Find out what your peers are saying about Snowflake Computing, Oracle, Teradata and others in Data Warehouse.
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.
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.
Technical support is very costly for me, accounting for twenty-five to thirty percent of the product cost.
They are responsive and get back to us.
I would rate my experience with technical support around six on a scale of 1 to 10 because I have not had a particular experience with technical support.
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.
Overall, our experience with OpenText Analytics Database (Vertica) customer support has been good and reliable.
It is provided as a pre-configured box, and scaling is not an option.
We go from a couple of users to tons of users all the time, and it scales and handles things really well.
I give the scalability an eight out of ten, indicating it scales well for our needs.
As a consultant, we hire additional programmers when we need to scale up certain major projects.
We have experienced easy horizontal scaling, consistent query performance as data grew, and the ability to handle large analytic workloads.
OpenText Analytics Database (Vertica) has very good scalability.
OpenText Analytics Database (Vertica) can scale to a great extent.
Microsoft Parallel Data Warehouse is stable for us because it is built on SQL Server.
OpenText Analytics Database (Vertica) is very stable.
The cloud version is only available in AWS, and in the Middle East, it is not well-developed in the Azure environment.
It would be better to release patches less frequently, maybe once a month or once every two months.
Addressing the cost would be the number one area for improvement.
When there are many users or many expensive queries, it can be very slow.
Smarter automatic projection management is needed with more intelligence, auto projection creation, automatic optimization, and reduced manual testing with better workload management.
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.
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.
Microsoft Parallel Data Warehouse is very expensive.
The pricing for OpenText Analytics Database (Vertica) is somewhat on the higher side for the license.
It operates as a high-speed data warehouse, which is essential for handling big data.
The columnstore index enhances data query performance by using less space and achieving faster performance than general indexing.
Microsoft Parallel Data Warehouse is used in the logistics area for optimizing SQL queries related to the loading and unloading of trucks.
There's a feature that allows users to set alerts on triggers within reports, enabling timely actions on pending applications and effectively reducing waiting time.
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.
Projection and columnar storage are the most valuable features because they dramatically improve query performance and reduce the need for index management.
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.
| Product | Mindshare (%) |
|---|---|
| OpenText Analytics Database (Vertica) | 5.1% |
| Microsoft Parallel Data Warehouse | 2.9% |
| IBM Netezza Performance Server | 4.4% |
| Other | 87.6% |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 5 |
| Large Enterprise | 33 |
| Company Size | Count |
|---|---|
| Small Business | 16 |
| Midsize Enterprise | 6 |
| Large Enterprise | 22 |
| Company Size | Count |
|---|---|
| Small Business | 29 |
| Midsize Enterprise | 23 |
| Large Enterprise | 43 |
IBM Netezza Performance Server offers high performance, scalability, and minimal maintenance. It seamlessly integrates SQL for efficient data processing, making it ideal for enterprise data warehousing needs.
IBM Netezza Performance Server is known for its outstanding data processing capabilities. Its integration of FPGA technology, compression techniques, and partitioning optimizes query execution and scalability. Users appreciate its appliance-like architecture for straightforward deployment, distributed querying, and high availability, significantly boosting operations and analytics capabilities. However, there are areas for improvement, particularly in handling high concurrency, real-time integration, and specific big data functionalities. Enhancements in database management tools, XML integration, and cloud options are commonly desired, along with better marketing and community engagement.
What are the key features of IBM Netezza Performance Server?Industries rely on IBM Netezza Performance Server for robust data warehousing solutions, particularly in sectors requiring intensive data analysis such as finance, retail, and telecommunications. Organizations use it to power business intelligence tools like Business Objects and MicroStrategy for customer analytics, establishing data marts and staging tables to efficiently manage and update enterprise data. With the capacity to handle large volumes of compressed and uncompressed data, it finds numerous applications in on-premises setups, powering data mining and reporting with high reliability and efficiency.
The traditional structured relational data warehouse was never designed to handle the volume of exponential data growth, the variety of semi-structured and unstructured data types, or the velocity of real time data processing. Microsoft's SQL Server data warehouse solution integrates your traditional data warehouse with non-relational data and it can handle data of all sizes and types, with real-time performance.
OpenText Analytics Database Vertica is known for its fast data loading and efficient query processing, providing scalability and user-friendliness with a low cost per TB. It supports large data volumes with OLAP, clustering, and parallel ingestion capabilities.
OpenText Analytics Database Vertica is designed to handle substantial data volumes with a focus on speed and efficient storage through its columnar architecture. It offers advanced performance features like workload isolation and compression, ensuring flexibility and high availability. The database is optimized for scalable data management, supporting data scientists and analysts with real-time reporting and analytics. Its architecture is built to facilitate hybrid deployments on-premises or within cloud environments, integrating seamlessly with business intelligence tools like Tableau. However, challenges such as improved transactional capabilities, optimized delete processes, and better real-time loading need addressing.
What features define OpenText Analytics Database Vertica?OpenText Analytics Database Vertica's implementation spans industries such as finance, healthcare, and telecommunications. It serves as a central data warehouse offering scalable management, high-speed processing, and geospatial functions. Companies benefit from its capacity to integrate machine learning and operational reporting, enhancing analytical capabilities.
