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Apache Spark vs Cloudera DataFlow vs QueryIO 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:
 

Mindshare comparison

Hadoop Mindshare Distribution
ProductMindshare (%)
Apache Spark12.9%
Cloudera Distribution for Hadoop13.8%
HPE Data Fabric11.6%
Other61.699999999999996%
Hadoop
Streaming Analytics Mindshare Distribution
ProductMindshare (%)
Cloudera DataFlow1.9%
Apache Flink9.8%
Databricks8.2%
Other80.1%
Streaming Analytics
Hadoop Mindshare Distribution
ProductMindshare (%)
QueryIO2.8%
Cloudera Distribution for Hadoop13.8%
Apache Spark12.9%
Other70.5%
Hadoop
 

Featured Reviews

Devindra Weerasooriya - PeerSpot reviewer
Data Architect at Devtech
Provides a consistent framework for building data integration and access solutions with reliable performance
The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.
Mohamed-Saied - PeerSpot reviewer
Senior Data Architect at Teradata Corporation
Efficient data integration and workflow scheduling elevate project performance
Cloudera DataFlow is used as an ETL or ELT solution within Cloudera's data pipeline. Our organization heavily relies on it for data ingestion, transformation, and warehousing. It is also used daily for operational tasks, and it integrates well within Cloudera's ecosystem for high performance and…
MR
Manager of Process & Systems / Solutions Architect / BI Developer at HENKEL FRANCE
Stable with good connectivity and good integration capabilities
Data cleansing is not intuitive and user-friendly. When things have errors, you have to hunt them down as opposed to the solution simply showing you intuitively where to find it. I would recommend that they look at that Tableau Prep tool and see how it is pieced together. That's a great data cleansing tool. If Microsoft has something like that, then we wouldn't even have to look at some of the other options. There needs to be some simplification of the user interface. Right now it's too complicated. There isn't a way to put controls on the solution, so anyone can use any part of it, and sometimes novices will go and try to create things, but not know enough about what is official and what is published. It would be ideal if we could segment off certain sections so that not everyone had access to the whole solution. I'd like to see something more of a mapping tool so that you could see how the reports are connected, similar to Tableau Prep and Naim. That would make for a pretty useful diagnostics check. People would be better able to understand the linkage between your datasets. It would be nice if the solution offered some templates. It would make it even more plug and play, and give people a good jumping-off point. After that, they could explore other bells and whistles as they get further into understanding the solution. The solution should work in some virtualization. It would be a good added feature. If this product had those things then I wouldn't need to use other products.

Quotes from Members

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

Pros

"The most valuable feature of Apache Spark is its ease of use."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"The fast performance is the most valuable aspect of the solution."
"The data processing framework is good."
"It's a nice system for batch processing huge data."
"As it uses in-memory data processing, Spark is very fast."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"The processing time is very much improved over the data warehouse solution that we were using."
"Cloudera DataFlow is fully compatible with Cloudera's ecosystem and offers high efficiency through native connectors for various ecosystems."
"DataFlow's performance is okay."
"This solution is very scalable and robust."
"The initial setup was not so difficult"
"The most effective features are data management and analytics."
"This solution is very scalable and robust."
"It's so readily available and there's information online to educate yourself on the product."
"Anyone who has even a little bit of knowledge of the solution can begin to create things. You don't have to be technical to use the solution."
 

Cons

"It is useful for scientific purposes, but for commercial use of big data, it gives some trouble."
"There were some problems related to the product's compatibility with a few Python libraries."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"Apache Spark lacks geospatial data."
"Better data lineage support."
"Dynamic DataFrame options are not yet available."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"Although their workflow is pretty neat, it still requires a lot of transformation coding; especially when it comes to Python and other demanding programming languages."
"Cloudera DataFlow's UI interface could be enhanced significantly. Memory handling can also be improved to be better than it is today."
"Although their workflow is pretty neat, it still requires a lot of transformation coding; especially when it comes to Python and other demanding programming languages."
"It is not easy to use the R language. Though I don't know if it's possible, I believe it is possible, but it is not the best language for machine learning."
"It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists."
"There needs to be some simplification of the user interface."
"Technical support is not that great. It's more like a study session than support."
 

Pricing and Cost Advice

"Spark is an open-source solution, so there are no licensing costs."
"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."
"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"It is an open-source platform. We do not pay for its subscription."
"Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."
"Apache Spark is an open-source tool."
"The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks."
"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"DataFlow isn't expensive, but its value for money isn't great."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
24%
Manufacturing Company
7%
Comms Service Provider
6%
Computer Software Company
6%
Financial Services Firm
18%
Healthcare Company
8%
Computer Software Company
8%
Construction Company
8%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise16
Large Enterprise32
No data available
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for Apache Spark?
Apache Spark is open-source, so it doesn't incur any charges.
What needs improvement with Apache Spark?
I find that there really lacks the technical depth to do any recommendations for future updates of Apache Spark. I us...
What is your primary use case for Apache Spark?
I attempted to use Apache Spark in one of our customer projects, but after the initial test, our customer moved to an...
What do you like most about Cloudera DataFlow?
The most effective features are data management and analytics.
What needs improvement with Cloudera DataFlow?
Cloudera DataFlow's UI interface could be enhanced significantly. Memory handling can also be improved to be better t...
What is your primary use case for Cloudera DataFlow?
Cloudera DataFlow is used as an ETL or ELT solution within Cloudera's data pipeline. Our organization heavily relies ...
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Also Known As

No data available
CDF, Hortonworks DataFlow, HDF
No data available
 

Overview

 

Sample Customers

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