VMware Tanzu Data Solutions and Apache Hadoop are major competitors in the big data analysis and processing category. VMware Tanzu seems to have the upper hand in speed and advanced analytics, whereas Apache Hadoop is favored for its cost-efficiency and data management capabilities.
Features: VMware Tanzu Data Solutions excels in Massively Parallel Processing (MPP) for data aggregation and scalability, making it ideal for complex queries. It integrates with Python and supports data compression, enhancing its performance. Apache Hadoop is strong with the Hadoop Distributed File System (HDFS) for scalable and flexible storage, handling unstructured data efficiently, and integrating well with Spark to boost data processing.
Room for Improvement: VMware Tanzu needs to improve stability and scalability, better integrate with big data technologies, and enhance session management for concurrent applications. Apache Hadoop requires more user-friendly interfaces and improved real-time data processing. Additionally, its integration process can be complex, and formal support structures are limited compared to commercial products.
Ease of Deployment and Customer Service: VMware Tanzu offers versatile deployment options across On-premises, Private, and Hybrid Clouds, with strong vendor support, yet struggles with community-based resources due to a smaller user base. Apache Hadoop offers deployment flexibility in On-premises and Public Clouds, relying mainly on community resources for support, which can vary in reliability.
Pricing and ROI: VMware Tanzu Data Solutions is competitively priced with its open-source version appealing for large data projects, offering good value in licensed versions with extra features. Apache Hadoop is highly cost-effective in its open-source form, though commercial distributions may incur additional costs. Both platforms offer substantial ROI depending on organizational needs, with Hadoop being more appealing for larger enterprises.
I have seen a return on investment with VMware Tanzu Data Solutions because of its speed and the robustness of the environment.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
Customer support for VMware Tanzu Data Solutions has been good with me and with VMware, including Broadcasts.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
Most of our functions or jobs are queued due to that.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
I have faced stability issues, mainly due to the storage my organization has, though I am not sure if it's specifically due to the tool.
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.
VMware Tanzu Data Solutions can be improved as it is better and faster for administration and clusters, Dockers, and Kubernetes.
My experience with pricing, setup cost, and licensing for VMware Tanzu Data Solutions is that it is a bit expensive.
Hadoop is a distributed file system, and it scales reasonably well provided you give it sufficient resources.
I assess Apache Hadoop's fault tolerance during hardware failures positively since we have hardware failover, which works without problems.
The principal aspect is the creation of Kubernetes clusters.
The product is not complex; I do not have to create stored procedures, functions, or views.
| Product | Market Share (%) |
|---|---|
| Apache Hadoop | 3.5% |
| VMware Tanzu Data Solutions | 4.0% |
| Other | 92.5% |
| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 8 |
| Large Enterprise | 21 |
| Company Size | Count |
|---|---|
| Small Business | 30 |
| Midsize Enterprise | 10 |
| Large Enterprise | 49 |
VMware Tanzu is a robust platform tailored for data warehousing, complex analytics, BI applications, and predictive analytics. It excels in scalability, performance, and parallel processing, enhancing data handling efficiency. Users report significant productivity improvements and streamlined operations, making it ideal for comprehensive data solutions.
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