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. Let's explore this dilemma in a blog series. Learn how to build a modern data stack with cloud-native technologies. This is part 5: Best Practices for Building a Cloud-Native Data Warehouse or Data Lake.
Blog Series: Data Warehouse vs. Data Lake vs. Data Streaming
This blog series explores concepts, features, and trade-offs of a modern data stack using a data warehouse, data lake, and data streaming together: