Vertica by the Hour, Amazon Linux

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. 

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.

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.