SQL to NoSQL: Architecture Differences and Considerations for Migration

whitePaper | November 18, 2022

For decades, the lack of visibility into the health of our data has led to data downtime in companies around the world. When data is missing, inaccurate, or erroneous, data quality initiatives are doomed to fail, leaving enterprises at a privacy compliance and business disadvantage.

whitePaper | December 28, 2022

StreetLight Data, Inc. (“StreetLight”) pioneered the use of Big Data analytics to shed light on how people, goods, and services move, empowering smarter, data-driven transportation decisions. StreetLight's proprietary data processing engine, Route Science® algorithmically transforms its vast data resources to measure travel patterns of vehicles, bicycles and pedestrians, accessible as analytics on the StreetLight InSight® SaaS platform. StreetLight provides innovative digital solutions to help communities reduce congestion, improve safe and equitable transportation, and maximize the positive impact of infrastructure investment.

whitePaper | September 1, 2022

Artificial Intelligence (AI) is not a recently discovered field. Since the beginning of the computer science discipline, in the late 1950s, AI has drawn a lot of attention in the international scientific community and since then it has represented a field of study which has triggered diverse and numerous research activities.

whitePaper | March 12, 2020

Operational Acceptance Testing (OAT) is the penultimate phase of Acceptance Testing in a Software Testing Life cycle and is the last defence line between a software development project and deployment of software on production. FFor decedes, Operational Acceptance has been undermined and misunderstood. Where User Acceptance has been written about and hailed as a final phase in testing before production. User Acceptance is but one side of the coin, Operational Acceptance is the other.

whitePaper | September 1, 2022

Organizations often adopt a hybrid cloud strategy when deploying their first analytical solution to the cloud. The cloud combines sheer infinite scalability with consumption-based pricing. These are desirable attributes for analytical environments that require scalability and can be very expensive when scaled for maximum capacity over an extended period.

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