Big data solutions requires avant-garde technologies to proficiently process large volumes of data, emanating from multiple source in quick time. At [x]cube DATA, we have extensive experience in several Big Data technologies and techniques to help organizations leverage their available data to obtain actionable insights.
NoSQL is fast emerging as a popular choice for storing Big Data. Enterprises have both structured and unstructured data to store and analyze. NoSQL databases like Mongo DB, Cassandra, redis, memcached enable storing and sorting unstructured and cluttered data with greater efficiency and scalability.
No data is too big with Hadoop. The open source platform provides a new, scalable and cost-effective facility to store and process huge amounts of data and is an excellent alternative over legacy systems. Hadoop is capable of handling all data types collected from multiple sources to derive actionable insights.
The aim of text mining or text data mining is to derive information from unstructured texts like open-ended survey responses, e-mails, messages etc., and make it compatible to various data mining algorithms. Tool kits like RDF, SPARQL and LingPipe etc., are highly efficient in processing text information.
Machine Learning- the building of systems that can learn from data and past experience is critical to big data as huge data sets are difficult to explore using conventional exploration methods. Apache Mahout, an open source machine learning library, enables recommendation engines, clustering, and classification of data sets.
Statistical Programming refers to computation techniques that assist in data analysis. Open source and commercial programming languages like R, MatLab and SAS etc., offers a wide variety of statistical and graphical techniques and performs complex data science swiftly and cost-effectively.