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How Microsoft Fabric Lakehouse Complements Data Streaming (Apache Kafka, Flink, et al.)
In today’s data-driven world, understanding data at rest versus data in motion is crucial for businesses. Data streaming frameworks like Apache Kafka and Apache Flink enable real-time data processing, offering quick insights and seamless system integration. They are ideal for applications that require immediate responses and handle transactional workloads. Meanwhile, lakehouses like Snowflake, Databricks, and Microsoft Fabric excel in long-term data storage and detailed analysis, perfect for reports and AI training. By leveraging both data streaming and lakehouse systems, businesses can effectively meet both short-term and long-term data needs. This blog post delves into how these technologies complement each other in enterprise architecture.
This is part two of a blog series about Microsoft Fabric and its relation to other data platforms on the Azure cloud:
- What is Microsoft Fabric for Azure Cloud (Beyond the Buzz) and how it Competes with Snowflake and Databricks
- How Microsoft Fabric Lakehouse Complements Data Streaming (Apache Kafka, Flink, et al.)
- When to Choose Apache Kafka vs. Azure Event Hubs vs. Confluent Cloud for a Microsoft Fabric Lakehouse