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.

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.

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.

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.