ASML, manufacturer of machines for the production of semiconductors, is implementing a central data lake to capture this data and make it accessible for reporting and analytics in a central environment. The data lake environment also includes an analytics lab for detailed exploration of data. In this session Jeroen Vermunt presents real-life examples of how ASML approaches the challenges of managing rapidly changing data.
Ensembling is one of the hottest techniques in today’s predictive analytics competitions. Every single recent winner of Kaggle.com and KDD competitions used an ensemble technique, including famous algorithms such as XGBoost and Random Forest. This session will provide a detailed overview of ensemble models, their origin, and show why they are so effective.
Migrating an existing data warehouse to the cloud is a complex process of moving schema, data, and ETL. The complexity increases when architectural modernization, restructuring of database schema or rebuilding of data pipelines is needed. In this session Dave Wells provides an overview of the benefits, techniques, and challenges when migrating an existing data warehouse to the cloud.
Business teams are raising the bar on Business Intelligence and Datawarehouse support. BI competence centers and data managers have to respond to expanding requirements: offer more data, more insight, maximal quality and accuracy, ensuring appropriate governance, etc. All to create guidance for enhancing their business.
Organizations worldwide are facing the challenge of effectively analyzing their exponentially growing data stores. Most data warehouses were designed before the big data explosion, and struggle to support modern workloads. To make due, many companies are cutting down on their data pipelines, severely limiting the productivity of data professionals.