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Apache Spark is a multi-language engine for data engineering, data science, and machine learning on single-node machines or clusters. It supports batch/streaming data, SQL analytics, data science at scale, and machine learning, and integrates with various frameworks and storage systems.
- Examples
Apache Spark Examples
- Download
Downloads | Apache Spark
- Graphx
GraphX - Apache Spark
- FAQ
Apache Spark ™ FAQ. How does Spark relate to Apache Hadoop?...
- Libraries
Spark SQL & DataFrames - Apache Spark
- Documentation
Overview - Spark 3.5.1 Documentation
- Community
Apache Spark community
- Developers
Useful Developer Tools | Apache Spark
- Apache Software Foundation
Welcome to The Apache Software Foundation!
- Spark Streaming
Spark Structured Streaming uses the same underlying...
- Examples
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Apache Spark is an open-source, distributed processing system for big data workloads. It supports fast analytic queries, machine learning, real-time analytics, and graph processing with in-memory caching and optimized query execution.
Apache Spark is a unified engine for large-scale data processing, with an interface for programming clusters with implicit data parallelism and fault tolerance. It supports various data sources, algorithms, and APIs, such as RDDs, DataFrames, SQL, and machine learning.
- Resilient Distributed Dataset (RDD) Resilient Distributed Datasets (RDDs) are fault-tolerant collections of elements that can be distributed among multiple nodes in a cluster and worked on in parallel.
- Directed Acyclic Graph (DAG) As opposed to the two-stage execution process in MapReduce, Spark creates a Directed Acyclic Graph (DAG) to schedule tasks and the orchestration of worker nodes across the cluster.
- DataFrames and Datasets. In addition to RDDs, Spark handles two other data types: DataFrames and Datasets. DataFrames are the most common structured application programming interfaces (APIs) and represent a table of data with rows and columns.
- Spark Core. Spark Core is the base for all parallel data processing and handles scheduling, optimization, RDD, and data abstraction. Spark Core provides the functional foundation for the Spark libraries, Spark SQL, Spark Streaming, the MLlib machine learning library, and GraphX graph data processing.
- Speed
- Real-Time Stream Processing
- Supports Multiple Workloads
- Increased Usability
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Spark executes very fast by caching data in memory across multiple parallel operations. The main feature of Spark is its in-memory engine that increases the processing speed; making it up to 100 times faster than MapReduce when processed in-memory, and 10 times faster on disk, when it comes to large scale data processing. Spark makes this possible ...
Apache Spark can handle real-time streaming along with the integration of other frameworks. Spark ingests data in mini-batches and performs RDD transformations on those mini-batches of data.
Apache Spark can run multiple workloads, including interactive queries, real-time analytics, machine learning, and graph processing. One application can combine multiple workloads seamlessly.
The ability to support several programming languages makes it dynamic. It allows you to quickly write applications in Java, Scala, Python, and R; giving you a variety of languages for building your applications.
Apache Spark is a fast and versatile engine for big data processing and analytics. It supports multiple workloads, languages, and frameworks, and is based on Hadoop MapReduce.
Apr 3, 2024 · Apache Spark is a data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute data processing tasks across multiple computers,...
Feb 24, 2019 · What is Apache Spark? The company founded by the creators of Spark — Databricks — summarizes its functionality best in their Gentle Intro to Apache Spark eBook (highly recommended read - link to PDF download provided at the end of this article):