OpenText Analytics Database (Vertica) is very strong. Being a columnar database, it scales quite well.

Customer support for OpenText Analytics Database (Vertica) is excellent. We had multiple use cases because we moved from proof of concept to enterprise mode. We had to scale up OpenText Analytics Database (Vertica) and make some changes. Throughout this process, customer support was outstanding, and we had a person actively supporting us from the OpenText Analytics Database (Vertica) team for our use case.

Positive

I did not use any other solution before using OpenText Analytics Database (Vertica). Initially, this was the use case we had. We analyzed all the present tools and started with OpenText Analytics Database (Vertica) itself.

For migration or upgrade to OpenText Analytics Database (Vertica), we were using three nodes and then shifted to five nodes based on the load. The migration was very smooth. We can take a backup in S3 and update it, so the compute part was stateless. The upgrade became very straightforward. Regarding metrics, the latency of the reports was very low, and we had many consumers because it was a B2B and B2C fintech company.

I have seen a return on investment with OpenText Analytics Database (Vertica). I saved a lot of money because the storage was on a cheaper alternative and was not directly on OpenText Analytics Database (Vertica), but on S3. I also saved time because setting up OpenText Analytics Database (Vertica) was a one-time effort, and the ETL pipeline was configured. We had different use cases for different reports, some of which were daily, some monthly, and some hourly. There I saved a lot of time, and the entire pipeline became automated. We benefited in terms of money, time, and employee resources.

The pricing for OpenText Analytics Database (Vertica) is somewhat on the higher side for the license. Beyond that, the support from the OpenText Analytics Database (Vertica) team was very streamlined and excellent. We upgraded the OpenText Analytics Database (Vertica) cluster multiple times, and we always had a person supporting us from the OpenText Analytics Database (Vertica) team, so my experience was really strong.

Before choosing OpenText Analytics Database (Vertica), we evaluated other options such as Snowflake and others, but OpenText Analytics Database (Vertica) was the best for our use case because we wanted our data to be in our system. The support for S3 was also strong, and we had some expertise in OpenText Analytics Database (Vertica), as some team members were experts in it, so we started using it. We analyzed BigQuery and Snowflake.

My advice to others looking into using OpenText Analytics Database (Vertica) is that if you want a read-heavy database and do not want it to be transactional in terms of write, delete, or insert operations, and if your use case is reporting, then OpenText Analytics Database (Vertica) is excellent for that use case. However, it is not ideal if you want to use it as a regular database, such as in e-commerce. If you want to use it in e-commerce, use it primarily for reporting. I would rate this product a 7 out of 10.

The main use case for OpenText Analytics Database (Vertica) is that we have the Hive and a Hadoop layer for data availability, and Vertica serves as a big data solution. Within a Hive table, OpenText Analytics Database (Vertica) is basically a final layer for querying and pulling only the required data points which are needed for the analysis for any kind of reporting and dashboarding.

There are multiple ways we have integrated OpenText Analytics Database (Vertica) with our BI tool. Power BI gives flexibility to integrate OpenText Analytics Database (Vertica) and create dashboards on top of it. That is one use case. Other than that, within my organization, there is an in-house built data build tool. There also, we used to do reporting on top of the final layer of OpenText Analytics Database (Vertica) and pull only the required fields and dimensions which are relevant to our key metrics for our stakeholders based on the problem statement.

OpenText Analytics Database (Vertica) has been a key use case for driving the analytical operations or analytical insights mostly for the OLAP and OLTP use cases. OpenText Analytics Database (Vertica) helped a lot in building the insights and giving the right set of actionable items to the business holders or the stakeholders in the business along with leadership.

The best features OpenText Analytics Database (Vertica) offers in my experience are the simple way to use it and to implement it or to integrate with any kind of open source tool or within the in-house data tools or with any kind of BI tool such as Power BI or Tableau. These things I find quite impressive while using OpenText Analytics Database (Vertica).

OpenText Analytics Database (Vertica) has impacted my organization a lot in terms of driving the key results and giving the key insights to the leadership. Through those data points, a good amount of decisions have been made which was really helpful and impactful at an organizational level.

OpenText Analytics Database (Vertica) is already doing great. There could be a community which could have been much more advanced and more people can be engaged so that any kind of questions, queries, or discussions can be taken faster. That is one of the things which can be improved.

, I used the solution for almost two years.

OpenText Analytics Database (Vertica) is stable.

OpenText Analytics Database (Vertica) can scale to a great extent. We have used it to handle terabytes of our data, and it is performing quite smoothly.

Customer support for OpenText Analytics Database (Vertica) is quite decent.

