Compare Apache Hadoop vs. Teradata IntelliFlex

Apache Hadoop is ranked 5th in Data Warehouse with 7 reviews while Teradata IntelliFlex is ranked 11th in Data Warehouse with 2 reviews. Apache Hadoop is rated 7.6, while Teradata IntelliFlex is rated 9.6. 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 Teradata IntelliFlex writes "User-friendly and has good parallel processing ". Apache Hadoop is most compared with Snowflake, Pivotal Greenplum and Oracle Exadata, whereas Teradata IntelliFlex is most compared with Teradata, Oracle Exadata and Microsoft Azure SQL Data Warehouse. See our Apache Hadoop vs. Teradata IntelliFlex report.
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Most Helpful Review
Find out what your peers are saying about Apache Hadoop vs. Teradata IntelliFlex and other solutions. Updated: November 2019.
378,397 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
What comes with the standard setup is what we mostly use, but Ambari is the most important.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.

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It is very stable. It's 100% uptime. Speed and resilience are one of the greatest features of this product. In almost twenty years we've never had downtime, except for outages for patches and upgrades. We've never had a system failure in twenty years.Intelliflex is very scalable. In fact, scalability has improved 100 times by Intelliflex, in my personal opinion.

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Cons
In the next release, I would like to see Hive more responsive for smaller queries and to reduce the latency.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.

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They should add more connectors to different platforms.The reporting side wasn't very good in the past, but with the latest versions, it's getting better. Still, the friendliness of the PDC reporting and functionality needs to be improved.

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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​.

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Ranking
5th
out of 30 in Data Warehouse
Views
12,237
Comparisons
10,615
Reviews
7
Average Words per Review
478
Avg. Rating
7.6
11th
out of 30 in Data Warehouse
Views
864
Comparisons
598
Reviews
2
Average Words per Review
473
Avg. Rating
9.5
Top Comparisons
Compared 32% of the time.
Compared 29% of the time.
Compared 13% of the time.
Compared 72% of the time.
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Apache
Teradata
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.

Teradata IntelliFlex delivers real-time intelligence to place vital data into the hands of front-line decision makers, while extending traditional data warehouse functionality into the realm of tactical decision making. With IntelliFlex, you can combine both these strategic and operational workloads in a single data warehouse.

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Sample Customers
Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web Lab
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Top Industries
VISITORS READING REVIEWS
Software R&D Company34%
Financial Services Firm17%
Comms Service Provider10%
Media Company6%
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Find out what your peers are saying about Apache Hadoop vs. Teradata IntelliFlex and other solutions. Updated: November 2019.
378,397 professionals have used our research since 2012.
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.
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