Apache Spark

Apache Spark

Apache Software Foundation
Ray

Ray

Anyscale
+

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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

Dask is open source and freely available. It is developed in coordination with other community projects like NumPy, pandas, and scikit-learn. Dask uses existing Python APIs and data structures to make it easy to switch between NumPy, pandas, scikit-learn to their Dask-powered equivalents. Dask's schedulers scale to thousand-node clusters and its algorithms have been tested on some of the largest supercomputers in the world. But you don't need a massive cluster to get started. Dask ships with schedulers designed for use on personal machines. Many people use Dask today to scale computations on their laptop, using multiple cores for computation and their disk for excess storage. Dask exposes lower-level APIs letting you build custom systems for in-house applications. This helps open source leaders parallelize their own packages and helps business leaders scale custom business logic.

About

Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

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

Enterprises requiring a solution that provides advanced parallelism for analytics, enabling performance at scale

Audience

ML and AI Engineers, Software Developers

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Pricing

Free
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

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

Training

Documentation
Webinars
Live Online
In Person

Company Information

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

Company Information

Dask
Founded: 2019
dask.org

Company Information

Anyscale
Founded: 2019
United States
ray.io

Alternatives

dbt

dbt

dbt Labs

Alternatives

Alternatives

Ray

Ray

Anyscale
AWS Glue

AWS Glue

Amazon
MLlib

MLlib

Apache Software Foundation

Categories

Categories

Categories

Streaming Analytics Features

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

Integrations

Flyte
Union Cloud
Amazon EKS
Dagster
Hadoop
IBM Analytics Engine
IBM watsonx.data integration
Kestra
Kylo
Metabase
PHEMI Health DataLab
Riak TS
Saturn Cloud
SingleStore
Spark NLP
TiMi
Timbr.ai
Tonic Ephemeral
Warp 10
lakeFS

Integrations

Flyte
Union Cloud
Amazon EKS
Dagster
Hadoop
IBM Analytics Engine
IBM watsonx.data integration
Kestra
Kylo
Metabase
PHEMI Health DataLab
Riak TS
Saturn Cloud
SingleStore
Spark NLP
TiMi
Timbr.ai
Tonic Ephemeral
Warp 10
lakeFS

Integrations

Flyte
Union Cloud
Amazon EKS
Dagster
Hadoop
IBM Analytics Engine
IBM watsonx.data integration
Kestra
Kylo
Metabase
PHEMI Health DataLab
Riak TS
Saturn Cloud
SingleStore
Spark NLP
TiMi
Timbr.ai
Tonic Ephemeral
Warp 10
lakeFS
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