Compare Apache Hadoop vs. Microsoft Analytics Platform System
Apache Hadoop is ranked 4th in Data Warehouse with 6 reviews while Microsoft Analytics Platform System is ranked 16th in Data Warehouse with 1 review. Apache Hadoop is rated 7.6, while Microsoft Analytics Platform System is rated 0. The top reviewer of Apache Hadoop writes "We are able to ingest huge volumes/varieties of data, but it needs a data visualization tool and enhanced Ambari for management". On the other hand, the top reviewer of Microsoft Analytics Platform System writes "Helps our customers to discover trends, which provides useful information based on their business". Apache Hadoop is most compared with Snowflake, Pivotal Greenplum and Oracle Exadata, whereas Microsoft Analytics Platform System is most compared with Apache Hadoop, Teradata and Oracle Exadata.
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Most Helpful Review | |||
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Quotes From Members
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
Pros | |
The best thing about this solution is that it is very powerful and very cheap. The most valuable features are the ability to process the machine data at a high speed, and to add structure to our data so that we can generate relevant analytics. Two valuable features are its scalability and parallel processing. There are jobs that cannot be done unless you have massively parallel processing. Initially, with RDBMS alone, we had a lot of work and few servers running on-premise and on cloud for the PoC and incubation. With the use of Hadoop and ecosystem components and tools, and managing it in Amazon EC2, we have created a Big Data "lab" which helps us to centralize all our work and solutions into a single repository. This has cut down the time in terms of maintenance, development and, especially, data processing challenges. Since both Apache Hadoop and Amazon EC2 are elastic in nature, we can scale and expand on demand for a specific PoC, and scale down when it's done. Most valuable features are HDFS and Kafka: Ingestion of huge volumes and variety of unstructured/semi-structured data is feasible, and it helps us to quickly onboard a new Big Data analytics prospect. High throughput and low latency. We start with data mashing on Hive and finally use this for KPI visualization. Data ingestion: It has rapid speed, if Apache Accumulo is used. | It is closely integrated with other products in the MS portfolio. Helps our customers to discover trends, which provides useful information based on their business. |
Cons | |
The upgrade path should be improved because it is not as easy as it should be. We would like to have more dynamics in merging this machine data with other internal data to make more meaning out of it. I would like to see more direct integration of visualization applications. Based on our needs, we would like to see a tool for data visualization and enhanced Ambari for management, plus a pre-built IoT hub/model. These would reduce our efforts and the time needed to prove to a customer that this will help them. General installation/dependency issues were there, but were not a major, complex issue. While migrating data from MySQL to Hive, things are a little challenging, but we were able to get through that with support from forums and a little trial and error. It needs better user interface (UI) functionalities. | Hybrid environments are complex to manage. |
Pricing and Cost Advice | |
This is a low cost and powerful solution. There are no licensing costs involved, hence money is saved on the software infrastructure. | Information Not Available |
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Ranking  | |
Views 11,781 Comparisons 10,295 Reviews 7 Average Words per Review 440 Avg. Rating 7.6 | Views 871 Comparisons 489 Reviews 0 Average Words per Review 179 Avg. Rating N/A |
Top Comparisons | |
 Compared 31% of the time.  Compared 31% of the time.  Compared 13% of the time. |  Compared 21% of the time.  Compared 16% of the time. Compared 15% of the time. |
Also Known As | |
| Microsoft APS | |
Learn | |
| Apache | Microsoft |
Overview | |
| The Apache Hadoop project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. | The Microsoft Analytics Platform System can meet the demands of your evolving data warehouse environment with its scale-out, massively parallel processing integrated system supporting hybrid data warehouse scenarios. It provides the ability to query across relational and non-relational data by leveraging Microsoft PolyBase and industry-leading big data technologies. It offers the lowest price per terabyte for large data warehouse workloads. |
Offer | |
Learn more about Apache Hadoop | Learn more about Microsoft Analytics Platform System |
Sample Customers | |
| Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web Lab | Transport for London, E-Plus Mobilfunk GmbH & Co. KG, Prometeia, Tangerine, SSM Health Care, Service Corporation International |
Top Industries | |
Financial Services Firm24% Software R&D Company24% Comms Service Provider11% Government9% | No Data Available |
Find out what your peers are saying about Oracle, Teradata, Micro Focus and others in Data Warehouse. Updated: September 2019.
366,756 professionals have used our research since 2012.
See also Apache Hadoop Reviews, Microsoft Analytics Platform System Reviews, and our list of Best Data Warehouse Companies.
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