Home > Resources > Whitepapers > Graph Analytics and Big Data

Graph Analytics and Big Data

May 2, 2019

Graph analytics, which is an analytics alternative that uses an abstraction called a graph model. The simplicity of this model allows for rapidly absorbing and connecting large volumes of data from many sources in ways that finesse limitations of the source structures (or lack thereof, of course). Graph analytics is an alternative to the traditional data warehouse model as a framework for absorbing both structured and unstructured data from various sources to enable analysts to probe the data in an undirected manner. Big data analytics systems should enable a platform that can support different analytics techniques that can be adapted in ways that help solve a variety of challenging problems. This suggests that these systems are high performance, elastic distributed data environments that enable the use of creative algorithms to exploit variant modes of data management in ways that differ from the traditional batchoriented approach of traditional approaches to data warehousing

Spotlight

TransOrg Analytics

TransOrg is a niche player in ‘Analytics and Advisory’ space.TransOrg Analytics has created a scalable and cost effective integration of ‘Big Data’ and ‘Predictive Analytics. We provide fast, flexible, need-based and affordable solutions to help clients realize both short-term tactical and long-term strategic value. Our expertise lies in assimilating structured, unstructured, social media and text data to create real time 360-degree customer’s view. Our data scientists have creatively used open source technologies to develop a suite of productized services and industry centric proprietary, predictive and optimization models. TransOrg provides analytics solutions and services in the areas of marketing, supply chain and risk management. We work with clients across industries such as retail, consumer goods, healthcare, BFSI, telecom, travel and hospitality.

OTHER WHITEPAPERS

The Blueprint for a Data Governance Program

whitePaper | August 25, 2022

Most organizations realize data governance is necessary - if not indispensable - for mitigating data risk while sustaining its longterm value. However, there’s still a fair amount of ambiguity around what exactly data governance is and how to best implement it. By definition, data governance is an enterprise-wide initiative for formalizing the processes, people, and protocols for using data. It reduces risk while increasing data’s ultimate value: consistent reusability for achieving mission critical objectives. Data governance is a strategic approach for creating unassailable trust in data throughout the enterprise.

A blueprint for data-driven insights

whitePaper | August 20, 2021

While finding and creating insights can often feel like luck, there are proven methodologies you can use to ensure you’re seeing regular insight from your on-hand data. Access this short e-book to learn how you can start seeing regular, valuable insight success from you big data and analytics investments.

Future of care: Patient-centricity with real-world predictive analytics

whitePaper | February 8, 2023

For centuries, patients have sought medical help for their ailments. Just as in the past, however, there are still many illnesses – both wellknown, widespread diseases and rare conditions – that initially cause few or inconclusive symptoms, and many patients leave the doctor’s office with an incorrect diagnosis. In addition, diseases may progress slowly or quickly depending on the individual.

HPE Data Management Framework

whitePaper | December 8, 2022

HPE DMF v7 policies can be configured to migrate and store copies of both new and updated files in the capacity tier at specific intervals or after certain periods of file change inactivity. This effectively implements a high-quality, granular backup of a very large filesystem. DMF will automatically make backup copies of files and metadata on a rolling basis using policy definitions that can be tailored by administrators.

Better together: AVEVA™ Predictive Analytics and the AVEVA™ PI System™ maximize mining plant ROI

whitePaper | December 23, 2022

From volatile markets and tough competition to the increasingly stringent demands of government regulators and customers alike, there’s no shortage of challenges ahead for the mining industry. To navigate these obstacles and others all while improving profitability, mining companies are seeking innovative ways to optimize the reliability, efficiency, and safety of their operations. Many industry leaders are already finding the answers they are looking for in their operations data.

Introducing Apache Druid

whitePaper | January 31, 2020

Many companies have invested heavily in specialized enterprise data warehouses (EDW) and Extract, Transform and Load (ETL) technologies to analyze their operational data. But these technologies were never designed to be truly real-time.They were originally built for batch, and that original design limits how real-timeEDWs and ETL can become. They were also designed to support a focused group ofanalysts, not a larger group of employees spanning operational functions, or even partner and end customers.

Graph Analytics and Big Data