Amazon EMR

Amazon EMR

Amazon
Spark Streaming

Spark Streaming

Apache Software Foundation
+
+

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About

Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. With EMR you can run Petabyte-scale analysis at less than half of the cost of traditional on-premises solutions and over 3x faster than standard Apache Spark. For short-running jobs, you can spin up and spin down clusters and pay per second for the instances used. For long-running workloads, you can create highly available clusters that automatically scale to meet demand. If you have existing on-premises deployments of open-source tools such as Apache Spark and Apache Hive, you can also run EMR clusters on AWS Outposts. Analyze data using open-source ML frameworks such as Apache Spark MLlib, TensorFlow, and Apache MXNet. Connect to Amazon SageMaker Studio for large-scale model training, analysis, and reporting.

About

Spark Streaming brings Apache Spark's language-integrated API to stream processing, letting you write streaming jobs the same way you write batch jobs. It supports Java, Scala and Python. Spark Streaming recovers both lost work and operator state (e.g. sliding windows) out of the box, without any extra code on your part. By running on Spark, Spark Streaming lets you reuse the same code for batch processing, join streams against historical data, or run ad-hoc queries on stream state. Build powerful interactive applications, not just analytics. Spark Streaming is developed as part of Apache Spark. It thus gets tested and updated with each Spark release. You can run Spark Streaming on Spark's standalone cluster mode or other supported cluster resource managers. It also includes a local run mode for development. In production, Spark Streaming uses ZooKeeper and HDFS for high availability.

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

Companies that want to easily run and scale Apache Spark, Hive, Presto, and other big data frameworks

Audience

Real-Time Data Streaming solution for businesses

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

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

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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

Amazon
Founded: 1994
United States
aws.amazon.com/emr/

Company Information

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

Alternatives

Alternatives

ksqlDB

ksqlDB

Confluent
Samza

Samza

Apache Software Foundation
Apache Spark

Apache Spark

Apache Software Foundation
E-MapReduce

E-MapReduce

Alibaba
Apache Spark

Apache Spark

Apache Software Foundation
MLlib

MLlib

Apache Software Foundation

Categories

Categories

Integrations

Apache Spark
Amazon SageMaker Studio
Data Virtuality
EC2 Spot
Feast
Gurucul
IBM watsonx.data integration
New Relic
Pepperdata
Presto
PubSub+ Platform
Sifflet
Tecton
Tonic Ephemeral
TrustLogix
Unravel
Veza
Zepl

Integrations

Apache Spark
Amazon SageMaker Studio
Data Virtuality
EC2 Spot
Feast
Gurucul
IBM watsonx.data integration
New Relic
Pepperdata
Presto
PubSub+ Platform
Sifflet
Tecton
Tonic Ephemeral
TrustLogix
Unravel
Veza
Zepl
Claim Amazon EMR and update features and information
Claim Amazon EMR and update features and information
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