Microsoft Parallel Data Warehouse and Apache Hadoop compete in the data warehousing and big data analytics space. Microsoft seems to have the upper hand in performance and ease of use, while Hadoop excels in scalability and flexibility.
Features: Microsoft Parallel Data Warehouse offers fast data loading, robust integration with Microsoft products, and strong querying capabilities for large data volumes. Apache Hadoop features distributed processing, ability to manage diverse data types, and seamless integration with tools like Spark, making it ideal for big data environments.
Room for Improvement: Microsoft Parallel Data Warehouse could improve its compatibility with non-Microsoft tools, enhance scalability, and support other operating systems. Apache Hadoop needs improvement in user-friendliness, reduction in query latency, and better integration features. Both solutions have room for stronger security measures.
Ease of Deployment and Customer Service: Microsoft Parallel Data Warehouse provides flexibility for deployment across cloud environments and offers reliable technical support. Apache Hadoop, while flexible, primarily operates on-premises and could enhance its deployment process, often requiring community support which may not be as prompt as vendor support.
Pricing and ROI: Microsoft Parallel Data Warehouse is considered costly but offers significant ROI, particularly with Azure integration. Its pricing is complicated by licensing variations. Apache Hadoop is generally more cost-effective due to its open-source nature, though licensed distributions can be expensive. Both solutions provide substantial returns by efficiently managing large data volumes.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
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.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
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.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
Microsoft Parallel Data Warehouse is stable for us because it is built on SQL Server.
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.
Addressing the cost would be the number one area for improvement.
It would be better to release patches less frequently, maybe once a month or once every two months.
When there are many users or many expensive queries, it can be very slow.
Microsoft Parallel Data Warehouse is very 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.
The columnstore index enhances data query performance by using less space and achieving faster performance than general indexing.
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.
Its scalability is impressive as it scales up and down really well.
| Product | Mindshare (%) |
|---|---|
| Apache Hadoop | 3.5% |
| Microsoft Parallel Data Warehouse | 2.9% |
| Other | 93.6% |
| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 8 |
| Large Enterprise | 21 |
| Company Size | Count |
|---|---|
| Small Business | 16 |
| Midsize Enterprise | 6 |
| Large Enterprise | 22 |
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.
Microsoft Parallel Data Warehouse offers high performance and usability with seamless SQL Server integration, handling large data efficiently with a user-friendly interface. Known for its cost-effectiveness and robust security, it excels in integrating data across Microsoft ecosystem.
Microsoft Parallel Data Warehouse efficiently manages large datasets from diverse sources, supporting a unified data approach. Its integration with SQL Server and compatibility with tools like Qlik enhances data management and decision-making capabilities. With impressive scalability and security features, it is widely used in sectors such as finance, healthcare, and logistics for analytics and reporting. However, users seek improvements in integration with non-Microsoft layers, memory usage, SQL configuration, and scalability.
What are the key features of Microsoft Parallel Data Warehouse?In industries like finance, healthcare, and logistics, Microsoft Parallel Data Warehouse supports analytics, reporting, and decision-making processes. Organizations utilize it to maintain historical data, develop business intelligence models, and create actionable dashboards, benefiting from its integration with key tools and efficient data management.
We monitor all Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
