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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
5.4
Apache Hadoop offers cost-effective storage and processing, benefiting some with analytics and optimizing data applications for resource savings.
Sentiment score
5.8
BigQuery provides 27% better query performance and 17% cost reduction, offering seamless integration and efficient data management with training.
 

Customer Service

Sentiment score
6.1
Customer service for Apache Hadoop varies, with differing satisfaction levels and reliance on external resources and forums for support.
Sentiment score
7.2
Google's support is rated 8/10 for responsiveness, but challenges exist in accessibility, integration, and documentation resources.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
I have been self-taught and I have been able to handle all my problems alone.
rating the customer support at ten points out of ten
I would rate their customer service pretty good on a scale of one to 10, as they gave me access to the platform on a grant.
 

Scalability Issues

Sentiment score
7.4
Apache Hadoop is valued for its scalability, supporting large data and users effectively, especially in cloud environments.
Sentiment score
7.8
BigQuery excels in scalability and resource management, supporting large data with seamless auto-scaling despite some processing limitations.
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
7.1
Apache Hadoop is stable and reliable in multi-node clusters, performing well with minimal instability during high-load operations.
Sentiment score
8.3
BigQuery is a stable, efficient cloud analytics tool, with minor scalability challenges and reliable Google support for large-scale data handling.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
 

Room For Improvement

Apache Hadoop needs user-friendly enhancements, better integration, improved security, streamlined setup, and modernized features and support.
BigQuery struggles with special character restrictions, high costs, complex optimization, and lacks user-friendly integration and performance flexibility.
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.
Troubleshooting requires opening each pipeline individually, which is time-consuming.
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.
 

Setup Cost

Enterprise Apache Hadoop pricing varies greatly, influenced by distribution choice, deployment type, and specific usage requirements.
BigQuery provides flexible pricing with pay-as-you-go models, $300 credits, and competitive costs requiring mindful usage to control expenses.
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 offers scalable, cost-effective data processing, supporting diverse environments with fault tolerance, integration, and analytics tools like Hive.
BigQuery excels in scalability, speed, and usability with serverless architecture, SQL support, and cost-effective data processing capabilities.
Hadoop is a distributed file system, and it scales reasonably well provided you give it sufficient resources.
I assess Apache Hadoop's fault tolerance during hardware failures positively since we have hardware failover, which works without problems.
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
8.0
Reviews Sentiment
6.6
Number of Reviews
41
Ranking in other categories
Data Warehouse (6th)
BigQuery
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
42
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
9%
University
6%
Government
5%
Computer Software Company
15%
Financial Services Firm
14%
Manufacturing Company
12%
Retailer
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business14
Midsize Enterprise8
Large Enterprise21
By reviewers
Company SizeCount
Small Business12
Midsize Enterprise9
Large Enterprise20
 

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...
 

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: September 2025.
872,706 professionals have used our research since 2012.