The leading document-oriented NoSQL database
MongoDB stores data using a flexible document data model based on JSON. Documents contain one or more fields, including arrays, binary data and sub-documents. Scalability is ensured by a sharding architecture that allows automatic distribution of selected collections to multiple servers.
nēodata consulting holds, from MongoDB, both the DBA and developer associate level certifications.
In partnering with nēodata consulting, you benefit from several years of experience in integrating and managing best of breed technologies and advanced computational methods. Here is some of our expertise.
An open-source Map Reduce ecosystem
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models, such as Map Reduce. Apache Hadoop's MapReduce and HDFS components were inspired by Google papers on their MapReduce and Google File System.
The leading column-oriented NoSQL database
Apache Cassandra, a top-level Apache project born at Facebook and built on Amazon’s Dynamo and Google’s BigTable, is a distributed storage system for managing very large amounts of structured data spread out across many commodity servers, while providing highly available service with no single point of failure.
Software Engineering and Project Management
With nēodata consulting, you have access to professionals that have consistent records of assembling high performance teams and introducing to market innovative software products for leading organizations. We have a deep understanding of software development and expertise in applying project management techniques to the software area, including SCRUM and agile approaches.
Fast and general engine for large-scale data processing
Apache Spark stems from the AMPLab at the University of California, Berkeley. It is in many regards a substitute for the Hadoop ecosystem, as it provides similar capabilites while seeking better component integration and increased performance through in-memory computing. Yet, it is highly complementary, as it is storage agnostic and thus can sit on top of Hadoop's persistence layer (HDFS, HBase, Hive) and integrate with YARN, Hadoop's cluster scheduling system.