Positive

OpenText Analytics Database (Vertica) was our first and foremost solution.

My experience with pricing, setup cost, and licensing for OpenText Analytics Database (Vertica) was pretty smooth. The pricing is also competitive, so overall it is a good experience.

OpenText Analytics Database (Vertica) was in our mind from day one, and we went ahead with that.

It was pretty smooth. All the BI tools are supported by OpenText Analytics Database (Vertica). In my use case, it was Power BI where the integration was quite smooth. We just needed to whitelist a few of the IPs for our key core relational databases and that was it. Then later on, we could pull or do a direct query or do an import mode and pull all the relevant data points, and the retrieval was quite faster.

We have different kinds of breaches which we were calculating in our supply chain line haul vertical. One of the metrics was a vehicle placement breach or functional breach. We have pulled those data points and firstly built a key layer on Hive and then pulled the required metrics, specific dimensions, and measures on OpenText Analytics Database (Vertica) and then showcased it on Power BI. We then monitored it based on a month-on-month basis and week-on-week basis and continuously tracked the performance and were able to bring down the breach to a great extent, and along with that, we were able to deep dive into the RCA of what is causing it.

All the features I have covered make OpenText Analytics Database (Vertica) a very reliable and key database for our use case. The reason I did not give it a full ten is that there is still a good scope of improvement which can be done, and I am hoping to see that in the near future.

My advice to others looking into using OpenText Analytics Database (Vertica) is that it is a good tool that can solve your key problems, which you might face while working with other alternatives that have quite limitations which OpenText Analytics Database (Vertica) can overcome. I would rate this product nine out of ten.

My main use case for OpenText Analytics Database (Vertica) is mostly data sourcing and analyzing the data in OpenText Analytics Database (Vertica) for a telecom client.

For example, whatever telecom data we have stored in the OpenText Analytics Database (Vertica) database, we have to analyze as per our use case. For offer decisioning-related use cases, we had to gather some data from OpenText Analytics Database (Vertica) regarding customer analytics, usage analytics, or the different plans. We used to analyze that data based on the requirement.

We also use OpenText Analytics Database (Vertica) to generate SQL queries, and we analyze the data there.

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.

OpenText Analytics Database (Vertica) can be improved by adding some more features in analytics.

OpenText Analytics Database (Vertica) does not have a cloud-based UI that Snowflake has, which features a very good comprehensive GUI for querying and analyzing data. This is something OpenText Analytics Database (Vertica) can improve. Otherwise, the database is very strong.

I have been using OpenText Analytics Database (Vertica) for almost five to six years.

OpenText Analytics Database (Vertica) is stable, and there have been no issues till now.

OpenText Analytics Database (Vertica) has very good scalability. Whenever we need to scale the database, the database configuration is easy to modify.

Regarding customer support, I actually never had to contact customer support, so I do not see any issues there.

We previously used a different solution and also used Snowflake database in place. While Snowflake is a better option, we are pretty much familiar with OpenText Analytics Database (Vertica), so we use OpenText Analytics Database (Vertica) more than Snowflake.

Before choosing OpenText Analytics Database (Vertica), I did not evaluate other options. We directly started with OpenText Analytics Database (Vertica) only.

My company has no business relationship with this vendor other than being a customer of OpenText Analytics Database (Vertica).

I have seen a return on investment from using OpenText Analytics Database (Vertica), specifically in terms of time saved. The time we used to take with our earlier databases has reduced to one-tenth of what was there earlier, which is a positive outcome that can be converted to financial metrics in terms of return on investment.

My experience with pricing, setup cost, and licensing is limited because the organization handled the licensing and pricing as well as the cost setup.

My advice for others looking into using OpenText Analytics Database (Vertica) is to proceed with it. We found no issues, and it can definitely help organizations in database analytics. I believe we covered everything regarding OpenText Analytics Database (Vertica). I gave this review a rating of ten.

We use the solution for various tasks, including preparing data marts and generating offers. It helps extract data based on rules from the policy team and provides insights to enhance business operations. We also analyze transactions to target customers and improve business performance.

The platform has improved our organization by providing faster data retrieval and cost-effective solutions compared to other databases like Oracle. The columnar storage format allows for quicker data processing and reduced costs.

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

The product could improve by adding support for a wider variety of data types and enhancing features to better compete with other databases.

I have been working with Vertica for five years.

The product is stable. I rate the stability an eight. 

The product is scalable. However, there is room for improvement. I rate the scalability a seven. 

In my organization, I frequently reach out to the support team for assistance. They provide guidance when we encounter issues related to Vertica, such as problems with heavy queries or permission issues. 

