Why Multi-Cloud is Imperative to Any Modern Data Strategy

whitePaper | August 18, 2022

The 2022 data connectivity survey pivots somewhat from prior years’ studies. Data connectivity and data integration are well established in the traditional on-premise computing environment. But we’ve seen that as organizations increasingly expand their domains to one or more cloud service providers, it is important to better understand the challenges and opportunities for connectivity presented by modernization initiatives.

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.

whitePaper | June 3, 2021

Data growth continues at an exponential rate even as cloud architectures make data management more complex and advanced applications necessitate more data movement. So what can be done to enable clean data capture and movement across an enterprise? Read this white paper to learn the requirements for data orchestration at scale and discover how you can build a holistic data architecture that enables successful DataOps.

whitePaper | March 12, 2020

This point of view is an exploration of the possibilities engendered by rethinking the role of data scientists in the wake of industrial revolution. It might be claimed that current trends in industrial revolution reflect a paradigm shift towards data centric processing with data science playing an increasingly critical role. This point of view also explicitly highlights the potential role of Data scientists as an emerging phenomenon, and then to show some of the benefits that this role can bring as we move towards industrial disruption

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.

whitePaper | December 13, 2022

Modern Data Analytics Platforms (DAP) have gone through rapid architectural pattern changes over the past few years, independent of the use cases, to provide maximum benefits to the consumers.