Apache Spark

Apache Spark

159 Reviews and Ratings

Overview
Product	Rating	Most Used By	Product Summary	Starting Price
Apache Spark	Score 8.8 out of 10	N/A	N/A	N/A

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Apache Spark

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Apache Spark
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Community Pulse
	Apache Spark
Considered Both Products	Apache Spark Ananth Gouri Assistant Professor 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 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 … Helpful? Steven Li Senior Software Developer (Consultant) Chose Apache Spark Other teams used to work on Apache Hadoop but our team started with Apache Spark directly. Helpful? Verified User Engineer 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? Verified User Analyst 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 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? Chetan Munegowda Software Engineer 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. Helpful? YM Yogesh Mhasde Technical Manager 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. Helpful? Verified User Executive 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 Analyst 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? SS Shiv Shivakumar Acquisitions Leader 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. 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 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 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 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 Employee Chose Apache Spark We specifically choose Spark over MapReduce to make the cluster processing faster Incentivized Helpful? Verified User Engineer 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 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 Director 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 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?
Top Pros	Pro Machine learning Pro Data sets Pro Easy to use
Top Cons	Minus Data visualization Minus Learning curve Minus Amounts of data

Best Alternatives
	Apache Spark
Small Businesses	No answers on this topic
Medium-sized Companies	Cloudera Manager Score 9.9 out of 10
Enterprises	IBM Analytics Engine Score 8.6 out of 10
All Alternatives	View all alternatives

User Ratings
	Apache Spark
Likelihood to Recommend	10.0 (23 ratings)
Likelihood to Renew	10.0 (1 ratings)
Usability	10.0 (3 ratings)
Support Rating	8.7 (4 ratings)

User Testimonials
	Apache Spark
Likelihood to Recommend	Apache Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible. Incentivized Ananth Gouri Assistant Professor Read full review
Pros	Apache Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues Faster in execution times compare to Hadoop and PIG Latin Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner Interoperability between SQL and Scala / Python style of munging data Incentivized Nitin Pasumarthy Software Engineer Read full review
Cons	Apache Memory management. Very weak on that. PySpark not as robust as scala with spark. spark master HA is needed. Not as HA as it should be. Locality should not be a necessity, but does help improvement. But would prefer no locality Incentivized Anson Abraham Data Czar Read full review
Likelihood to Renew	Apache Capacity of computing data in cluster and fast speed. Steven Li Senior Software Developer (Consultant) Read full review
Usability	Apache The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times. Incentivized Verified User Anonymous Read full review
Support Rating	Apache 1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications. YM Yogesh Mhasde Technical Manager Read full review
Alternatives Considered	Apache 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 getting the ability to write your application in the language of your choosing. Incentivized Verified User Anonymous Read full review
Return on Investment	Apache Business leaders are able to take data driven decisions Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available Business is able come up with new product ideas Incentivized Surendranatha Reddy Chappidi Senior Data Engineer Read full review
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