Apache Spark Reviews

Name: Apache Spark
Brand: Apache
Rating: 4 (60 reviews)

Vendor: Apache

4.2 out of 5

60 reviews
89% willing to recommend

1,169 followers

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What is Apache Spark?UNIXBusinessApplication
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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

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Apache Spark is the #1 ranked solution in top Hadoop solutions, #2 ranked solution in top Java Frameworks, and #5 ranked solution in top Compute Service solutions. PeerSpot users give Apache Spark an average rating of 8.4 out of 10. Apache Spark is most commonly compared to Spring Boot: Apache Spark vs Spring Boot. Apache Spark is popular among the large enterprise segment, accounting for 71% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 25% of all views.

Helped 767,319 peers since 2012

Featured reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

Aug 3, 2022

We use batch processing. It works well with our formats and file versions. There's a lot of functionality. In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000. The solution is scalable. It's a stable product.

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SurjitChoudhury

Data engineer at Cocos pt

Feb 20, 2024

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.

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Suriya Senthilkumar

Analyst at Deloitte

Feb 26, 2024

We use the product in our environment for data processing and performing Data Definition Language (DDL) operations The product’s most valuable features are lazy evaluation and workload distribution. They could improve the issues related to programming language for the platform. We have been…

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Apache Spark market share

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As of March 2024, the market share of Apache Spark in the Hadoop category stands at 19.1%, marking a decrease of 18.3% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Hadoop

Key learnings from peers

Last updated Apr 1, 2024

Valuable Features

"The product's deployment phase is easy."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark.Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."

Room for Improvement

"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."
"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."

Pricing

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"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."
"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."

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

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Review data by company size

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Top industries

By visitors reading reviews

Financial Services Firm

25%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Retailer

University

Educational Organization

Government

Insurance Company

Healthcare Company

Energy/Utilities Company

Real Estate/Law Firm

Media Company

Construction Company

Non Profit

Hospitality Company

Wholesaler/Distributor

Transportation Company

Legal Firm

Recreational Facilities/Services Company

Logistics Company

Performing Arts

Pharma/Biotech Company

Outsourcing Company

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Apache Spark customers

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