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Apache Hadoop vs BigQuery comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

ROI

Sentiment score
6.5
Apache Hadoop provides cost-effective data storage and processing, though ROI varies based on analytics use and sophistication.
Sentiment score
7.4
Organizations experienced improved performance and cost savings after adopting BigQuery, achieving a 75% cost reduction and efficient data management.
 

Customer Service

Sentiment score
6.4
Customer service varies by Hadoop distributor, with Hortonworks rated highly; support depends on vendor, community resources, or external vendors.
Sentiment score
7.2
Customers generally find BigQuery support helpful, but integration challenges and resource availability need improvement despite positive responsiveness.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
rating the customer support at ten points out of ten
I have been self-taught and I have been able to handle all my problems alone.
 

Scalability Issues

Sentiment score
7.6
Apache Hadoop excels in scalability, allowing easy cluster expansion and efficient data handling, ideal for varied organizational needs.
Sentiment score
8.0
BigQuery offers impressive scalability and efficiency for large data, but may be costly and present integration challenges for smaller users.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
It is a 10 out of 10 in terms of scalability.
The scalability is definitely good because we are migrating to the cloud since the computers on the premises or the big database we need are no longer enough.
 

Stability Issues

Sentiment score
7.3
Apache Hadoop's stability, rated 8/10, improves with newer versions, though minor issues exist with memory and data processing.
Sentiment score
8.5
BigQuery is highly stable and reliable for cloud data analytics, efficiently handling large volumes with minor issues.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
 

Room For Improvement

Apache Hadoop needs improved usability, integration, security, support, and performance for efficient high-volume data processing and better community resources.
BigQuery users face challenges with migration, integration, cost, scaling, user interfaces, and call for better machine learning capabilities.
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it.
BigQuery is already integrating Gemini AI into the data extraction process directly in order to reduce costs.
In general, if I know SQL and start playing around, it will start making sense.
Troubleshooting requires opening each pipeline individually, which is time-consuming.
 

Setup Cost

Enterprise Hadoop offers cost benefits but varies with deployment type and distribution, impacting smaller organizations more heavily.
BigQuery offers flexible, pay-as-you-go pricing based on data usage, with low storage costs and adaptable enterprise plans.
Being able to optimize the queries to data is critical. Otherwise, you could spend a fortune.
The price is perceived as expensive, rated at eight out of ten in terms of costliness.
 

Valuable Features

Apache Hadoop excels with a scalable, cost-effective system handling diverse data types, integrating with tools, and supporting big data analytics.
BigQuery excels in scalability, performance, cost-efficiency, and integration with Google products, making it ideal for complex data analyses.
Hadoop is a distributed file system, and it scales reasonably well provided you give it sufficient resources.
It is really fast because it can process millions of rows in just a matter of one or two seconds.
The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data.
BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes.
 

Categories and Ranking

Apache Hadoop
Average Rating
7.8
Reviews Sentiment
6.7
Number of Reviews
40
Ranking in other categories
Data Warehouse (7th)
BigQuery
Average Rating
8.2
Reviews Sentiment
7.2
Number of Reviews
41
Ranking in other categories
Cloud Data Warehouse (3rd)
 

Featured Reviews

Sushil Arya - PeerSpot reviewer
Provides ease of integration with the IT workflow of a business
When working with Kafka, I saw that the data came in an incremental order. The incremental data processing part is still not very effective in Apache Hadoop. If the data is already there, it can be processed very effectively, especially if the data is coming in every second. If you want to know the location of some data every second, then such data is not processed effectively in Apache Hadoop. I can say that one of the features where improvements are required revolves around the licensing cost of the tool. If the tool can build some licensing structures in a pay-per-use manner, organizations can get the look and feel of Apache Hadoop. Apache Hadoop can offer a licensing structure of the product that can be seen as similar to how AWS operates. Apache Hadoop can look into the capability of processing incremental data. The tool's setup process can be a scope of improvement. Also, it is not very simple because while doing the setup, we need to do all the server settings, including port listing and firewall configurations. If we look at other products on the market, then they can be made simpler. There are certain shortcomings when it comes to the product's technical support part, making it an area where improvements are required. The time frame for the resolution is an area that needs to be improved. The overall communication part of the technical support team also needs improvement.
Luís Silva - PeerSpot reviewer
Handles large data sets efficiently and offers flexible data management capabilities
The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data. It is kind of difficult to explain, but structured data and the ability to handle large data sets are key features. The data integration capabilities in BigQuery were, in fact, an issue at the beginning. There are two types of integrations. As long as integration is within Google, it is pretty simple. When you start to try to connect external clients to that data, it becomes more complex. It is not related to BigQuery, it is related to Google security model, which is not easy to manage. I would not call it an integration issue of BigQuery, I would call it an integration issue of Google security model.
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Top Industries

By visitors reading reviews
Financial Services Firm
34%
Computer Software Company
12%
University
5%
Energy/Utilities Company
5%
Computer Software Company
17%
Financial Services Firm
16%
Manufacturing Company
11%
Retailer
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Apache Hadoop?
It's primarily open source. You can handle huge data volumes and create your own views, workflows, and tables. I can also use it for real-time data streaming.
What is your experience regarding pricing and costs for Apache Hadoop?
The product is open-source, but some associated licensing fees depend on the subscription level. While it might be free for students, organizations typically need to pay for their subscriptions. Th...
What needs improvement with Apache Hadoop?
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it. This wa...
What do you like most about BigQuery?
The initial setup process is easy.
What is your experience regarding pricing and costs for BigQuery?
I believe the cost of BigQuery is competitive versus the alternatives in the market, but it can become expensive if the tool is not used properly. It is on a per-consumption basis, the billing, so ...
What needs improvement with BigQuery?
I have not used BigQuery for AI and machine learning projects myself. I know how to use it, and I can see where it would be useful, but so far, in my projects, I have not included a BigQuery compon...
 

Comparisons

 

Overview

 

Sample Customers

Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web Lab
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Find out what your peers are saying about Apache Hadoop vs. BigQuery and other solutions. Updated: June 2025.
856,873 professionals have used our research since 2012.