Parallel Processing Makes All The Difference: MapReduce
Storing massive amounts of data across nodes with replication and fail over can be difficult with traditional SQL based solutions. Using MapReduce and other YARN-based systems for parallel processing of large data sets, USD implements networks capable of handling trillions of files that are distributed across geographic nodes for efficient serving.
With Apache Hadoop we leverage an open-source framework for file storage and advanced processing of big data across distributed environments, connecting with both .NET and Java based systems.
I/O Efficencies With Querying And Queueing: Solr And Spark
Storing massive amounts of data is just the start with Big Data… to efficiently visualize that data it needs to be queried via an engine that is capable of handling the nature of both in-file searching and NoSQL data.
Using advanced DAG execution engines that supports cyclic data flow and in-memory computing like Apache Spark and query engines that are highly reliable, scaleable and fault tolerant like Apache Solr, we create fast operating systems that process massive amounts of data.
The Rise Of The Machines: Intelligent Learning With Mahout
As humans, we’re limited by our perception and natural biases which lead us to assume correlations and causality. To overcome these tendencies, USD implements objective artificial intelligence engines to analyze data into “neighborhoods” which lead to “trends” and “taste” preferences.
Using Apache Mahout and our library of distributed and scaleable machine learning algorithms – we implement engines that find hidden insights through collaborative filtering, clustering and classification.
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Some people think design means how it looks. But of course, if you dig deeper, it's really how it works.