Apache Spark

Apache Spark

Apache Software Foundation
Hadoop

Hadoop

Apache Software Foundation
+
+

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About

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.

About

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. A wide variety of companies and organizations use Hadoop for both research and production. Users are encouraged to add themselves to the Hadoop PoweredBy wiki page. Apache Hadoop 3.3.4 incorporates a number of significant enhancements over the previous major release line (hadoop-3.2).

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Organizations that want a unified analytics engine for large-scale data processing

Audience

Data scientists and anyone looking for a platform to manage the distributed processing of large data sets

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Apache Software Foundation
Founded: 1999
United States
spark.apache.org

Company Information

Apache Software Foundation
Founded: 1999
United States
hadoop.apache.org

Alternatives

dbt

dbt

dbt Labs

Alternatives

AWS Glue

AWS Glue

Amazon
Amazon EMR

Amazon EMR

Amazon

Categories

Categories

Streaming Analytics Features

Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation
Real-time Analysis / Reporting
Visualization Dashboards

Integrations

Akira AI
Amazon EMR
Apache Bigtop
Apache Cassandra
Baidu Palo
Google Cloud Bigtable
IBM Analytics Engine
IBM Intelligent Operations Center for Emergency Mgmt
IBM SPSS Modeler
Inferyx
Mage Platform
NVMesh
Quantexa
Scalytics Connect
Style Intelligence
Unravel
Warp 10
Xtendlabs
doolytic
eQube®-DaaS

Integrations

Akira AI
Amazon EMR
Apache Bigtop
Apache Cassandra
Baidu Palo
Google Cloud Bigtable
IBM Analytics Engine
IBM Intelligent Operations Center for Emergency Mgmt
IBM SPSS Modeler
Inferyx
Mage Platform
NVMesh
Quantexa
Scalytics Connect
Style Intelligence
Unravel
Warp 10
Xtendlabs
doolytic
eQube®-DaaS
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Claim Apache Spark and update features and information
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