Apr 19, 2020
I haven't used SQream personally. However, if you are only considering GPU based rdbms's please check the following
https://hackernoon.com/which-gpu-database-is-right-for-me-6ceef6a17505
OpenText Analytics Database (Vertica) and Apache Hadoop compete in the data analytics solutions category. Vertica appears to have the upper hand with its advanced analytical capabilities and speed, whereas Hadoop is cost-effective for large-scale data management.
Features: Vertica offers fast data ingestion, impressive query performance, and supports large-scale analytics with its in-memory architecture. Its columnar database structure is beneficial for handling massive datasets efficiently. Apache Hadoop is praised for flexibility in data processing and scalability, excelling in distributed computing across large data volumes. Its ability to handle various data types is enhanced by its open-source nature, making it a cost-effective option for many organizations.
Room for Improvement: Vertica requires better transactional processing and load management, along with clearer documentation. It lacks native support for heterogeneous storage and workload management tools. Apache Hadoop needs improvements in handling complex queries under memory constraints and enhanced user interfaces. Both could benefit from stronger community support to aid in integration and troubleshooting.
Ease of Deployment and Customer Service: Vertica supports both on-premises and cloud deployments, although users report mixed experiences with its technical support. Its ability to provide timely assistance varies. Apache Hadoop's setup can be complex but is versatile in supporting different deployment environments. It benefits from a strong community network, although specialized technical assistance quality can be inconsistent.
Pricing and ROI: Vertica's pricing is based on data size, typically considered fair and transparent relative to its performance benefits. The initial costs can be high, but the ROI is strong thanks to its analytical capabilities. Apache Hadoop offers minimal software costs due to its open-source nature, making it economically appealing for extensive data operations, despite infrastructure expenses. Its cost benefits are significant for organizations with large data sets, although setting up may require higher investment.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it.
If you don't do the upgrades, the platform ages out, and that's what happened to the Hadoop content.
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.
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