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Research this topic
Explore the main themes, entities and connections around Apache Spark. Start with the topic map, then use the sections below for research and deeper semantic analysis.
Explore this topic
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
Overview
Open-sourceApplication programming interfaceData parallelismFault toleranceUniversity of California, Berkeley
119 related topics
History
5 related topics
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
- Available in
- Scala, Java, SQL, Python, R, C#, F#
- Developer
- Apache Spark
- License
- Apache License 2.0
- Operating system
- Windows, macOS, Linux
- Original author
- Matei Zaharia
- Release
- May 26, 2014; 12 years ago (2014-05-26)
Topics to explore
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.Browse the full topic structure. Each item opens a new analysis centered on that subject.
Overview
- Open-source Open-source software
- Application programming interface
- Data parallelism
- Fault tolerance
- University of California, Berkeley UC Berkeley
- AMPLab
- Codebase
- Apache Software Foundation
- Multiset Set (abstract data type)
- Fault-tolerant Fault-tolerant computing
- Deprecated
- MapReduce
- Paradigm Programming paradigm
- Dataflow
- Map Map (parallel pattern)
- Reduce Fold (higher-order function)
- Working set
- Shared memory
- Directed acyclic graph
- Iterative algorithms Iterative algorithm
- Database
- Latency Latency (engineering)
- Hadoop Distributed File System (HDFS) Apache Hadoop
- Machine learning
- Cluster manager
- Distributed storage system Clustered file system
- Apache Mesos
- Kubernetes
- Alluxio
- MapR File System (MapR-FS) MapR
History
- Matei Zaharia
- BSD license BSD licenses
- Apache 2.0 Apache License
- Databricks
- Big data
Advanced semantic analysis
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
Map overview Semantic statistics
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.Apache Spark
Nodes127
Edges126
Triples97
Avg. degree1.98
Density0.015748
Components1
How this topic connects Entity context
Quick relationship hints grouped by predicate. Useful for spotting recurring semantic connections around the current entity.See the strongest relationship patterns around the current topic before diving into the raw triples.
Apache Spark
Top relations
related to PySpark · 18
Apache Spark → CPython, DataFrame API, February, It, MLlib, NumPy, Py4J, PySpark, Python, Python API, SciPy, Spark, Spark Core, Spark Declarative Pipelines API, Spark SQL, Spark's, Spark's JVM-based, Structured Streaming
related to GraphX · 14
Apache Spark → Apache Software Foundation, APIs, Bagel, Because, Databricks, GraphX, Like Apache Spark, MapReduce-style API, PageRank, Pregel, RDDs, Spark, UC Berkeley's AMPLab, Unlike
related to Spark Declarative Pipelines · 12
Apache Spark → December, Developers, ETL, Pipelines, Python, SDP, Spark, Spark Declarative Pipelines, Spark SQL, Spark's, SQL, The
related to overview · 11
Apache Spark → API, Dataset API, In Spark, MapReduce, RDD, RDD API, RDDs, Spark, Spark's RDDs, The Dataframe API, The RDD
related to Spark Connect · 11
Apache Spark → Apache Arrow-encoded, April, DataFrame, IDEs, Protocol Buffers, Results, Spark, Spark Connect, Spark's, The, The Spark Connect
related to Language support · 7
Apache Spark → Java, Julia, NET CLR, Python, Scala, SQL, Swift
related to Developers · 3
Apache Spark → PMC, Project Management Committee, The
Available in · 1
Apache Spark → Scala, Java, SQL, Python, R, C#, F#
Developer · 1
Apache Spark → Apache Spark
License · 1
Apache Spark → Apache License 2.0
Important terminology Word statistics
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Important terminology
spark apache data sql python distributed scala provides streaming support api pyspark also rdd programming interface machine pipelines cluster rdds
Entity relationships Subject–Predicate–Object triples
Extracted RDF-like relationships with confidence and source. The table includes structured facts and lower-confidence contextual relations.| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Apache Spark | Available in | Scala, Java, SQL, Python, R, C#, F# | 1.00 | infobox |
| Apache Spark | Developer | Apache Spark | 1.00 | infobox |
| Apache Spark | License | Apache License 2.0 | 1.00 | infobox |
| Apache Spark | Operating system | Windows, macOS, Linux | 1.00 | infobox |
| Apache Spark | Original author | Matei Zaharia | 1.00 | infobox |
| Apache Spark | Release | May 26, 2014; 12 years ago (2014-05-26) | 1.00 | infobox |
| Apache Spark | Repository | Spark Repository | 1.00 | infobox |
| Apache Spark | Stable release | 4.1.2 (Scala 2.13) / May 21, 2026; 3 months ago (2026-05-21) | 1.00 | infobox |
| Apache Spark | Type | Data analytics, machine learning algorithms | 1.00 | infobox |
| Apache Spark | Website | spark.apache.org | 1.00 | infobox |
| Apache Spark | Written in | Scala | 1.00 | infobox |
| Apache Spark | is a | open-source unified analytics engine for large-scale data processing | 0.90 | text |
No triples extracted yet.
Related concept clusters Concept neighborhoods
Clusters of nearby vocabulary surrounding the topic. Scan them for adjacent concepts and language you may have missed.These clusters group vocabulary that occurs around closely connected concepts in the source material.
Connections between topic areas Semantic bridges
Bridge nodes connect otherwise separate parts of the map. Expand a row to inspect the topic groups on each side.Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.
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