Apache Spark

Apache Spark

161 Reviews and Ratings

Overview
Product	Rating	Most Used By	Product Summary	Starting Price
Apache Spark	Score9.2 out of 10	N/A	Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.	N/A

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Apache Spark

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Apache Spark
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Key User Insights
	Apache Spark
Considered Both Products	Apache Ananth Gouri Assistant Professor in Engineering at The National Institute of Engineering, Mysuru (Education Management, 501-1000 employees) Chose Apache Spark We used Surprise Kit for one of the other research works. It is more fine-tuned to Recommendation systems and their algorithms. Apache Spark has MLlib for majority of ML problems. Where as software like Surprse Kit - it suitable for a specific task of Recommendations only. Incentivized Helpful? Riyaz Khan Staff Engineer in Information Technology at Nagarro (Information Technology & Services, 10,001+ employees) Chose Apache Spark Apache Spark is a fast-processing in-memory computing framework. It is 10 times faster than Apache Hadoop. Earlier we were using Apache Hadoop for processing data on the disk but now we are shifted to Apache Spark because of its in-memory computation capability. Also in SAP … Incentivized Helpful? Steven Li Senior Software Developer (Consultant) in Information Technology at Morgan Stanley (Banking, 10,001+ employees) Chose Apache Spark Other teams used to work on Apache Hadoop but our team started with Apache Spark directly. Incentivized Helpful? Verified User Chose Apache Spark There are a few alternatives that can do the same transformation and aggregation like Apache Spark can do but most of them are not able to perform parallel computation. For example, pandas is a really good tool to do that but not parallelized; However, there are some tools that … Incentivized Helpful? Surendranatha Reddy Chappidi Senior Data Engineer in Information Technology at A.P. Moller - Maersk (Logistics & Supply Chain, 10,001+ employees) Chose Apache Spark Apache Spark works in distributed mode using cluster Informatica and Datastage cannot scale horizontally We can write custom code in spark, whereas in Datastage and Informatica we can only choose the different features proivided already. Incentivized Helpful? Verified User Chose Apache Spark Apache Spark has much more better performance and features if we compare with Hive or map/reduce kind of solutions. Spark has many other features for machine learning, streaming. Incentivized Helpful? Chetan Munegowda Software Engineer in Information Technology at SemanticBits (Information Technology and Services, 201-500 employees) Chose Apache Spark Spark is simply awesome to work on with any data sets and also has an in-memory database which makes it very flexible. Incentivized Helpful? Yogesh Mhasde Technical Manager in Information Technology at Rishabh Software Private Limited (Information Technology & Services, 501-1000 employees) Chose Apache Spark 1. Apache Spark is almost 100 % faster than Hadoop. 2. Apache Spark is more stable than Amazon EMR. 3. The end to end distributed machine library is more robust in Apache Spark. Incentivized Helpful? Verified User Chose Apache Spark Databricks uses Spark as a foundation, and is also a great platform. It does bring several add-ons, which we did not feel needed by the time we evaluated - and haven't needed since then. One interesting plus in our opinion was the engineering support, which is great depending … Incentivized Helpful? Verified User Chose Apache Spark It is easy to learn, read and to maintain. It brings the best of the Ruby on Rails framework from Java that helps to create a web service so easily. Communication is one of the most distinctive features of Apache Spark compared to alternative products. You are able to … Incentivized Helpful? Shiv Shivakumar Acquisitions Leader in Information Technology at Abbott (Computer Software, 1001-5000 employees) Chose Apache Spark We evaluated SAS alongside with Apache Spark but during the course of proof of concept found that Apache Spark was able to support the hadoop eco-system and hadoop file system much better. It was much faster at that time while having the ability to process data quickly for the … Incentivized Helpful? Carla Borges Consultor Tecnico - Java Developer and Php Developer. in Engineering at Consultec-TI (Computer Software, 51-200 employees) Chose Apache Spark I prefer Apache Spark compared to Hadoop, since in my experience Spark has more usability and comes equipped with simple APIs for Scala, Python, Java and Spark SQL, as well as provides feedback in REPL format on the commands. At the same time, Apache Spark seems to have the … Incentivized Helpful? Nitin Pasumarthy Software Engineer in Engineering at LinkedIn (Internet, 5001-10,000 employees) Chose Apache Spark All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional … Incentivized Helpful? Kartik Chavan Data Analyst in Information Technology at The University of Texas at Arlington (Electrical/Electronic Manufacturing, 1001-5000 employees) Chose Apache Spark Even with Python, MapReduce is lengthy coding. Combination of Python with Apache Spark will not only shorten the code, but it will effectively increase the speed of algorithms. Occasionally, I use MapReduce, but Apache Spark will replace MapReduce very soon. It has many … Incentivized Helpful? Anson Abraham Data Czar in Engineering at Envisagenics, Inc. (Marketing and Advertising, 51-200 employees) Chose Apache Spark vs MapRedce, it was faster and easier to manage. Especially for Machine Learning, where MapReduce is lacking. Also Apache Storm was slower and didn't scale as much as Spark does. Spark elasticity was easier to apply compared to storm and MapReduce. managing resources for … Incentivized Helpful? Verified User Chose Apache Spark We specifically choose Spark over MapReduce to make the cluster processing faster Incentivized Helpful? Verified User Chose Apache Spark Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and … Incentivized Helpful? Kamesh Emani Software Developer Intern in Information Technology at Louisiana Tech University (Higher Education, 1001-5000 employees) Chose Apache Spark Apache Pig and Apache Hive provide most of the things spark provide but apache spark has more features like actions and transformations which are easy to code. Spark uses optimization technique as we can select driver program and manipulate DAG (Directed Acyclic Graph) Python … Incentivized Helpful? Verified User Chose Apache Spark There are a few newer frameworks for general processing like Flink, Beam, frameworks for streaming like Samza and Storm, and traditional Map-Reduce. I think Spark is at a sweet spot where its clearly better than Map-Reduce for many workflows yet has gotten a good amount of … Incentivized Helpful? Jordan Moore Staff Consultant in Information Technology at Avalon Consulting, LLC (Information Technology and Services, 51-200 employees) Chose Apache Spark Spark has primarily replaced my use of writing pure Hadoop MapReduce or Apache Pig jobs for processing data. I like the fact that I can alternate between the main programming languages that I know - Java and Python - and use those to learn the Scala API. Spark also can be … Incentivized Helpful?
	Would buy again
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	Delivers good value for the price
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	Happy with the feature set
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	Lived up to sales and marketing promises
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	Implementation went as expected
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User Ratings
	Apache Spark
Likelihood to Recommend	9.0 (24 ratings)
Likelihood to Renew	10.0 (1 ratings)
Usability	8.0 (4 ratings)
Support Rating	8.7 (4 ratings)