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.

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.