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Apache Hadoop vs Azure Data Factory 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 offers cost-effective storage and processing, with varying returns based on analytics sophistication and workload optimization.
Sentiment score
7.1
Azure Data Factory automates data processes, cuts costs, and improves efficiency, offering potential ROI of 20%-30% over five years.
 

Customer Service

Sentiment score
6.5
Apache Hadoop's support varies, with high satisfaction from vendor packages, responsive teams, and helpful documentation and community.
Sentiment score
6.5
Azure Data Factory support is responsive and helpful, though response times and efficiency vary based on issue complexity and support package.
The technical support is responsive and helpful
The technical support from Microsoft is rated an eight out of ten.
 

Scalability Issues

Sentiment score
7.6
Apache Hadoop offers scalable data management for large-scale deployments, efficiently supports diverse users and adapts across industries.
Sentiment score
7.5
Azure Data Factory is scalable and flexible for enterprises, though some concerns about costs and external integration exist.
Azure Data Factory is highly scalable.
 

Stability Issues

Sentiment score
7.4
Apache Hadoop is stable, especially newer versions, with occasional issues in setup, memory, and online data ingestion.
Sentiment score
7.8
Azure Data Factory is reliable, scoring 8-9/10, though some report occasional glitches or slowdowns under heavy loads.
The solution has a high level of stability, roughly a nine out of ten.
 

Room For Improvement

Apache Hadoop requires enhanced compatibility, improved usability, real-time processing, better security, modern interfaces, and cost-effective solutions to boost adoption.
Azure Data Factory needs better integration, simplified UI, improved connectivity, enhanced support, clearer pricing, and performance scalability for large data.
Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
 

Setup Cost

Apache Hadoop is cost-effective for large-scale deployments, but smaller enterprises face higher expenses despite potential cloud cost savings.
Azure Data Factory has complex pricing, with costs varying widely due to usage, data volume, and additional services.
The pricing is cost-effective.
It is considered cost-effective.
 

Valuable Features

Apache Hadoop offers cost-efficient, scalable data processing with HDFS, supporting large datasets and seamless integration with tools like Spark.
Azure Data Factory provides scalable data transformation, integration, and orchestration with user-friendly interface and extensive connector support.
It connects to different sources out-of-the-box, making integration much easier.
The interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with.
 

Categories and Ranking

Apache Hadoop
Average Rating
7.8
Reviews Sentiment
6.8
Number of Reviews
39
Ranking in other categories
Data Warehouse (7th)
Azure Data Factory
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
88
Ranking in other categories
Data Integration (1st), 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.
Joy Maitra - PeerSpot reviewer
Facilitates seamless data pipeline creation with good analytics and and thorough monitoring
Azure Data Factory is a low code, no code platform, which is helpful. It provides many prebuilt functionalities that assist in building data pipelines. Also, it facilitates easy transformation with all required functionalities for analytics. Furthermore, it connects to different sources out-of-the-box, making integration much easier. The monitoring is very thorough, though a more readable version would be appreciable.
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Top Industries

By visitors reading reviews
Financial Services Firm
33%
Computer Software Company
10%
University
7%
Energy/Utilities Company
6%
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
7%
 

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?
Hadoop lacks OLAP capabilities. I recommend adding a Delta Lake feature to make the data compatible with ACID properties. Also, video and audio streaming import issues could be improved to ensure p...
How do you select the right cloud ETL tool?
AWS Glue and Azure Data factory for ELT best performance cloud services.
How does Azure Data Factory compare with Informatica PowerCenter?
Azure Data Factory is flexible, modular, and works well. In terms of cost, it is not too pricey. It offers the stability and reliability I am looking for, good scalability, and is easy to set up an...
How does Azure Data Factory compare with Informatica Cloud Data Integration?
Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power Q...
 

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Overview

 

Sample Customers

Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web Lab
1. Adobe 2. BMW 3. Coca-Cola 4. General Electric 5. Johnson & Johnson 6. LinkedIn 7. Mastercard 8. Nestle 9. Pfizer 10. Samsung 11. Siemens 12. Toyota 13. Unilever 14. Verizon 15. Walmart 16. Accenture 17. American Express 18. AT&T 19. Bank of America 20. Cisco 21. Deloitte 22. ExxonMobil 23. Ford 24. General Motors 25. IBM 26. JPMorgan Chase 27. Microsoft (Azure Data Factory is developed by Microsoft) 28. Oracle 29. Procter & Gamble 30. Salesforce 31. Shell 32. Visa
Find out what your peers are saying about Apache Hadoop vs. Azure Data Factory and other solutions. Updated: December 2024.
825,609 professionals have used our research since 2012.