Compare Apache Hadoop vs. SAP IQ

Apache Hadoop is ranked 4th in Data Warehouse with 7 reviews while SAP IQ is ranked 9th in Data Warehouse with 2 reviews. Apache Hadoop is rated 7.6, while SAP IQ is rated 7.0. 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 SAP IQ writes "An excellent column-based, enterprise relational database solution with good speed and compression". Apache Hadoop is most compared with Snowflake, Pivotal Greenplum and Oracle Exadata, whereas SAP IQ is most compared with SAP Adaptive Server Enterprise, Apache Hadoop and SAP HANA. See our Apache Hadoop vs. SAP IQ report.
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Apache Hadoop Logo
11,979 views|10,438 comparisons
SAP IQ Logo
4,024 views|2,922 comparisons
Most Helpful Review
Find out what your peers are saying about Apache Hadoop vs. SAP IQ and other solutions. Updated: September 2019.
371,062 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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Unbeatable speed and compression with a colummn-structured relational database.

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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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The organization who owns the product does not support it well and appears not to be doing significant development for the future.

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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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The only costs after standard licensing fees are for add-ons and upgrades.

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Ranking
4th
out of 30 in Data Warehouse
Views
11,979
Comparisons
10,438
Reviews
7
Average Words per Review
478
Avg. Rating
7.6
9th
out of 30 in Data Warehouse
Views
4,024
Comparisons
2,922
Reviews
2
Average Words per Review
732
Avg. Rating
7.0
Top Comparisons
Compared 31% of the time.
Compared 30% of the time.
Compared 13% of the time.
Compared 20% of the time.
Compared 16% of the time.
Also Known As
Sybase IQ
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Apache
SAP
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.

SAP® IQ software delivers speed and power for extreme-scale enterprise data warehousing and analytics. Its column-oriented, grid-based massively parallel processing (MPP) architecture and patented data compression and indexing technologies enable companies to exploit the value of huge amounts of data at the speed of business.

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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 LabASR Group, Citrix, State of Indiana, PocketCard Co. Ltd.
Top Industries
VISITORS READING REVIEWS
Software R&D Company28%
Financial Services Firm20%
Comms Service Provider12%
Government7%
REVIEWERS
Financial Services Firm67%
Marketing Services Firm11%
Insurance Company11%
Healthcare Company11%
Find out what your peers are saying about Apache Hadoop vs. SAP IQ and other solutions. Updated: September 2019.
371,062 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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