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Compare Apache Spark vs. Google Cloud Dataflow

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Apache Spark Logo
10,059 views|8,087 comparisons
Google Cloud Dataflow Logo
4,985 views|4,601 comparisons
Top Review
Find out what your peers are saying about Apache, Cloudera, IBM and others in Hadoop. Updated: October 2021.
542,608 professionals have used our research since 2012.
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Pricing and Cost Advice
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

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Questions from the Community
Top Answer: I don't think using Apache Spark without Hadoop has any major drawbacks or issues. I have used Apache Spark quite successfully with AWS S3 on many projects which are batch based. Yes for very high… more »
Top Answer: The solution has been very stable.
Top Answer: We use the open-source version. It is free to use. However, you do need to have servers. We have three or four. they can be on-premises or in the cloud.
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Ranking
1st
out of 22 in Hadoop
Views
10,059
Comparisons
8,087
Reviews
11
Average Words per Review
472
Rating
8.6
11th
out of 38 in Streaming Analytics
Views
4,985
Comparisons
4,601
Reviews
0
Average Words per Review
0
Rating
N/A
Comparisons
Also Known As
Google Dataflow
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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

Google Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
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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 Solutions
Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale
Top Industries
REVIEWERS
Financial Services Firm40%
Computer Software Company20%
Marketing Services Firm10%
Non Profit10%
VISITORS READING REVIEWS
Computer Software Company23%
Comms Service Provider19%
Financial Services Firm11%
Media Company9%
VISITORS READING REVIEWS
Computer Software Company21%
Comms Service Provider21%
Media Company10%
Financial Services Firm9%
Company Size
REVIEWERS
Small Business38%
Midsize Enterprise21%
Large Enterprise41%
No Data Available
Find out what your peers are saying about Apache, Cloudera, IBM and others in Hadoop. Updated: October 2021.
542,608 professionals have used our research since 2012.

Apache Spark is ranked 1st in Hadoop with 10 reviews while Google Cloud Dataflow is ranked 11th in Streaming Analytics. Apache Spark is rated 8.6, while Google Cloud Dataflow is rated 0.0. The top reviewer of Apache Spark writes "Good Streaming features enable to enter data and analysis within Spark Stream". On the other hand, Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, AWS Lambda and Informatica Big Data Parser, whereas Google Cloud Dataflow is most compared with Apache NiFi, Apache Flink, Amazon Kinesis, Azure Stream Analytics and Talend Data Streams.

We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.