Getting information out of Hadoop requires a different approach. The key is using in-memory analytics and an end-to-end process that includes everything from data preparation to analysis and model deployment. Analyzing huge volumes of data simultaneously by many users reveals the value hidden in Hadoop.
The key to getting relevant information from Hadoop is using in-memory analytics and an end-to-end process. One that includes everything from data preparation to analysis and model deployment. Analyzing huge volumes of data simultaneously by many users can reveal the value hidden in Hadoop. Faster than you think.
Delve deep into Hadoop to reveal 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 have had to piece together different programming languages or products to manage the variety of analytical lifecycle tasks in Hadoop. And when it can time to operationalize models, the software couldn't scale. No more. From data management and exploration and to model building and deployment, our solution is proven, tested and accurate – and can scale to your production environment.
Avoid unnecessary and expensive 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 also 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. Go from data management to model deployment with a flexible multi-user coding environment that covers the entire analytical life cycle.
- Highly scalable 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 preparation. Prepare data for modeling with data integration, variable transformations and creation, and exploratory analysis.
- 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 advanced text analysis techniques.
- Scalable recommendation systems. Develop recommendations in real time with an extensive set of in-memory machine-learning algorithms.