In the face of massive

heterogeneous and fast growing big data of power distribution and utilization

how to apply big data technology to improve the breadth

depth

and accuracy of power distribution and utilization business becomes a new opportunity and challenge in power industry.In order to solve the two major problems of big data with the multi-source integration and efficient storage

according to the composition and characteristics of big data of power distribution and utilization

we adopt standardized metadata and corresponding data dictionaries to realize the standardized integration of multi-source data of power distribution and utilization.On the basis of data integration

the optimization method of big data storage is studied based on the Hadoop platform.A Hash bucket algorithm considering data correlation is proposed.The algorithm realizes the centralized storage of related data

so as to enhance the efficiency of data query and processing.On the basis of data storage optimization

the parallel association query for multi-source big data of power distribution and utilization based on Map Reduce is realized.Tests on a Hadoop platform show that

after optimization of hash bucket storage

the time of the multi-source data parallel association query is significantly shortened than traditional Hadoop method.