Apache Hadoop vs. SAP IQ

As of June 2019, Apache Hadoop is ranked 6th in Data Warehouse with 6 reviews vs SAP IQ which is ranked 9th in Data Warehouse with 6 reviews. 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". The top reviewer of SAP IQ writes "A column-based solution with the highest performance on query". Apache Hadoop is most compared with Pivotal Greenplum, Snowflake and Oracle Exadata. SAP IQ is most compared with SAP Adaptive Server Enterprise, Apache Hadoop and SQL Server. See our Apache Hadoop vs. SAP IQ report.
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Apache Hadoop Logo
12,309 views|9,053 comparisons
SAP IQ Logo
11,561 views|3,467 comparisons
Most Helpful Review
Find out what your peers are saying about Apache Hadoop vs. SAP IQ and other solutions. Updated: May 2019.
347,894 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
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.As compared to Hive on MapReduce, Impala on MPP returns results of SQL queries in a fairly short amount of time, and is relatively fast when reading data into other platforms like R.

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The column-based technologies (basically all the database for ITP) are used for SAP IQ. It is used as a column-based solution.It is very robust for ad hoc DW queries and its columnar compression is unique and valuable.Valuable features for us include the compression, speed, fast response time, and easy object maintenance.Columnar storage allows high compression, high load rates and high query performance.

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Cons
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.The key shortcoming is its inability to handle queries when there is insufficient memory. This limitation can be bypassed by processing the data in chunks.

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The room for improvement would be the marketing of the product, because this product is much better than advertised.Multiplex is very problematic. There are consistency problems in the metadata, meaning it is possible to lose metadata consistency. You should make sure you have healthy backups.I think the universe should be part of the Sybase IQ tool set.Concurrency and functional error messaging.

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Pricing and Cost Advice
​There are no licensing costs involved, hence money is saved on the software infrastructure​.Do take into consider that data storage and compute capacity scale differently and hence purchasing a "boxed" / 'all-in-one" solution (software and hardware) might not be the best idea.

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Negotiation is very important.Negotiate based on the features required.Evaluate all options; check to see if you need expensive add-ons.

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Ranking
6th
out of 30 in Data Warehouse
Views
12,309
Comparisons
9,053
Reviews
6
Average Words per Review
421
Avg. Rating
7.5
9th
out of 30 in Data Warehouse
Views
11,561
Comparisons
3,467
Reviews
6
Average Words per Review
346
Avg. Rating
7.8
Top Comparisons
Compared 33% of the time.
Compared 27% of the time.
Compared 15% of the time.
Compared 18% of the time.
Compared 13% of the time.
Also Known As
Sybase IQ
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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.
Find out what your peers are saying about Apache Hadoop vs. SAP IQ and other solutions. Updated: May 2019.
347,894 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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