Positive

I previously used Sybase but switched to Vertica for its superior speed and cost-effectiveness.

The initial setup was straightforward. I rate the process a seven. 

We implemented the solution with the help of our in-house team. 

The solution is relatively cost-effective. Pricing and licensing are reasonable compared to other solutions.

I rate Vertica a seven out of ten. 

Our company uses one of the latest containerized versions of Vertica. When the curated processes are completed, our company uses Vertica at the end of all pipelines. In our organization, we usually store the curated datasets in parquet files, but we offer Vertica storage access for data consumers to manipulate and query the data easily. Our company also stores data in the Vertica database for analytics. 

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.

Vertica is fast enough in data copying processes. The data digestion process is quickly completed using a certain copy command. The solution gets accelerated in processes if the queries are properly designed. 

I have previously worked with Microsoft Secret Server and Deep Secure. I was familiar with the system's assets and transaction habits, so when I switched to Vertica, I had some expectations in terms of features from the other systems, which didn't quite match. The transactional operations and rollback were missing in Vertica, which I am trying to implement using the AUTOCOMMIT setting, but I am unsure whether the features will work according to my expectations. 

I have been using Vertica for three months. 

I would rate the stability a nine out of ten. 

In our company, we have faced difficulties in scaling the solution for certain use cases. The solution didn't scale as expected in some cases, for which our company team witnessed performance issues. But in our company, we had doubts in the aforementioned cases if it was a query optimization or scaling problem. There are around 100 Vertica users in our organization. I would rate the scalability a seven out of ten. 

I would rate the technical support an eight out of ten. 

Positive

I have previously used Redshift, MySQL, PostgreSQL and Microsoft Secret Server. 

When I was dealing with an onboarding task for the solution to implement a specific data pipeline with Vertica, I had to ideally and precisely configure the infrastructure and data architecture so that Vertica worked well enough in integration with Spark. If you have multiple systems and you want to establish communication between them, some drivers need to be provided, and some installations need to be made using JAR files. 

For the deployment of Vertica, no client agents are required because it only involves passing the secret code to Vertica and executing the solution using Airflow DAGs, Vertica operators, Vertica hook and Spark solution, which work satisfyingly together. After processing datasets in our company, we store them in Vertica directly using Spark and Spark connector for Vertica. Our company uses Spark as a computational engine. Our company's operational team mainly takes care of Vertica's maintenance. 

It's an expensive product. I would rate the pricing a five out of ten. 

In one of the projects in our company, we dealt with a huge amount of customer data, and the configuration of this data was quite specific as it arrived from multiple business units. Our customers can have contact with different business units, and each unit is a company in itself. The same customer might pass, for example, one shopping mall of a business unit and might shift to another unit and identify themselves in both business units depending upon the identification process of how the data is collected. 

The aforementioned project required identifying a unique user or customer with contacts with multiple business units and commercial centers. Our company processed a huge amount of the mentioned data type with the help of data science and machine learning models. Using the clustering process and leveraging the identified cluster users stored in the Vertica database, we executed some basic analytics on transactions involving customer spend, frequency of visits to a commercial center, and types of product selection. 

I found the swapped partition of tables option to be extremely useful for our company which allowed me to perform some quick operations that would be otherwise impossible to guarantee the rollback in operations involving all assets. I would overall rate the solution an eight out of ten. I am unsure if my company would stick with Vertica in the future and it also depends on the vendor's promises on Vertica's scaling capacity. 

In my company, we use Vertica as it's a tool meant to serve as database performance software and to make some selections, including some usual activities on a database revolving around options like select, insert, update, delete, and a few more.

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

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.

I have been using Vertica for two years. I am a user of the product.

The stability offered by the product depends on various factors. Honestly, in my company, we encounter some problems with Vertica, though it is something that depends on the applications for which one uses it. I consider the stability of Vertica to be okay.

Around 100 people in my company use Vertica.

The product's initial setup phase is extremely simple.

The solution is deployed on the cloud.

Vertica is an expensive tool.

My company chose Vertica over other products since I think that all the other products we use are meant for the children in our college. I don't exactly know the reason why my company chose Vertica.

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.

We use the solution for the warehouse. We implement machine-learning solutions such as clustering or classification models.

We can implement advanced solutions with very interesting capabilities to review whether the customer returns the tool and the licensing cost.

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.

Pricing could be more competitive.

I have been using Vertica for three years. We are using the V23 of the solution.

I rate the solution’s stability an eight out of ten.

100-200 users are using this solution.

I rate the solution’s scalability a nine out of ten.

Support is good.

