Amazon EMR is a cloud-native big data platform for processing vast amounts of data quickly, at scale. Using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi (Incubating), and Presto, coupled with the scalability of Amazon EC2 and scalable storage of Amazon S3, EMR gives analytical teams the engines and elasticity to run Petabyte-scale analysis.
Director of Customer Operations & Account Management
Chose Amazon EMR (Elastic MapReduce)
EMR is more suited for developers. Databricks feel more for data science-oriented with its notebooks and customs visualizations. With EMR you can more easily add additional capacity on-damnd on the instance. With others is a more cumbersome process. And then, you can also …
Perhaps the biggest advantage Amazon Elastic MapReduce has over competing big data management software is the user base. Elastic MapReduce, compliments of its connection with Amazon, has a large user base to whom questions about functionality can be addressed. The software also …
The alternatives to EMR are mainly hadoop distributions owned by the 3 companies above. I have not used the other distributions so it is difficult to comment, but the general tradeoff is, at the cost of a longer setup time and more infra management, you get more flexible …
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.
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.
How does Apache Spark perform against competing tools? I think Apache Spark does well in processing large volumes of data. The machine learning models also seem to be easier to program and interpret. With that said, the programming side of Apache Spark seems more difficult …
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 …
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 …