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.