The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Storing data at rest for reporting and analytics requires different capabilities and SLAs than continuously processing data in motion for real-time workloads. Many open-source frameworks, commercial products, and SaaS cloud services exist. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. 

The Value of Data: Transactional vs. Analytical Workloads

The last decade offered many articles, blogs, and presentations about data becoming the new oil. Today, nobody questions that data-driven business processes change the world and enable innovation across industries.

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