Positive

The initial setup is straightforward. The deployment process includes creating a solar package to deploy this package on-premise or in a cloud environment. The solar package has all the configurations and components we need to implement as part of customer solutions. There are various software components requiring a specific configuration. So, we package this solar component and deploy it in the customer environment.

I rate the initial setup an eight out of ten, where one is difficult and ten is easy.

I rate the product’s pricing a seven out of ten, where one is cheap and ten is expensive.

You can implement a cluster of servers, and we should guarantee high availability in a disaster recovery scenario. You can use Vertica in a production environment with distributed workflows and workloads. Vertica is available and has parallel processing and other capabilities.

You can implement a cluster of servers to guarantee high availability and massive parallel processing. It's a very sophisticated solution.

Vertica can be used to implement machine learning models such as classification, clustering, and aggregation models to support various use cases depending on customer needs. We have already implemented some machine learning models to detect anomalies. Some employees have distinct patterns in their working behaviors. 

It is another feature-intelligent solution from OpenTex. It can implement or process structural data such as images, videos, text documents, and semi-structured data.

I recommend using this kind of solution because you can index your data and use a balancing algorithm to manage and retrieve data efficiently. Customers don't need a very large infrastructure to implement this type of solution. You can use it to implement advanced machine learning models, classification, and clustering. It also supports advanced artificial intelligence solutions.

Overall, I rate the solution a nine out of ten.

We use Vertica for different business analytics, like IPTV and viewership analytics.

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

Vertica's native cloud support could be improved, and its installation could be made easier. It's possible to deploy the solution on different hyperscalers, but it's not an easy process. Vertica is an MPP database, and sometimes, some nodes may fail. It could have a better warning system to let us know if we use all the storage space.

I have been using Vertica for more than five years.

Vertica provides good stability.

I rate the solution a nine out of ten for stability.

There are different options for scaling the solution through physical or virtual nodes or Kubernetes containers. Scaling is easy, but once we add more nodes, some actions have to be performed on the database. More than 200 users are using the solution in our organization.

The solution's technical support is great.

We usually work with other vendors like Netezza and Oracle, some open-source databases, big data systems, and cloud-native tools like Azure, GCP, or BigQuery. We decided to go with Vertica because we had everything on-premises, and we preferred to have a database on-premise.

The solution's deployment takes a week or even more. We implemented the solution with the help of its support. The deployment can be done in-house, but expertise is needed.

Vertica has a perpetual license, but they are currently trying to convert all those licenses to subscription-based licenses on a yearly basis.

Vertica improved real-time data ingestion from sources and reporting to business users. The solution's native functions were usable for some simple use cases. However, developers prefer something else, like Python, for some complex projects. Vertica has features very similar to those of other databases like Netezza or Snowflake. The solution provides great value for its price.

Vertica's integration with third-party systems is very easy because it supports standard integrations like ODBC and JDBC. The solution's price-performance ratio is great, and it is used as a group data warehouse.

Overall, I rate the solution a seven out of ten.

In this particular use case, our company is in the media industry and we rely exclusively on Vertica for our analytical and operational reporting requirements.

What I find personally beneficial about Vertica is its ability to scale effortlessly, regardless of the data load. 

In addition, there are several optimization techniques available to fine-tune queries, resulting in faster report generation times compared to any other databases I have worked with.

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.

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.

Vertica is a solution that my team manages.

We have been working with Vertica for a year and a half.

We are currently using version nine, specifically 9.3 or a similar version. We have not yet upgraded to the latest version.

Vertica is always stable.

We have not had any stability issues with Vertica.

Vertica is a scalable solution.

I would rate the scalability an eight or nine out of ten based on the use case that we do.

We are a team that handles operational reporting, and our Vertica implementation is used by thousands of people. However, the team responsible for managing and writing code for Vertica consists of only around ten to twelve people.

I personally have not contacted technical support.

Snowflake is a platform that we use in our company. We have been using this solution for the last six months.

As a user of the platform, I don't engage in Snowflake development directly.

Instead, I use Snowflake as a platform where tables are already set up for me to work with. 

I do not handle any aspects of Snowflake management, such as table creation or sharing configurations, as these are typically set up with minimal clicks. 

My work revolves solely around Vertica, and I am responsible for handling everything related to Vertica.

Regarding the initial setup, I don't have direct involvement in it and cannot provide much comment. It's something that my team handles, and while they found it easy to set up, I don't have many specific insights to share.

Our team handled the deployment, we did it ourselves.

It is not difficult to maintain. 

We have a Database Performance Analyzer (DPA), and we've gained experience over time. I've been handling it for three years now.

I am aware that we have licensed it, but I have no knowledge of its cost. As I am not involved in that area.

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.