Neo Technology recently announced that retail giants such as eBay and Walmart are using graph database Neo4j in production-grade applications that improve their operations and marketing analytics. In a recently published case study, Neo Technology revealed how eBay’s e-commerce technology platform acquisition, Shutl, leverages Neo4j to expedite delivery to the point where customers can enjoy same day delivery in select cases. Shutl constitutes the technology platform that undergirds eBay Now, a service that delivers products in 1-2 hours from local stores by means of relationships between couriers and stores. eBay decided to make the transition from MySQL to Neo4j because:
Its previous MySQL solution was too slow and complex to maintain, and the queries used to calculate the best route additionally took too long. The eBay development team knew that a graph database could be added to the existing SOA and services structure to solve the performance and scalability challenges. The team turned to Neo4j as the best possible solution on the market.
According to Volker Pacher, Senior Developer at eBay, eBay found that Neo4j enabled dramatic improvements in its computational and querying ability:
We found Neo4j to be literally thousands of times faster than our prior MySQL solution, with queries that require 10-100 times less code. Today, Neo4j provides eBay with functionality that was previously impossible.
eBay’s current ecommerce technology platform leverages Ruby, Sinatra, MongoDB, and Neo4j. Importantly, queries “remain localized to their respective portions on the graph” in order to ensure scalability and performance. Walmart, meanwhile, uses Neo4j to understand the online habits of its shoppers in order to deliver more relevant real-time product recommendations for their online shoppers. Neo4j’s adoption by eBay and Walmart symptomatically illustrates how graph databases are disrupting the nature of real-time analytics, a trend further underscored by Pivotal HD 2.0’s integration of GraphLab into its offerings, and the use of graphing technologies by startups such as Aorato.
DataRPM today announced the finalization of $5.1M in Series A funding in a round led by InterWest Partners. DataRPM specializes in a next generation business intelligence platform that leverages machine learning and artificial intelligence to facilitate the delivery of actionable business intelligence by means of a natural language-based search engine that allows customers to dispense with complex, time consuming data modeling and query production. DataRPM stores customer data within a “distributed computational search index” that enables its platform to apply its natural language query interface to heterogeneous data sources without modeling the data into intricate taxonomic relationships or master data management frameworks. Because DataRPM’s distributed computational search index empowers customers to run queries against different data sources without constructing data schemas that organize the constituent data fields and their relationships, it promises to accelerate the speed with which customers can derive insights from their data. Not only does the platform deliver a natural language interface, but it also performs data visualization of the requisite Google-like searches as illustrated below:
In an interview with Cloud Computing Today, DataRPM CEO Sundeep Sanghavi noted that its natural language search functionality is based on proprietary graphing technology analogous to Apache Giraph and Neo4j. The platform operates on data in relational and non-relational formats, although it currently does not support unstructured data. Available via both a cloud-based and on-premise deployment solution, DataRPM promises to disrupt Big Data analytics and contemporary business intelligence platforms by dispensing with the need for complex, time consuming and expensive data modeling as well as empowering business stakeholders with neither SQL nor scripting skills to analyze data. Today’s funding raise is intended to accelerate the company’s go-to-market strategy and correspondingly support product development in conjunction with the platform’s reception by current and future customers.
DataRPM belongs to the rapidly growing space of products that expedite Big Data analytics on Hadoop clusters as exemplified by the constellation of SQL-like interfaces for querying Hadoop-based data. That said, its natural language query interface represents a genuine innovation in a space dominated by products that render Hadoop accessible to SQL developers and analysts, as opposed to data savvy stakeholders with Google-like querying expertise. Moreover, DataRPM’s natural language search capabilities push the envelope of “next generation business intelligence” even further than contemporaries such as Jaspersoft, Talend and Pentaho, which thus far have focused largely on the transition within the enterprise from reporting to analytics and data discovery. Expect to hear more about DataRPM as the battle to streamline and simplify the derivation of actionable business intelligence from Big Data takes shape within a vendor landscape marked by the proliferation of analytic interfaces for petabyte-scale relational and non-relational databases.