Apache Hadoop vs Apache Spark comparison

The compared Apache Hadoop and Apache Spark solutions aren't in the same category. Apache Hadoop is ranked #6 in Data Warehouse , with an average rating of 7.9, and holds a 5.1% mindshare in the category. Apache Spark is ranked #1 in H , with an average rating of 8.7, and holds a 18.2% mindshare. Additionally, 89% of Apache Hadoop users are willing to recommend the solution, compared to 90% of Apache Spark users who would recommend it.

Apache Hadoop

Read 39 Apache Hadoop reviews

1,860 Views
1,555 Comparison Views

89% willing to recommend

Apache Spark

Read 64 Apache Spark reviews

1,975 Views
1,544 Comparison Views

90% willing to recommend

Apache Hadoop

Apache Spark

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Apache Hadoop and Apache Spark based on real PeerSpot user reviews.

Find out what your peers are saying about Snowflake Computing, Oracle, Teradata and others in Data Warehouse.

To learn more, read our detailed Data Warehouse Report (Updated: October 2024).

Buyer's Guide

Data Warehouse

October 2024

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Helped 816,406 peers since 2012

Categories and Ranking

Apache Hadoop

Average Rating

7.8

Number of Reviews

Ranking in other categories

Data Warehouse (6th)

Apache Spark

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

Hadoop (1st), Compute Service (4th), Java Frameworks (2nd)

Mindshare comparison

Apache Hadoop and Apache Spark aren’t in the same category and serve different purposes. Apache Hadoop is designed for Data Warehouse and holds a mindshare of 5.1%, down 6.2% compared to last year.
Apache Spark, on the other hand, focuses on Hadoop, holds 18.2% mindshare, down 21.9% since last year.

Data Warehouse

Hadoop

Q&A Highlights

it_user1272297

Special Adviser Strategy at a university with 501-1,000 employees

Apr 19, 2020

Which is the best RDMBS solution for big data?

I haven't used SQream personally. However, if you are only considering GPU based rdbms's please check the following https://hackernoon.com/which-gpu-database-is-right-for-me-6ceef6a17505

See all answers

Featured Reviews

Sushil Arya

Software developer at Fiserv

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.

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SurjitChoudhury

Data engineer at Cocos pt

Offers batch processing of data and in-memory processing in Spark greatly enhances performance

Spark supports real-time data processing through Spark Streaming. It allows for batch processing of data. If you have immediate data, like chat information, that needs to be processed in real-time, Spark Streaming is used. For data that can be evaluated later, batch processing with Apache Spark is suitable. Mostly, batch processing is utilized in our organization, but for streaming data processing, tools like Kafka are often integrated. In-memory processing in Spark greatly enhances performance, making it a hundred times faster than the previous MapReduce methods. This improvement is achieved through optimization techniques like caching, broadcasting, and partitioning, which help in optimizing queries for faster processing.

Read full review

Quotes from Members

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

Pros

"The scalability of Apache Hadoop is very good."

"Hadoop can store any kind of data—structured, unstructured, and semi-structured—and presents it using the relational model through Hive."

"High throughput and low latency. We start with data mashing on Hive and finally use this for KPI visualization."

"The platform's quick data processing capabilities have been instrumental in supporting our AI-driven projects."

"The most valuable feature is the database."

"Since both Apache Hadoop and Amazon EC2 are elastic in nature, we can scale and expand on demand for a specific PoC, and scale down when it's done."

"Hadoop File System is compatible with almost all the query engines."

"The tool's stability is good."

More Apache Hadoop pros

"Provides a lot of good documentation compared to other solutions."

"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."

"We use it for ETL purposes as well as for implementing the full transformation pipelines."

"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."

"The good performance. The nice graphical management console. The long list of ML algorithms."

"The most valuable feature of Apache Spark is its flexibility."

"We use Spark to process data from different data sources."

"There's a lot of functionality."

More Apache Spark pros

Cons

"There are certain shortcomings when it comes to the product's technical support part, making it an area where improvements are required."

"Improvements in security measures would be beneficial, given the large volumes of data handled."

"The integration with Apache Hadoop with lots of different techniques within your business can be a challenge."

"In the next release, I would like to see Hive more responsive for smaller queries and to reduce the latency."

"I would like to see more direct integration of visualization applications."

