Big Data

 




Big Data

Big Data strategy and business analytics should encompass an evaluation of the decision-making processes of the organization as well as an evaluation on the groups and types of decision makers.The final goal of a Big Data project is not the collection of much data as possible but the support of the concrete business needs and provide new reliable information to decision makers; ? Only one technology cannot meet all the Big Data requirements. The presence of different workloads, data types, and user types should be served by the most suitable technology. For example, Hadoop could be the best choice for a large-scale Web log analysis but is not suitable for a real-time streaming at all. Multiple Big Data technologies must coexist and address use cases for which they are optimized.

Our Features

Information Privacy

Information privacy(or data protection), is the relationship between collection and dissemination of data, technology, the public expectation of privacy, and the legal and political issues surrounding them.Privacy concerns exist wherever personally identifiable information or other sensitive information is collected, stored, used, and finally destroyed or deleted.

Streaming Analytics

One of the biggest challenges for a DSMS is to handle potentially infinite data streams using a fixed amount of memory and no random access to the data.For the one hand, There are compression techniques that try to summarize the data and for the other hand there are window techniques that try to portion the data into (finite) parts.

Partitioning

A partition is a division of a logical database or its constituent elements into distinct independent parts. Database partitioning is normally done for manageability, performance or availability reasons, as for load balancing.Each partition may be spread over multiple nodes, and users at the node can perform local transactions on the partition.

Predictive Analytics

Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns.This analytics is often defined as predicting at a more detailed level of granularity,Technology that learns from experience to predict the future behavior of individuals in order to drive better decision.This distinguishes it from forecasting.

Graph Data Processing

Large scale graph analysis applications typically involve datasets of massive scale. Most of existing approaches address the iterative graph computation problem by programming and executing graph computation using either vertex centric or edge centric approaches. We develop a path-centric graph processing system PathGraph for fast iterative computation on large graphs with billions of edges.

Monitoring

Oracle database management tracks its computer data storage with the help of information stored in the SYSTEM tablespace. The SYSTEM tablespace contains the data dictionary, indexes and clusters. A data dictionary consists of a special collection of tables that contains information about all user-objects in the database.Monitoring ensuring that it performs optimally is an important task for a database administrator.