By Aileen Agricola | January 3, 2019
Excerpt of article published on Analytics India.
How Graphic Analytics Works With Hadoop
Apache Hadoop has been challenged by Google when they brought their own framework called Dataflow, a cloud-based system which does real-time data analysis. According to reports, Hadoop lacks abstraction and encryption at storage and network levels. Graphic analytics techniques could easily help Hadoop analyse the data systematically.
One of the examples of graph storage and processing is a Neo4J database system. This platform is an open-source graph database, which is also developed using Java. Some of the advantages of Neo4J are it has a flexible model, the real-time insights which aren’t available on Hadoop and easy retrieval of data.
Hadoop has several limitations due to which Apache Spark and Flink came into the market. These include lengthy lines of code, issues with small files, no real-time data processing, no security and slow processing speed. These flaws make Hadoop unfit for enterprise data processing. To overcome this, Spark used in-memory processing of data, which increased processing speed. Graph analytics can work on a platform and store data in a suitable and convenient format for the user. It increases intra-cluster similarity and has applications ranging over machine learning, image processing and tracing weak spots in the data. It can also be used for traffic analysis, social network analysis etc.
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