Make the Most of Graph Databases Through Interactive Analytics

That’s where the graph engine is doing the analytical process in a fundamentally different and better way.” Graph Databases Compared to other approaches for dealing with Big Data, Zane remarked that “it turns out that graph is a much simpler and more flexible model than what you see with relational – let alone the Hadoop model.” If answering questions in a batch relational model takes 24 hours, Hadoop puts it a step even farther away, he said..The ability to ask a question and get an answer is usually measured in the seconds range..Zane believes that one of the reasons the use of graph is showing so much growth is that it’s closer to how people think..“People don’t really think in terms of rectangular tables that link to each other..They think in terms of relationships.” Rueter sees the potential promise of lower storage costs with Hadoop as a driver toward its popularity, “but Hadoop alone still doesn’t get at the issue of what you want to do with your data.” Deeper analysis, iterative questioning processes, and creating context for data require additional tools..“The potential is there with Hadoop, but graph really delivers on that promise.” The roadblock to widespread adoption of graph has been the performance needs of Big Data operations..Zane said there were similar concerns about relational databases until technology was developed to address speed issues..“That’s why we got into this space, because the people involved had deep experience in getting very high performance out of relational.” By addressing large-scale performance issues, he saw a way to bring graph into the mainstream, as had been done with relational..“Anything is going to perform with a million pieces of data, but when you go to a trillion pieces of data, that really requires a design that leverages parallel computing.” Zane added that they benchmarked a trillion almost two years ago..Advancements in OLTP and OLAP Zane noted that there are now places where customer data is not just being collected and retained by a company, but also strategically used to provide a better, more holistic customer experience..“We’re now seeing the data go in the other direction,” back to the customer to encourage buying behavior, or to assist in a sale..Amazon, a leader in the Big Data space, uses information gleaned from other customers’ buying habits to inform suggestions such as “customers who bought this item also bought this item,” or “80 percent of users who did this same search bought this item,” said Zane.. More details

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