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Apache Spark

A distributed processing system utilized for big data workloads, supporting batch processing, stream processing and machine learning, with in-memory caching and optimized query execution for large datasets

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Swift Processing
Achieves high data processing speed by reducing read-write to disk
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Dynamic Nature
Supports the development of parallel applications with 80 high-level operators
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In-Memory Computation
Increases processing speed by caching data, avoiding disk fetch each time
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Reusability
Allows code reuse for batch-processing, stream joining, and ad-hoc queries
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Fault Tolerance
Utilizes RDD abstraction to handle worker node failures, reducing data loss
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Real-Time Stream Processing
Handles real-time data, overcoming Hadoop MapReduce limitations
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Lazy Evaluation
Transforms in Spark RDD are lazy, increasing system efficiency
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Multiple Language Support
Provides APIs in Java, Scala, Python, and R
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DAG Execution Engine
Facilitates in-memory computation and acyclic data flow
...21 more features/limitations. Contact us to get a complete list of features and system requirements.

Platform

Desktop
Language
SQLScalaRPythonJava

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System Requirements

#Minimum
1
8-16 cores per machine
2
8 GB to hundreds of GBs
3
4-8 disks per node
4
10 Gigabit or higher network
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Ratings

4.08
5

PAT RESEARCH
7.7
10
based on professional's opinion
PAT RESEARCH
8.2
10
based on 2 reviews
TrustRadius
8.6
10
based on 101 reviews

Developer

Written in

Scala, Java, Python, R

Initial Release

2014-05-26

Repository

License

Categories


Notes

Libraries:

  1. Spark SQL is Apache Spark’s module for working with structured data.
  2. Spark Connect is a protocol that specifies how a client application can communicate with a remote Spark Server.
  3. Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.
  4. MLlib is Apache Spark’s scalable machine learning library.
  5. GraphX is Apache Spark’s API for graphs and graph-parallel computation.