Select Page

WhereScape Highly Commended at 2016 AmCham Success & Innovation Awards.

By WhereScape
| August 19, 2016

The American Chamber of Commerce celebrated success and innovation in the export, import and investment sectors between New Zealand and the USA at the 2016 AmCham – DHL Express Annual Success & Innovation Awards.

WhereScape has been awarded Highly Commended in the Exporter of the Year to the USA over $10 million category. We are honoured to be recognised in this way.

The Awards offer an opportunity to showcase the imagination, innovation and entrepreneurship demonstrated by the companies doing business with the USA.

This year AmCham has seen a very strong group of entries, and it was great to see the ever-growing ties between the US and New Zealand businesses.

Enterprise Data Warehouse Guide: Architecture, Costs and Deployment

TL;DR: Enterprise data warehouses centralize business data for analysis, but most implementations run over budget and timeline while requiring specialized talent. They unify reporting across departments and enable self-service analytics, yet the technical complexity...

What Is a Data Vault? A Complete Guide for Data Leaders

A data vault is a data modeling methodology designed to handle rapidly changing source systems, complex data relationships, and strict audit requirements that traditional data warehouses struggle to manage.  Unlike conventional approaches that require extensive...

New in 3D 9.0.6.1: The ‘Source Aware’ Release

When your sources shift beneath you, the fastest teams adapt at the metadata layer. WhereScape 3D 9.0.6.1 focuses on precisely that: making your modeling, conversion rules and catalog imports more aware of where data comes from and how it should be treated in-flight....

Data Vault on Snowflake: The What, Why & How?

Modern data teams need a warehouse design that embraces change. Data Vault, especially Data Vault 2.0, offers a way to integrate many sources rapidly while preserving history and auditability. Snowflake, with elastic compute and fully managed services, provides an...

Data Vault 2.0: What Changed and Why It Matters for Data Teams

Data Vault 2.0 emerged from years of production implementations, codifying the patterns that consistently delivered results. Dan Linstedt released the original Data Vault specification in 2000. The hub-link-satellite modeling approach solved a real problem: how do you...

Building an AI Data Warehouse: Using Automation to Scale

The AI data warehouse is emerging as the definitive foundation of modern data infrastructure. This is all driven by the rise of artificial intelligence. More and more organizations are rushing to make use of what AI can do. In a survey run by Hostinger, around 78% of...

Data Vault Modeling: Building Scalable, Auditable Data Warehouses

Data Vault modeling enables teams to manage large, rapidly changing data without compromising structure or performance. It combines normalized storage with dimensional access, often by building star or snowflake marts on top, supporting accurate lineage and audit...

Building a Data Warehouse: Steps, Architecture, and Automation

Building a data warehouse is one of the most meaningful steps teams can take to bring clarity and control to their data. It’s how raw, scattered information turns into something actionable — a single, trustworthy source of truth that drives reporting, analytics, and...

Related Content

What Is a Data Vault? A Complete Guide for Data Leaders

What Is a Data Vault? A Complete Guide for Data Leaders

A data vault is a data modeling methodology designed to handle rapidly changing source systems, complex data relationships, and strict audit requirements that traditional data warehouses struggle to manage.  Unlike conventional approaches that require extensive...