Getting insights out of Hadoop in a timely manner requires a different approach. You need in-memory analytics and an interactive, end-to-end process – data preparation, exploration, modeling and deployment. For precise answers instantly.
Delve deep into Hadoop for fast, accurate insights.
Apply proven state-of-the-art statistical algorithms and machine-learning techniques to find the best answers. You can explore and use multiple analytic approaches to reveal insights and make fact-based decisions.
Increase productivity for your data scientists.
Multiple users can concurrently and interactively analyze big data in Hadoop using the fast, in-memory analytical programming language. Prepare, manipulate, transform, explore, model, access and score data – all within Hadoop.
Take advantage of an end-to-end, scalable environment.
Until now, statisticians and data scientists had to piece together different programming languages or products to manage the variety of analytical lifecycle tasks in Hadoop. And when it came time to operationalize models, the software couldn't scale. No more. From data management and exploration to model building and deployment, our solution is proven, tested and accurate – and can scale to your production environment.
Avoid unnecessary, multiple passes through the data.
Our in-memory infrastructure running on top of Hadoop 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. Extensive data management capabilities make it easy for you to quickly prep data before analysis, which can typically take up to 80 percent of an analytic project's implementation time.
- Interactive programming. Move through the entire analytical life cycle in Hadoop with an extremely fast, multi-user environment.
- In-memory analytical processing. Get fast analytic computations that are optimized for multiple passes across distributed clusters.
- Persists data in-memory. Gain speed and reduce latency because data is held in-memory.
- Analytical data management. Prepare data for modeling with data integration, variable transformations and creation, and exploratory analysis.
- Model development. Quickly create, evaluate and compare multiple statistical models.
- Statistical algorithms and machine-learning techniques. Uncover patterns and trends faster than ever before with a huge breadth and depth of analytical techniques.
- Text analytics. Analyze your unstructured (and structured) data using a wide range of text analysis techniques.
- Recommendation system. Generate personalized, meaningful recommendations in real time with a high level of customization.