Technical Guide: Unleashing the Power of Hadoop with Informatica

whitePaper | September 12, 2022

This whitepaper examines clinical trial data contribution policies and the data protection methodologies applied to protect patient privacy. Information published by 29 biopharma sponsors was collected across three data-sharing platforms, collated by sponsor size. Results showed that large sponsor contribution policies can provide helpful benchmarks for medium and smaller sponsors.

whitePaper | December 22, 2022

The key objective of setting up data mesh or data fabric architecture is to enable the availability of quality data in a timely fashion to the right people in the right format. A data fabric is an architecture framework and a set of data services that provide frictionless data capabilities across a choice of endpoint applications or services spanning hybrid or multi-cloud and on-premises, by using rich metadata foundation and artificial intelligence/machine learning (AI/ML) automation.

whitePaper | July 25, 2022

Experts estimate that the average person generates more than 1.7 MB of digital data per second, amounting to over 2.5 quintillion bytes per day. However, as the world becomes increasingly digitized and networked, experts predict that, on average, people will produce 463 exabytes of data per day by 2025.

whitePaper | December 21, 2022

The evolution of cloud-based technology is continuing to reshape the data management landscape. As data volumes increase, users in different industries are demanding flexible, scalable processes that enable them to leverage a wider variety of sources, uncover new insights and confidently make decisions that will drive their businesses forward.

whitePaper | July 8, 2022

This series of white papers lays out the essential steps you need to take to ensure your business stays at the forefront of an increasingly data-driven and automated world. We cover everything you need to know about growing a data science team, the current trends in machine learning, data visualisation, essential data science tools and technologies, automation, deployment, how to prove value with data science and also give our thoughts on the next ‘big ideas’ that are set to take the world of AI in business by storm.

whitePaper | May 27, 2021

In many organizations, we need more collaboration between businesses, analytic teams, application developers and IT operations. These teams often work with data in silos and end up duplicating efforts, failing to integrate, or missing opportunities to deliver value from data. In addition to siloed efforts, data scientists are also faced with ever-increasing volumes and speeds of data. And the reality is they’re expected to answer questions just as fast as - or faster than - before. It’s important that we’re using the right data and the right techniques to ensure optimal outcomes. Download this guide to learn more about SAS and open source.