Compare Apache Spark vs. Hortonworks Data Platform

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Most Helpful Review
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Find out what your peers are saying about Apache Spark vs. Hortonworks Data Platform and other solutions. Updated: September 2020.
442,283 professionals have used our research since 2012.
Quotes From Members

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

Pros
"I found the solution stable. We haven't had any problems with it.""The scalability has been the most valuable aspect of the solution.""The most valuable feature of this solution is its capacity for processing large amounts of data.""The solution is very stable.""I feel the streaming is its best feature.""The features we find most valuable are the machine learning, data learning, and Spark Analytics.""The main feature that we find valuable is that it is very fast.""The processing time is very much improved over the data warehouse solution that we were using."

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"The Hortonworks solution is so stable. It is working as a production system, without any error, without any downtime. If I have downtime, it is mostly caused by the hardware of the computers.""Now, using this solution, it is much cheaper to have all of the data available for searching, not in real-time, but whenever there is a pending request.""The scalability is the key reason why we are on this platform.""Hortonworks should not be expensive at all to those looking into using it."

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Cons
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster.""The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive.""When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data.""The solution needs to optimize shuffling between workers.""When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources.""We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data.""We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time.""I would like to see integration with data science platforms to optimize the processing capability for these tasks."

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"I work a lot with banking, IT and communications customers. Hortonworks must improve or must upgrade their services for these sectors.""I would like to see more support for containers such as Docker and OpenShift.""The version control of the software is also an issue."

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Pricing and Cost Advice
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"It is priced well and it is affordable"

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Questions from the Community
Top Answer: SQreamDB is a GPU DB. It is not suitable for real-time oltp of course. Cassandra is best suited for OLTP database use cases, when you need a scalable database (instead of SQL server, Postgres)… more »
Top Answer: I love every core functionality of Apache Spark Initially they have only provided RDD basic interface to process the data across distributed cluster. Then it evolved to dataframe and dataset interface… more »
Top Answer: Apache spark is available in cloud services like AWS cloud, Azure. We have to use the specific service for our use case. For example we can use AWS Glue which runs spark for ETL process, AWS EMR… more »
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Ranking
1st
out of 23 in Hadoop
Views
11,256
Comparisons
9,343
Reviews
11
Average Words per Review
353
Avg. Rating
8.2
3rd
out of 23 in Hadoop
Views
3,265
Comparisons
2,107
Reviews
2
Average Words per Review
546
Avg. Rating
9.0
Popular Comparisons
Compared 32% of the time.
Compared 7% of the time.
Compared 7% of the time.
Compared 6% of the time.
Compared 16% of the time.
Compared 7% of the time.
Also Known As
Hortonworks, HDP
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Apache
Cloudera
Overview

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

Hortonworks is a leading innovator in the industry, creating, distributing and supporting enterprise-ready open data platforms and modern data applications. Our mission is to manage the world's data. We have a single-minded focus on driving innovation in open source communities such as Apache Hadoop, NiFi, and Spark. We along with our 1600+ partners provide the expertise, training and services that allow our customers to unlock transformational value for their organizations across any line of business. Our connected data platforms powers modern data applications that deliver actionable intelligence from all data: data-in-motion and data-at-rest. We are Powering the Future of Data.
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Sample Customers
NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi SolutionsMayo Clinic, Symantec, Progressive Insurance, Noble Energy, Cardinal Health, Rogers, Mercy, Neustar, TRUECar, T-Mobile
Top Industries
REVIEWERS
Financial Services Firm38%
Computer Software Company25%
Marketing Services Firm13%
Non Profit13%
VISITORS READING REVIEWS
Computer Software Company30%
Media Company14%
Comms Service Provider14%
Financial Services Firm6%
VISITORS READING REVIEWS
Computer Software Company39%
Media Company15%
Comms Service Provider8%
Transportation Company6%
Company Size
REVIEWERS
Small Business40%
Midsize Enterprise20%
Large Enterprise40%
REVIEWERS
Small Business27%
Midsize Enterprise18%
Large Enterprise55%
Find out what your peers are saying about Apache Spark vs. Hortonworks Data Platform and other solutions. Updated: September 2020.
442,283 professionals have used our research since 2012.
Apache Spark is ranked 1st in Hadoop with 11 reviews while Hortonworks Data Platform is ranked 3rd in Hadoop with 3 reviews. Apache Spark is rated 8.2, while Hortonworks Data Platform is rated 8.6. The top reviewer of Apache Spark writes "Good Streaming features enable to enter data and analysis within Spark Stream". On the other hand, the top reviewer of Hortonworks Data Platform writes "Provides a complete solution and just one user interface that can manage all the packages". Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, SAP HANA and AWS Lambda, whereas Hortonworks Data Platform is most compared with Amazon EMR, Cask, Cloudera Distribution for Hadoop, MapR and Cloudera DataFlow. See our Apache Spark vs. Hortonworks Data Platform report.

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