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About

Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets. We are happy to receive feedback and contributions. Deequ depends on Java 8. Deequ version 2.x only runs with Spark 3.1, and vice versa. If you rely on a previous Spark version, please use a Deequ 1.x version (legacy version is maintained in legacy-spark-3.0 branch). We provide legacy releases compatible with Apache Spark versions 2.2.x to 3.0.x. The Spark 2.2.x and 2.3.x releases depend on Scala 2.11 and the Spark 2.4.x, 3.0.x, and 3.1.x releases depend on Scala 2.12. Deequ's purpose is to "unit-test" data to find errors early, before the data gets fed to consuming systems or machine learning algorithms. In the following, we will walk you through a toy example to showcase the most basic usage of our library.

About

Muse Spark 1.1 is a multimodal reasoning model from Meta Superintelligence Labs built for agentic tasks, coding, computer use, tool use, and multimodal understanding. The model improves on the original Muse Spark with stronger performance in planning, orchestration, long-context work, coding workflows, and external app interactions. Muse Spark 1.1 can manage a 1 million token context window, remember earlier actions, retrieve important information, compact context, and delegate tasks across parallel subagents. It is designed to operate across tools, MCP servers, custom skills, browsers, native apps, scripts, images, video, PDFs, and audio-based workflows. Developers can access Muse Spark 1.1 through the new Meta Model API public preview, while users can try it in Thinking mode in the Meta AI app and on meta.ai.

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

Anyone looking for an Unit Testing solution that measures data quality in large datasets

Audience

Muse Spark 1.1 is best suited for developers, AI engineers, enterprises, agent builders, coding tool teams, research teams, and productivity-focused users that need a multimodal reasoning model for agentic workflows, coding, computer use, long-context tasks, tool orchestration, and advanced automation

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

$1.25 per 1M tokens (input)
$1.25 per million tokens in input, and $4.25 per million tokens of output
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 5.0 / 5
ease 5.0 / 5
features 4.0 / 5
design 5.0 / 5
support 5.0 / 5

Pros & Cons from Real Users

Pros

  • Muse Spark 1.1 has been awesome for the way I actually build with AI: agents, coding workflows, tool calls, debugging loops, and messy real-world tasks that do not fit neatly into a single prompt. It feels much stronger than the first version when I need it to reason through code, work across multiple steps, understand context, and keep an agent moving without constantly falling apart. The multimodal side is also a big plus because being able to work with docs, screenshots, images, and other inputs makes it way more useful for building practical AI products.

Cons

  • It is still early, so I would not call it perfect yet. Like any advanced model, you still need good scaffolding, evals, guardrails, and monitoring if you are putting it into production agent workflows. I also want to see the API ecosystem, docs, examples, and integration patterns mature more, because those things matter a lot when you are building real agentic systems instead of just testing prompts.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Deequ
github.com/awslabs/deequ

Company Information

Meta
Founded: 2004
United States
meta.ai

Alternatives

Spark Streaming

Spark Streaming

Apache Software Foundation

Alternatives

Claude Opus 5

Claude Opus 5

Anthropic
Claude Fable 5

Claude Fable 5

Anthropic
Apache Spark

Apache Spark

Apache Software Foundation
Claude Mythos 5

Claude Mythos 5

Anthropic
MLlib

MLlib

Apache Software Foundation
Apache Mahout

Apache Mahout

Apache Software Foundation

Categories

Categories

Integrations

Apache Spark
C++
Claude Agent SDK
Claude Code
Facebook
Facebook Messenger
Gray Swan
HTML
Java
Kotlin
Kubernetes
Meta AI
Muse Image
Muse Spark
Odysseus
OpenCode
Solidity
Swift
TypeScript
XML

Integrations

Apache Spark
C++
Claude Agent SDK
Claude Code
Facebook
Facebook Messenger
Gray Swan
HTML
Java
Kotlin
Kubernetes
Meta AI
Muse Image
Muse Spark
Odysseus
OpenCode
Solidity
Swift
TypeScript
XML
Claim Deequ and update features and information
Claim Deequ and update features and information
Claim Muse Spark 1.1 and update features and information
Claim Muse Spark 1.1 and update features and information