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Apache Hadoop pros and cons

Vendor: Apache
4.0 out of 5

Pros & Cons summary

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Prominent pros & cons

PROS

Apache Hadoop effectively centralizes data management and processing, significantly reducing maintenance and development time.
Its scalability and elastic nature allow for on-demand expansion and contraction, making it ideal for Proof of Concept projects.
The system handles vast data volumes with ease, providing powerful ingestion tools and seamless integration with a range of technologies.
Apache Hadoop's open-source nature offers cost-effectiveness, allowing users to avoid reliance on third-party vendors.
The platform's resilience and fault tolerance ensure uninterrupted operation, even in cases of hardware failure.

CONS

Its inability to handle queries with insufficient memory can be bypassed by processing data in chunks.
Real-time data processing is weak, which contributes to its difficulty in implementation and operation.
Apache Hadoop lacks robust technical support and extensive community resources for implementing new features or addressing technical issues.
The integration of Apache Hadoop with different business processes poses a challenge due to the need for significant technical expertise and configuration efforts.
Apache Hadoop's security features require enhancement to effectively manage large volumes of data.
 

Apache Hadoop Pros review quotes

it_user265830 - PeerSpot reviewer
Senior Hadoop Engineer with 1,001-5,000 employees
Dec 8, 2015
With the increase in data size for the business, this horizontal scalable appliance has answered every business question in terms of storage and processing.
it_user340983 - PeerSpot reviewer
Infrastructure Engineer at Zirous, Inc.
May 8, 2016
The Distributed File System, which is the base of Hadoop, has been the most valuable feature with its ability to store video, pictures, JSON, XML, and plain text all in the same file system.
it_user693231 - PeerSpot reviewer
Big Data Engineer at a tech vendor with 5,001-10,000 employees
Jun 28, 2017
After switching to big data pipelines, our query performance improved a hundred times.
Learn what your peers think about Apache Hadoop. Get advice and tips from experienced pros sharing their opinions. Updated: May 2026.
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RC
Senior Associate at a financial services firm with 10,001+ employees
Sep 4, 2017
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.
it_user576504 - PeerSpot reviewer
Software Architect at a tech services company with 10,001+ employees
Mar 6, 2018
High throughput and low latency. We start with data mashing on Hive and finally use this for KPI visualization.
Mar 6, 2018
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.
CB
Database/Middleware Consultant (Currently at U.S. Department of Labor) at a tech services company with 51-200 employees
Mar 13, 2018
​​Data ingestion: It has rapid speed, if Apache Accumulo is used.
SF
Analytics Platform Manager at a consultancy with 10,001+ employees
Aug 14, 2018
Two valuable features are its scalability and parallel processing. There are jobs that cannot be done unless you have massively parallel processing.
Jul 16, 2019
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.
reviewer1040328 - PeerSpot reviewer
IT Expert at a tech services company with 1,001-5,000 employees
Jul 28, 2019
The best thing about this solution is that it is very powerful and very cheap.
 

Apache Hadoop Cons review quotes

it_user265830 - PeerSpot reviewer
Senior Hadoop Engineer with 1,001-5,000 employees
Dec 8, 2015
Currently, Apache Oozie 4.0.1 has some compatibility issues with Hadoop 2.5.2.
it_user340983 - PeerSpot reviewer
Infrastructure Engineer at Zirous, Inc.
May 8, 2016
Hadoop in and of itself stores data with 3x redundancy and our organization has come to the conclusion that the default 3x results in too much wasted disk space.
it_user693231 - PeerSpot reviewer
Big Data Engineer at a tech vendor with 5,001-10,000 employees
Jun 28, 2017
Once we had an issue with stability, due to a complete shutdown of a cluster.
Learn what your peers think about Apache Hadoop. Get advice and tips from experienced pros sharing their opinions. Updated: May 2026.
892,776 professionals have used our research since 2012.
RC
Senior Associate at a financial services firm with 10,001+ employees
Sep 4, 2017
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.
it_user576504 - PeerSpot reviewer
Software Architect at a tech services company with 10,001+ employees
Mar 6, 2018
I rate it an eight out of 10. It's huge, complex, slow.
Mar 6, 2018
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.
CB
Database/Middleware Consultant (Currently at U.S. Department of Labor) at a tech services company with 51-200 employees
Mar 13, 2018
It needs better user interface (UI) functionalities.
SF
Analytics Platform Manager at a consultancy with 10,001+ employees
Aug 14, 2018
I would like to see more direct integration of visualization applications.
Jul 16, 2019
We would like to have more dynamics in merging this machine data with other internal data to make more meaning out of it.
reviewer1040328 - PeerSpot reviewer
IT Expert at a tech services company with 1,001-5,000 employees
Jul 28, 2019
The upgrade path should be improved because it is not as easy as it should be.