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Informatica Big Data Parser [EOL] vs Spark SQL comparison

 

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Executive Summary

Review summaries and opinions

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Categories and Ranking

Informatica Big Data Parser...
Average Rating
0.0
Number of Reviews
0
Ranking in other categories
No ranking in other categories
Spark SQL
Average Rating
7.8
Reviews Sentiment
7.6
Number of Reviews
15
Ranking in other categories
Hadoop (5th)
 

Featured Reviews

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Kemal Duman - PeerSpot reviewer
Team Lead, Data Engineering at Nesine.com
Data pipelines have run faster and support flexible batch and streaming transformations
We do not have any performance problems, but we do have some resource problems. Spark SQL consumes so many resources that we migrated our streaming job from Spark to Apache Flink. Resource management in Spark SQL should be better. It consumes more resources, which is normal. The main reason we switched from Spark is memory and CPU consumption. The major reason is the resource problem because the number of streaming jobs has been increasing in our company. That is why we considered resource management as a priority. Because of the resource consumption, I would say the development of Spark SQL is better. For development purposes, it is a top product and not difficult to work with, but resources are the major problem. We changed to Flink regardless of development time. Development time is less in Spark compared with Flink.
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
20%
University
12%
Retailer
11%
Healthcare Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise6
Large Enterprise4
 

Questions from the Community

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What needs improvement with Spark SQL?
We do not have any performance problems, but we do have some resource problems. Spark SQL consumes so many resources that we migrated our streaming job from Spark to Apache Flink. Resource manageme...
What is your primary use case for Spark SQL?
Spark SQL has been in our stack for less than one year, though some of our colleagues are using it. It is a useful product for transformation jobs. We generally use Spark SQL for batch processing. ...
What advice do you have for others considering Spark SQL?
Regarding the Catalyst query optimizer, I think we are using it. We were using it in the past, but I am not certain if we use it now. We used it a long time ago. I rate my experience with Spark SQL...
 

Also Known As

Big Data Parser
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Overview

 

Sample Customers

Western Union, UPMC, BNY Mellon
UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions
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