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.
If anything happens in terms of technicalities and I raise a ticket, they address it immediately, irrespective of the SLA agreement.
I am going to give them a ten out of ten.
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.
Broadcom and VMware are doing a great job. They are fast in response.
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.
Simplifying the initial setup and day-to-day operations could reduce the learning curve for new users.
I feel the pricing of VMware Tanzu Data Solutions could be improved, as the product is technically strong but sometimes the costs present challenges for the institutions to absorb and approve.
I think they must ensure compatibility with many cloud technologies, including those from the US and China, to make it more versatile in the future.
My experience with pricing, setup cost, and licensing for VMware Tanzu Data Solutions is that it is a bit expensive.
If you don't do the upgrades, the platform ages out, and that's what happened to the Hadoop content.
Apache Hadoop helps us in cases of hardware failure because it works 24/7, and sometimes servers crash in the field.
Kubernetes native application databases that are managed together allow scaling of application, database, storage, and networking to be automated.
The self-healing feature of VMware Tanzu Data Solutions has helped my team by recreating new instances automatically when particular instances are in a failure state, thereby maintaining the complete environment and reducing downtime.
The principal aspect is the creation of Kubernetes clusters.
| Product | Mindshare (%) |
|---|---|
| VMware Tanzu Data Solutions | 5.0% |
| Apache Hadoop | 3.2% |
| Other | 91.8% |
| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 8 |
| Large Enterprise | 22 |
| Company Size | Count |
|---|---|
| Small Business | 32 |
| Midsize Enterprise | 11 |
| Large Enterprise | 52 |
Apache Hadoop provides a scalable, cost-effective open-source platform capable of handling vast data volumes with features like HDFS, distributed processing, and high integration capabilities.
Apache Hadoop is known for its distributed file system HDFS, which supports large data volumes efficiently. Its open-source nature allows cost-effective scalability and compatibility with tools like Spark for enhanced analytics. While it offers significant processing power, areas for improvement include user-friendliness, interface design, security measures, and real-time data handling. Users benefit from data storage for structured and unstructured data, facilitated by its distributed processing architecture. Data replication ensures fault tolerance, while its capability to integrate with tools like Apache Atlas and Talend highlights its versatility.
What are the key features of Apache Hadoop?Industries leverage Apache Hadoop for Big Data analytics, data lakes, ETL tasks, and enterprise data hubs, handling unstructured and structured data from IoT, RDBMS, and real-time streams. Its applications extend to data warehousing, AI/ML projects, and data migration, employing tools like Apache Ranger, Hive, and Talend for effective data management and analysis.
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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