Apache Hadoop vs Aster Data Map Reduce comparison

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2,467 views|2,109 comparisons
87% willing to recommend
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122 views|98 comparisons
100% willing to recommend
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Executive Summary

We performed a comparison between Apache Hadoop and Aster Data Map Reduce based on real PeerSpot user reviews.

Find out in this report how the two Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
To learn more, read our detailed Apache Hadoop vs. Aster Data Map Reduce Report (Updated: May 2024).
770,428 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"We selected Apache Hadoop because it is not dependent on third-party vendors.""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.""It's good for storing historical data and handling analytics on a huge amount of data.""The most valuable features are powerful tools for ingestion, as data is in multiple systems.""Its integration is Hadoop's best feature because that allows us to support different tools in a big data platform.""Two valuable features are its scalability and parallel processing. There are jobs that cannot be done unless you have massively parallel processing.""Hadoop is extensible — it's elastic.""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 most valuable feature is the ease of uploading data from multiple sources.""It's stable and reliable.""The ease of deployment is useful so clients are up and running quickly in comparison to other products."

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Cons
"Hadoop's security could be better.""I think more of the solution needs to be focused around the panel processing and retrieval of data.""It would be helpful to have more information on how to best apply this solution to smaller organizations, with less data, and grow the data lake.""The solution needs a better tutorial. There are only documents available currently. There's a lot of YouTube videos available. However, in terms of learning, we didn't have great success trying to learn that way. There needs to be better self-paced learning.""In certain cases, the configurations for dealing with data skewness do not make any sense.""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.""I would like to see more direct integration of visualization applications.""I mentioned it definitely, and this is probably the only feature we can improve a little bit because the terminal and coding screen on Hadoop is a little outdated, and it looks like the old C++ bio screen. If the UI and UX can be improved slightly, I believe it will go a long way toward increasing adoption and effectiveness."

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"It is hard for some of our users to set up rules for cleansing and transforming data, so this is something that could be improved.""From my perspective, it would be good if they gave better ITIN/R plugins to use the data for AI modeling, or data science modeling. We can do it now; however, it could be more elegant in terms of interfacing.""There are some ways that the handling of unstructured data could be improved."

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Pricing and Cost Advice
  • "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."
  • "​There are no licensing costs involved, hence money is saved on the software infrastructure​."
  • "This is a low cost and powerful solution."
  • "The price of Apache Hadoop could be less expensive."
  • "If my company can use the cloud version of Apache Hadoop, particularly the cloud storage feature, it would be easier and would cost less because an on-premises deployment has a higher cost during storage, for example, though I don't know exactly how much Apache Hadoop costs."
  • "We don't directly pay for it. Our clients pay for it, and they usually don't complain about the price. So, it is probably acceptable."
  • "The price could be better. Hortonworks no longer exists, and Cloudera killed the free version of Hadoop."
  • "We just use the free version."
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  • "The product cost is high for what the client gets. There may be more cost-effective solutions for small and medium-sized organizations."
  • More Aster Data Map Reduce Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:Tools like Apache Hadoop are knowledge-intensive in nature. Unlike other tools in the market currently, we cannot understand knowledge-intensive products straight away. To use Apache Hadoop, a person… more »
    Top Answer:It's moderately priced. It's not cheap. I'd rate it 2.5 out of five in terms of affordability.
    Top Answer:Some of our clients are looking for on-premise installations as well. Although we don't have any, some of our prospects are also asking, and we are not sure if that part is easily doable or is as… more »
    Ranking
    5th
    out of 35 in Data Warehouse
    Views
    2,467
    Comparisons
    2,109
    Reviews
    11
    Average Words per Review
    573
    Rating
    7.9
    19th
    out of 35 in Data Warehouse
    Views
    122
    Comparisons
    98
    Reviews
    1
    Average Words per Review
    525
    Rating
    7.0
    Comparisons
    Learn More
    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.

    SQL-MapReduce is a framework created by Teradata Aster to allow developers to write powerful and highly expressive SQL-MapReduce functions in languages such as Java, C#, Python, C++, and R and push them into the discovery platform for high performance analytics. Analysts can then invoke SQL-MapReduce functions using standard SQL or R through Aster Database, the first discovery platform that allows applications to be fully embedded within the database engine to enable ultra-fast, deep analysis of massive data sets.

    Sample Customers
    Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web Lab
    Volvo, eBay, P&G, Verizon, 7Eleven, ABN Amro, Alior Bank, BBVA, Cabela's, Dell, DHL, Gortz, Homebase, IHG
    Top Industries
    REVIEWERS
    Financial Services Firm38%
    Comms Service Provider25%
    Hospitality Company6%
    Consumer Goods Company6%
    VISITORS READING REVIEWS
    Financial Services Firm29%
    Computer Software Company10%
    University6%
    Comms Service Provider6%
    No Data Available
    Company Size
    REVIEWERS
    Small Business34%
    Midsize Enterprise20%
    Large Enterprise46%
    VISITORS READING REVIEWS
    Small Business14%
    Midsize Enterprise11%
    Large Enterprise74%
    No Data Available
    Buyer's Guide
    Apache Hadoop vs. Aster Data Map Reduce
    May 2024
    Find out what your peers are saying about Apache Hadoop vs. Aster Data Map Reduce and other solutions. Updated: May 2024.
    770,428 professionals have used our research since 2012.

    Apache Hadoop is ranked 5th in Data Warehouse with 33 reviews while Aster Data Map Reduce is ranked 19th in Data Warehouse with 3 reviews. Apache Hadoop is rated 7.8, while Aster Data Map Reduce is rated 7.4. The top reviewer of Apache Hadoop writes "Handles huge data volumes and create your own workflows and tables but you need to have deeper knowledge". On the other hand, the top reviewer of Aster Data Map Reduce writes "Has good base product functionality of data storage and analytics but there should be an option to use it on the cloud ". Apache Hadoop is most compared with Azure Data Factory, Microsoft Azure Synapse Analytics, Oracle Exadata, Snowflake and Teradata, whereas Aster Data Map Reduce is most compared with . See our Apache Hadoop vs. Aster Data Map Reduce report.

    See our list of best Data Warehouse vendors.

    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.