If data were equated to temperature in physical analogy, according to the second law of thermodynamics its accumulation would lead to the heat death of the universe. In fact, 90% of the world’s data has been generated during the last two years. Fortunately, business intelligence (BI) architectures and technologies are evolving rapidly to keep up with the exponential rise of data and project complexity. An almost 30-year era of dominance for the relational database management system (RDBMS) ended, and there is no one-size-fits-all solution that solves all of today’s Big Data challenges. BI architecture drivers must change to satisfy new requirements in format, volume, latency, hosting, analysis, reporting, and visualization. In this session, we expose a number of reference architectures that address these challenges and speed up the design and implementation process, making it more predictable and economical:
- Traditional architecture based on an RDMBS data warehouse but modernized with column-based storage to handle a high load and capacity
- NoSQL-based architectures that address Big Data batch and stream-based processing and use popular NoSQL and complex event-processing solutions
- Hybrid architecture that combines traditional and NoSQL approaches to achieve completeness that would not be possible with either alone
The architectures are accompanied by real-life projects and case studies that the presenters have performed for multiple companies, including Fortune 100 and start-ups.