Apache Hadoop vs Aster Data Map Reduce comparison

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Executive Summary

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

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To learn more, read our detailed Data Warehouse Report (Updated: March 2024).
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Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The best thing about this solution is that it is very powerful and very cheap.""Hadoop is extensible — it's elastic.""What comes with the standard setup is what we mostly use, but Ambari is the most important.""The most valuable feature is the database.""The scalability of Apache Hadoop is very good.""Its integration is Hadoop's best feature because that allows us to support different tools in a big data platform.""It's good for storing historical data and handling analytics on a huge amount of data.""High throughput and low latency. We start with data mashing on Hive and finally use this for KPI visualization."

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"The most valuable feature is the ease of uploading data from multiple sources.""The ease of deployment is useful so clients are up and running quickly in comparison to other products.""It's stable and reliable."

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Cons
"There is a lack of virtualization and presentation layers, so you can't take it and implement it like a radio solution.""In certain cases, the configurations for dealing with data skewness do not make any sense.""I would like to see more direct integration of visualization applications.""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.""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.""The main thing is the lack of community support. If you want to implement a new API or create a new file system, you won't find easy support.""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.""From the Apache perspective or the open-source community, they need to add more capabilities to make life easier from a configuration and deployment perspective."

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"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.""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.""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:It's open-source, so it's very cost-effective.
    Top Answer:The main thing is the lack of community support. If you want to implement a new API or create a new file system, you won't find easy support. And then there's the server issue. You have to create and… 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 33 in Data Warehouse
    Views
    2,765
    Comparisons
    2,378
    Reviews
    10
    Average Words per Review
    539
    Rating
    8.0
    19th
    out of 33 in Data Warehouse
    Views
    137
    Comparisons
    109
    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 Firm40%
    Comms Service Provider27%
    Hospitality Company7%
    Consumer Goods Company7%
    VISITORS READING REVIEWS
    Financial Services Firm27%
    Computer Software Company10%
    Comms Service Provider6%
    Educational Organization6%
    No Data Available
    Company Size
    REVIEWERS
    Small Business33%
    Midsize Enterprise24%
    Large Enterprise42%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise10%
    Large Enterprise75%
    No Data Available
    Buyer's Guide
    Data Warehouse
    March 2024
    Find out what your peers are saying about Snowflake Computing, Oracle, Teradata and others in Data Warehouse. Updated: March 2024.
    763,955 professionals have used our research since 2012.

    Apache Hadoop is ranked 5th in Data Warehouse with 11 reviews while Aster Data Map Reduce is ranked 19th in Data Warehouse with 1 review. Apache Hadoop is rated 7.8, while Aster Data Map Reduce is rated 7.4. The top reviewer of Apache Hadoop writes "Has good processing power and speed and is capable of handling large volumes of data and doing online analysis". On the other hand, the top reviewer of Aster Data Map Reduce writes "Easy to set up with fast data input but needs more documentation surrounding their on-premises offering". Apache Hadoop is most compared with Microsoft Azure Synapse Analytics, Azure Data Factory, Oracle Exadata, Snowflake and Teradata, whereas Aster Data Map Reduce is most compared with .

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