"I mentioned it definitely, and this is probably the only feature we can improve a little bit because the terminal and coding screen on Hadoop is a little outdated, and it looks like the old C++ bio screen. If the UI and UX can be improved slightly, I believe it will go a long way toward increasing adoption and effectiveness."

"General installation/dependency issues were there, but were not a major, complex issue. While migrating data from MySQL to Hive, things are a little challenging, but we were able to get through that with support from forums and a little trial and error."

"The upgrade path should be improved because it is not as easy as it should be."

More Apache Hadoop cons

"The solution’s integration with other platforms should be improved."

"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."

"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."

"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."

"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."

"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."

"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."

"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."

More Apache Spark cons

Pricing and Cost Advice

"Do take into consider that data storage and compute capacity scale differently and hence purchasing a "boxed" / 'all-in-one" solution (software and hardware) might not be the best idea."

"It's reasonable, but there's room for improvement in cost-effectiveness."

"We don't directly pay for it. Our clients pay for it, and they usually don't complain about the price. So, it is probably acceptable."

"This is a low cost and powerful solution."

"The price of Apache Hadoop could be less expensive."

"The product is open-source, but some associated licensing fees depend on the subscription level."

"There are no licensing costs involved, hence money is saved on the software infrastructure."

"If my company can use the cloud version of Apache Hadoop, particularly the cloud storage feature, it would be easier and would cost less because an on-premises deployment has a higher cost during storage, for example, though I don't know exactly how much Apache Hadoop costs."

More Apache Hadoop pricing and cost advice

"It is an open-source solution, it is free of charge."

"They provide an open-source license for the on-premise version."

"Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."

"It is an open-source platform. We do not pay for its subscription."

"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."

"Spark is an open-source solution, so there are no licensing costs."

More Apache Spark pricing and cost advice

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Answers from the Community

it_user1272297

Special Adviser Strategy at a university with 501-1,000 employees

Apr 19, 2020

Which is the best RDMBS solution for big data?

I haven't used SQream personally. However, if you are only considering GPU based rdbms's please check the following https://hackernoon.com/which-gpu-database-is-right-for-me-6ceef6a17505

2 out of 4 answers

Russell Rothstein

CEO at PeerSpot

Jan 27, 2020

Morten, the most popular comparisons of SQream can be found here: https://www.itcentralstation.com/products/sqream-db-alternatives-and-competitors The top ones include Cassandra, MemSQL, MongoDB, and Vertica.

Read full answer

reviewer1219965

Data Architect at a tech services company with 201-500 employees

Jan 27, 2020

I haven't used SQream personally. However, if you are only considering GPU based rdbms's please check the following https://hackernoon.com/which-gpu-database-is-right-for-me-6ceef6a17505

Read full answer

See all 4 answers

Top Industries

By visitors reading reviews

Financial Services Firm

32%

Computer Software Company

11%

University

Energy/Utilities Company

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

University

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.

See all answers

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...

See all answers

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...

See all answers

What do you like most about Apache Spark?

We use Spark to process data from different data sources.

See all answers

What is your experience regarding pricing and costs for Apache Spark?

Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...

See all answers

What needs improvement with Apache Spark?

The main concern is the overhead of Java when distributed processing is not necessary. In such cases, operations can often be done on one node, making Spark's distributed mode unnecessary. Conseque...

See all answers

Comparisons

Azure Data Factory vs Apache Hadoop

Compared 17% of the time

Oracle Exadata vs Apache Hadoop

Compared 17% of the time

Microsoft Azure Synapse Analytics vs Apache Hadoop

Compared 11% of the time

Teradata vs Apache Hadoop

Compared 11% of the time

BigQuery vs Apache Hadoop

Compared 11% of the time

More Apache Hadoop Competitors

Spring Boot vs Apache Spark

Compared 30% of the time

SAP HANA vs Apache Spark

Compared 11% of the time

AWS Batch vs Apache Spark

Compared 11% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 8% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

More Apache Spark Competitors

Product Reports

Buyer's Guide

Apache Hadoop

November 2024

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Buyer's Guide

Apache Spark

November 2024

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Apache

Overview

The Apache Hadoop project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

Sample Customers

Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web Lab

NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions

Find out what your peers are saying about Snowflake Computing, Oracle, Teradata and others in Data Warehouse. Updated: October 2024.

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