SAS® In-Memory Statistics

A single, interactive programming environment for analytics.

Yes, you can get prompt insights out of big data. With in-memory analytics and interactive, analytical data preparation, exploration, modeling and deployment, you can get precise answers instantly.


Delve deeply into big data for fast, accurate insights.

Proven, state-of-the-art statistical algorithms and machine-learning techniques deliver precise answers quickly. Use multiple analytic approaches to explore big data. And unearth insights for making high-impact decisions.

Give your data scientists a productivity boost.

A fast, in-memory analytical programming language allows multiple users to analyze big data simultaneously and interactively. Data scientists can prepare, manipulate, transform, explore, model, access and score data – from a single environment.

Take advantage of a scalable environment.

Accessing, preparing, modeling and scoring data has typically meant piecing together different programming languages or products. And such piecemeal solutions simply can't scale. But no more. From data preparation and exploration to model building and deployment, our solution is tested and proven – and scales to fit your production environment.

Stop multiple, unnecessary passes through the data.

Our in-memory infrastructure eliminates costly data movement and persists data in memory for the entire analytic session. This significantly reduces data latency and provides rapid analysis at lightning-fast speeds.

Cloudera Chief Technologist Eli Collins talks about the SAS solution for data scientists. And the benefits the SAS partnership brings to Hadoop ecosystems.

Demos & Screenshots


  • Interactive programming. Multiple users can analyze large amounts of data concurrently by interactively submitting SAS code through SAS Studio, a web browser interface.
  • In-memory analytical processing. Fast analytic computations are optimized for a distributed, multithreaded and scalable in-memory environment for high-value, real-time decision making.
  • In-memory data persistence. Data is held in memory to boost speed and reduce latency.
  • Analytical data preparation. Includes capabilities for accessing and manipulating data, transforming and creating variables, and performing exploratory analysis.
  • Model development. Enables fast creation, evaluation and comparison of multiple statistical models.
  • Statistical algorithms and machine-learning techniques. A huge breadth and depth of analytical techniques can uncover patterns and trends faster than ever before.
  • Text analytics. A wide range of text analysis techniques lets you analyze unstructured – as well as structured – data.
  • Recommendation system. Personalized, meaningful recommendations are generated in real time with a high level of customization.

    Technical Information

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