Category: Data Warehouse Automation

Data management can be a daunting task. It requires significant time and resources to collect, process, and analyze large volumes of information. To address these challenges, approximately 44% of companies are planning to invest in artificial intelligence (AI) to streamline…

In the world of medical services, large volumes of healthcare data are generated every day. Currently, around 30% of the world’s data is produced by the healthcare industry and this percentage is expected to reach 35% by 2025.   The sheer…

As data warehousing technologies continue to grow in demand, creating effective data models has become increasingly important. In particular, optimizing online transactional processing (OLTP) data models—which define the structural relationships between normalized tables and entities—for efficient transactional processing is crucial.  However,…

Well-planned and properly executed data preparation influences analysis outcomes and impacts downstream applications. When performed correctly, data preparation or preprocessing leads to high-quality, reliable data that turns into insights you can count on. Whitepaper Home / Whitepapers / 2023 Game…

Dimensional modeling is still the most reliable modeling approach for designing a data warehouse for reporting use cases. Its denormalized structure significantly improves query performance, allowing for fast and seamless data consumption and reporting. The dimensions in a model-centric data…