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Apache Hadoop vs Databricks 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
6.5
Databricks enhances efficiency and ROI, offering scalable solutions and cost savings over traditional Hadoop with easy setup.
When it comes to big data processing, I prefer Databricks over other solutions.
For a lot of different tasks, including machine learning, it is a nice solution.
 

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
Databricks' support is generally praised for responsiveness, though some note delays, with resources often sufficient for independent problem-solving.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
As of now, we are raising issues and they are providing solutions without any problems.
Whenever we reach out, they respond promptly.
I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.
 

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
7.5
Databricks is praised for its adaptability, scalability, automation features, and performance across industries but needs improved autoscaling control.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
Databricks is an easily scalable platform.
I would rate the scalability of this solution as very high, about nine out of ten.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
 

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
7.7
Databricks is highly rated for stability and performance, with occasional minor issues often due to user or external factors.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
They release patches that sometimes break our code.
Databricks is definitely a very stable product and reliable.
 

Room For Improvement

Apache Hadoop needs improved usability, integration, security, support, and performance for efficient high-volume data processing and better community resources.
Databricks should improve visualization, integration, user experience, and scalability, addressing concerns about pricing, error messages, and onboarding.
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.
We could use their job clusters, however, that increases costs, which is challenging for us as a startup.
This feature, if made publicly available, may act as a game-changer, considering many global organizations use SAP data for their ERP requirements.
If I could right-click to copy absolute paths or to read files directly into a data frame, it would standardize and simplify the process.
 

Setup Cost

Enterprise Hadoop offers cost benefits but varies with deployment type and distribution, impacting smaller organizations more heavily.
Databricks offers flexible, often expensive pricing, mitigated by cloud deployment and tiered licensing, with varied user cost experiences.
 

Valuable Features

Apache Hadoop excels with a scalable, cost-effective system handling diverse data types, integrating with tools, and supporting big data analytics.
Databricks offers an intuitive interface for data processing, integrating SQL, Python, and features like Delta Lake and MLflow.
Hadoop is a distributed file system, and it scales reasonably well provided you give it sufficient resources.
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
Databricks' capability to process data in parallel enhances data processing speed.
 

Categories and Ranking

Apache Hadoop
Average Rating
7.8
Reviews Sentiment
6.7
Number of Reviews
40
Ranking in other categories
Data Warehouse (7th)
Databricks
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
91
Ranking in other categories
Cloud Data Warehouse (8th), Data Science Platforms (1st), Streaming Analytics (1st)
 

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.
ShubhamSharma7 - PeerSpot reviewer
Capability to integrate diverse coding languages in a single notebook greatly enhances workflow
Databricks offers various courses that I can use, whether it's PySpark, Scala, or R. I can leverage all these courses in a single notebook, which is beneficial for clients as they can access various tools in one place whenever needed. This is quite significant. I usually work with PySpark based on client requirements. After coding, I feed the Databricks notebooks into the ADF pipeline for updates. Databricks' capability to process data in parallel enhances data processing speed. Furthermore, I can connect our Databricks notebook directly with Power BI and other visualization tools like Qlik. Once we develop code, it allows us to transform raw data into visualizations for clients using analysis diagrams, which is very helpful.
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Top Industries

By visitors reading reviews
Financial Services Firm
34%
Computer Software Company
12%
University
5%
Energy/Utilities Company
5%
Financial Services Firm
18%
Computer Software Company
10%
Manufacturing Company
9%
Healthcare Company
6%
 

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...
Which do you prefer - Databricks or Azure Machine Learning Studio?
Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...
How would you compare Databricks vs Amazon SageMaker?
We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...
Which would you choose - Databricks or Azure Stream Analytics?
Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...
 

Comparisons

 

Also Known As

No data available
Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
 

Overview

 

Sample Customers

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