Hadoop vs Apache Spark: What are the differences?
What is Hadoop? Open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
What is Apache Spark? Fast and general engine for large-scale data processing. Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Hadoop and Apache Spark are primarily classified as "Databases" and "Big Data" tools respectively.
"Great ecosystem" is the primary reason why developers consider Hadoop over the competitors, whereas "Open-source" was stated as the key factor in picking Apache Spark.
Hadoop and Apache Spark are both open source tools. Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub appears to be more popular than Hadoop with 9.27K GitHub stars and 5.78K GitHub forks.
According to the StackShare community, Apache Spark has a broader approval, being mentioned in 266 company stacks & 112 developers stacks; compared to Hadoop, which is listed in 237 company stacks and 127 developer stacks.
What is Hadoop?
What is Apache Spark?
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The MapReduce workflow starts to process experiment data nightly when data of the previous day is copied over from Kafka. At this time, all the raw log requests are transformed into meaningful experiment results and in-depth analysis. To populate experiment data for the dashboard, we have around 50 jobs running to do all the calculations and transforms of data.
in 2009 we open sourced mrjob, which allows any engineer to write a MapReduce job without contending for resources. We’re only limited by the amount of machines in an Amazon data center (which is an issue we’ve rarely encountered).
The massive volume of discovery data that powers Pinterest and enables people to save Pins, create boards and follow other users, is generated through daily Hadoop jobs...
Spark is good at parallel data processing management. We wrote a neat program to handle the TBs data we get everyday.
Importing/Exporting data, interpreting results. Possible integration with SAS
TBD. Good to have I think. Analytics on loads of data, recommendations?