Reporting has become faster and more cost-effective but data cleanup and writes still need work
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been working in my current field for seven years.
What do I think about the stability of the solution?
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.
What do I think about the scalability of the solution?
The scalability of OpenText Analytics Database (Vertica) is very strong. Being a columnar database, it scales quite well.
How are customer service and support?
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.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
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.
How was the initial setup?
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.
What was our ROI?
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.
What's my experience with pricing, setup cost, and licensing?
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.
Which other solutions did I evaluate?
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.
What other advice do I have?
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.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Secured data shipping
What do you like best about the product?
Complete integration of data and cumulative services makes it one in all solution for data delivery and transfer on a big corporation level
What do you dislike about the product?
The affordability might not be the best feature as not small startups can afford it.
What problems is the product solving and how is that benefiting you?
Complete one in all integration makes it easy good to go solution for data management
Vertica: Powerful analytics tool
What do you like best about the product?
The vertica datawarehouse offers columnar store database structure capable of handling big data workloads
unlike other traditional databases.
This architecture coupled with other efficient parellel processing techniques
allows for exceptional performance when resolving complex OLAP problems
and rightly so that is their USP.
This comes in handy especially when you deal with near real-time stream workloads usecases.
Lastly, they have launched VerticaPylab which is a great addition for empowering ML based solutions
Also, integration to reporting services such as Tableau are easy to implement.
What do you dislike about the product?
In contrast to alternative tools in market, they have significantly
smaller userbase and hence getting help from online community for issues can be challenging.
After their aquisition from Micro focus to Opentext, there has been few changes in layout in documentation
as well as layout of UI , which creates additional learning curve.
After a certain threshold(of datasize), the operational costs drastically
increases which sometimes is not justifiable
What problems is the product solving and how is that benefiting you?
The goal was to explore the capabilties of datawarehouse and finds its place
in the big list of other established datawarehouse solutions in the market
Specifically for catering the reporting dashboard department and its data requirements.
In contrast to other datawarehouses, we found that integration to reporting
services such as Tableau are highly reliable even for large datasets.
Vertica Big Data Analytics Platform
What do you like best about the product?
As a big data Engineer This Platofrm is more useful for analtics on huge data platform.
It provides the business insights more acuratively as compared to other data wareshouse.
easily to work on all the major clouds.
more advantages on machine learning analysis.
What do you dislike about the product?
its difficult to implement on legacy platform on map reduce and hadoop.
needs improvments on on premises clouds.
What problems is the product solving and how is that benefiting you?
More on the data warehouse it uses ..same thing and improves on the scaling on huge data analytics..
Vertica is good tool if you are looking for managed analytical database with low Ops.
What do you like best about the product?
Easy to deploy both on-prem and on cloud.
What do you dislike about the product?
High concurrency could be managed better.
What problems is the product solving and how is that benefiting you?
We have a large amount of warehouse data using Vertica we can quickly convert massive amounts of data into insights.
Data Analytics with Vertica
What do you like best about the product?
The massive parallel processing power of vertica to handle big data is very amazing when compared to other database. It supports on premise Hadoop and other multi cloud.
What do you dislike about the product?
The integration of Vertica with industry leading BI platform is not straight forward. It will be good if there is a common standard connection component to achieve the same.
What problems is the product solving and how is that benefiting you?
Vertica Aim to become a leading Analytical platform is what aligns with our requirements for handling big data analytics. We have leveraged Vertica for building the data model for processing big data.
Software Engineer
What do you like best about the product?
Vertica is versatile since it supports both on-premises and cloud infrastructure. Besides, it allows engineers to see how query would be on executed and to optimize it both manually and automatically.
What do you dislike about the product?
There are standard SQL functions which Vertica has not supported yet. For instance, pivot, except, unnest, array_agg functions is not provided, causing various issues when migrating from another system like BigQuery.
What problems is the product solving and how is that benefiting you?
Vertica can run on-premises, which allows corporates control thier own infrastructure. As a result, the fees can be reduced by two-thirds of the payment for cloud infrastructure.
