Amazon EMR vs Argyle Data

Amazon EMR is ranked 5th in Hadoop with 1 review vs Argyle Data which is ranked 20th in Hadoop. The top reviewer of Amazon EMR writes "Ability to easily and quickly resize the cluster is what really makes it stand out". Amazon EMR is most compared with Cloudera Distribution for Hadoop, Hortonworks and IBM InfoSphere BigInsights. Argyle Data is most compared with HStreaming Enterprise [EOL], Outerthought Lily and Pepperdata.
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Quotes From Members Comparing Amazon EMR vs Argyle Data

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Ranking
RANKING
Views
3,284
Comparisons
2,850
Reviews
1
Followers
431
Avg. Rating
9.0
Views
255
Comparisons
199
Reviews
0
Followers
127
Avg. Rating
N/A
Top Comparisons
Top Comparisons
Hortonworks
Compared 33% of the time.
See more Amazon EMR competitors »
Outerthought
Compared 13% of the time.
Pgr4rvsx 400x400
Compared 8% of the time.
See more Argyle Data competitors »
Also Known As
Also Known AsAmazon Elastic MapReduce
Website/Video
Website/VideoAmazon
  • Vendor 6834 screenshot 1520701462
Argyle Data
  • Vendor 29105 screenshot 1519512354
OverviewQuestionmark icon
OverviewAmazon Elastic MapReduce (Amazon EMR) is a web service that makes it easy to quickly and cost-effectively process vast amounts of data. Amazon EMR simplifies big data processing, providing a managed Hadoop framework that makes it easy, fast, and cost-effective for you to distribute and process vast amounts of your data across dynamically scalable Amazon EC2 instances.

Argyle Data has had the privilege of working with global leaders and visionaries on their strategies for revenue threat analytics, big data, and machine learning. What consistently comes up is that best-in-class carriers know the revenue threats that they have been attacked with in the past. What they don’t know is how to prepare for future attacks that will likely incorporate new types and methods of revenue threats.

What is critical to understand is that a) criminals are continually innovating; b) each subscriber will have many devices, many channels, and many potential attack points; and c) we need a better way to detect new fraud and protect customers and carriers in this new world – today in 2015, not in 2020.

This requires an effective strategy for the use of big data and machine learning in the areas of:

Fraud Threats

Analytics apps for identifying threats from various types of domestic fraud and roaming fraud

Profit Threats

Analytics apps for identifying threats from arbitrage, negative margin, high usage, and bill shock

SLA Threats

Analytics apps for identifying threats from network vulnerabilities and from roaming partners not meeting their SLA windows

Forensic Threats

Graph analysis application for analyzing 1st to 5th degrees of separation between data assets



Sample Customers
Sample CustomersYelpCloudera, Gigamon, Hortonworks
Top IndustriesQuestionmark icon
Top Industries
VISITORS READING REVIEWS
Financial Services Firm
24%
Media Company
11%
Pharma/Biotech Company
10%
Healthcare Company
8%
No Data Available

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We monitor all Hadoop 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.

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