Apache Oozie vs Apache Spark: What are the differences?
Developers describe Apache Oozie as "An open-source workflow scheduling system *". It is a server-based workflow scheduling system to manage Hadoop jobs. Workflows in it are defined as a collection of control flow and action nodes in a directed acyclic graph. Control flow nodes define the beginning and the end of a workflow as well as a mechanism to control the workflow execution path. On the other hand, *Apache Spark** is detailed as "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.
Apache Oozie and Apache Spark are primarily classified as "Workflow Manager" and "Big Data" tools respectively.
Apache Spark is an open source tool with 22.9K GitHub stars and 19.7K GitHub forks. Here's a link to Apache Spark's open source repository on GitHub.
According to the StackShare community, Apache Spark has a broader approval, being mentioned in 356 company stacks & 564 developers stacks; compared to Apache Oozie, which is listed in 8 company stacks and 5 developer stacks.
What is Apache Oozie?
What is Apache Spark?
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Spark is good at parallel data processing management. We wrote a neat program to handle the TBs data we get everyday.