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Big Data Databases & Data Warehousing

Data Science Technologies can integrate various open source or variant technologies to your existing platform. Our resources have the expertise to integrate existing structured databases to Big Data Databases and Data Warehousing tools. We have custom developed methodologies to deliver to customers and our design needs to best exploit the power of the tool and the integrated hardware. Some of technologies we actively work for customers are:

Redis

Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.

Hive

The Apache Hive ™ data warehouse software facilitates querying and managing large datasets residing in distributed storage. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. At the same time, this language also allows traditional map and reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL.

Apache Hbase

Apache HBase™ is the Hadoop database – a distributed, scalable, big data store. Use Apache HBase when you need random, realtime read/write access to your Big Data. This project’s goal is the hosting of very large tables — billions of rows X millions of columns — atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google’s Bigtable: A Distributed Storage System for Structured Data by Chang; et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.

Infobright

Infobright’s high performance analytic database is designed for analyzing large volumes of machine-generated data – without IT intervention. Easy to implement and manage, Infobright provides the answers your business users need at a price you can afford.

 

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