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Apache Hadoop vs VMware Tanzu Greenplum comparison

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Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"Hadoop is designed to be scalable, so I don't think that it has limitations in regards to scalability.""What I like about Apache Hadoop is that it's for big data, in particular big data analysis, and it's the easier solution. I like the data processing feature for AI/ML use cases the most because some solutions allow me to collect data from relational databases, while Hadoop provides me with more options for newer technologies.""The scalability of Apache Hadoop is very good.""We selected Apache Hadoop because it is not dependent on third-party vendors.""Hadoop is extensible — it's elastic.""The performance is pretty good.""The most important feature is its ability to handle large volumes. Some of our customers have really large volumes, and it is capable of handling their data in terms of the core volume and daily incremental volume. So, its processing power and speed are most valuable."

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"The parallel load features mean that Greenplum is capable of high-volume data loading in parallel to all of the cluster segments, which is really valuable.""It's super easy to deploy and it also supports different languages and analytics.""A very good, open-source platform."

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Cons
"The integration with Apache Hadoop with lots of different techniques within your business can be a challenge.""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.""The solution is very expensive.""Real-time data processing is weak. This solution is very difficult to run and implement.""It requires a great deal of learning curve to understand. The overall Hadoop ecosystem has a large number of sub-products. There is ZooKeeper, and there are a whole lot of other things that are connected. In many cases, their functionalities are overlapping, and for a newcomer or our clients, it is very difficult to decide which of them to buy and which of them they don't really need. They require a consulting organization for it, which is good for organizations such as ours because that's what we do, but it is not easy for the end customers to gain so much knowledge and optimally use it.""What could be improved in Apache Hadoop is its user-friendliness. It's not that user-friendly, but maybe it's because I'm new to it. Sometimes it feels so tough to use, but it could be because of two aspects: one is my incompetency, for example, I don't know about all the features of Apache Hadoop, or maybe it's because of the limitations of the platform. For example, my team is maintaining the business glossary in Apache Atlas, but if you want to change any settings at the GUI level, an advanced level of coding or programming needs to be done in the back end, so it's not user-friendly.""Hadoop's security could be better."

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"Extra filters would be helpful.""The initial setup is somewhat complex and the out-of-the-box configuration requires optimization.""They should add more analytics. Their documentation could also be improved so that I don't have to bother my co-workers and tech support so often."

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Pricing and Cost Advice
  • "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."
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    Questions from the Community
    Top Answer:I don't think using Apache Spark without Hadoop has any major drawbacks or issues. I have used Apache Spark quite successfully with AWS S3 on many projects which are batch based. Yes for very high… more »
    Top Answer:The scalability of Apache Hadoop is very good.
    Top Answer:We're using the open-source version so there are no licensing fees.
    Top Answer:I'd like to see more support for structured data and features related to queries on NoSQL keys, extra filters would be helpful.
    Ranking
    6th
    out of 30 in Data Warehouse
    Views
    6,020
    Comparisons
    5,249
    Reviews
    6
    Average Words per Review
    437
    Rating
    7.5
    9th
    out of 30 in Data Warehouse
    Views
    4,337
    Comparisons
    3,096
    Reviews
    3
    Average Words per Review
    421
    Rating
    9.0
    Comparisons
    Also Known As
    Greenplum, Pivotal Greenplum
    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.

    Parallel Postgres for enterprise analytics at scale
    With improved transaction processing capability and support for streaming ingest, Greenplum can address workloads across a spectrum of analytic and operational contexts, from traditional business intelligence to deep learning.

    Offer
    Learn more about Apache Hadoop
    Learn more about VMware Tanzu Greenplum
    Sample Customers
    Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web Lab
    General Electric, Conversant, China CITIC Bank, Aridhia, Purdue University
    Top Industries
    REVIEWERS
    Financial Services Firm40%
    Comms Service Provider40%
    Consumer Goods Company10%
    Government10%
    VISITORS READING REVIEWS
    Computer Software Company24%
    Comms Service Provider17%
    Financial Services Firm15%
    Energy/Utilities Company5%
    REVIEWERS
    Financial Services Firm50%
    Marketing Services Firm15%
    Comms Service Provider15%
    Retailer10%
    VISITORS READING REVIEWS
    Computer Software Company29%
    Comms Service Provider19%
    Financial Services Firm12%
    Manufacturing Company7%
    Company Size
    REVIEWERS
    Small Business32%
    Midsize Enterprise28%
    Large Enterprise40%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise14%
    Large Enterprise71%
    REVIEWERS
    Small Business20%
    Midsize Enterprise17%
    Large Enterprise63%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise12%
    Large Enterprise73%
    Buyer's Guide
    Apache Hadoop vs. VMware Tanzu Greenplum
    July 2022
    Find out what your peers are saying about Apache Hadoop vs. VMware Tanzu Greenplum and other solutions. Updated: July 2022.
    619,967 professionals have used our research since 2012.

    Apache Hadoop is ranked 6th in Data Warehouse with 7 reviews while VMware Tanzu Greenplum is ranked 9th in Data Warehouse with 3 reviews. Apache Hadoop is rated 7.8, while VMware Tanzu Greenplum is rated 9.0. 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 VMware Tanzu Greenplum writes "Powerful external data integration and parallel load capabilities, with good technical support". Apache Hadoop is most compared with Microsoft Azure Synapse Analytics, Snowflake, Oracle Exadata, Azure Data Factory and Teradata, whereas VMware Tanzu Greenplum is most compared with Oracle Exadata, Snowflake, Oracle Database Appliance, Amazon Redshift and Teradata. See our Apache Hadoop vs. VMware Tanzu Greenplum report.

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    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.