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 |
|---|---|---|
| + | Dynamic Nature | Supports the development of parallel applications with 80 high-level operators |
| + | In-Memory Computation | Increases processing speed by caching data, avoiding disk fetch each time |
| + | Reusability | Allows code reuse for batch-processing, stream joining, and ad-hoc queries |
| + | Fault Tolerance | Utilizes RDD abstraction to handle worker node failures, reducing data loss |
| + | Real-Time Stream Processing | Handles real-time data, overcoming Hadoop MapReduce limitations |
| + | Lazy Evaluation | Transforms in Spark RDD are lazy, increasing system efficiency |
| + | Multiple Language Support | Provides APIs in Java, Scala, Python, and R |
| + | 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.
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
4.085
| PAT RESEARCH | 7.710 based on professional's opinion |
|---|---|
| PAT RESEARCH | 8.210 based on 2 reviews |
| TrustRadius | 8.610 based on 101 reviews |
Written in
Scala, Java, Python, R
Initial Release
2014-05-26
Repository
License
Categories
Alternatives
Notes
Libraries:
- Spark SQL is Apache Spark’s module for working with structured data.
- Spark Connect is a protocol that specifies how a client application can communicate with a remote Spark Server.
- Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.
- MLlib is Apache Spark’s scalable machine learning library.
- GraphX is Apache Spark’s API for graphs and graph-parallel computation.