"融合批处理和流处理：解决挑战与实践"

In the article "Building a Unified Data Processing Engine Based on Streaming: Challenges and Practices," the authors discuss the importance and difficulties of unifying batch and stream processing in data engineering. They highlight the challenges faced by organizations in managing multiple processing engines, such as Apache Flink and Apache Spark, and emphasize the benefits of streamlining data processing with a unified engine. The authors point out that one of the main challenges in using separate batch and stream processing engines is the steep learning curve for developers who have to write and maintain code for two different systems. This not only increases complexity and maintenance costs but also hinders scalability and flexibility in data processing. By unifying batch and stream processing, organizations can simplify their data architecture, reduce operational costs, and improve performance and efficiency. A unified data processing engine allows for real-time data processing, easier integration with data sources, and better support for complex event processing and stream analytics. The authors also highlight the importance of adopting a unified approach to data processing in the context of the lambda architecture, which combines batch and stream processing to handle real-time and historical data analysis. This approach enables organizations to achieve faster insights, better decision-making, and more efficient resource utilization. In conclusion, the article emphasizes the significance of unifying batch and stream processing in data engineering to address the challenges of managing multiple processing engines and to leverage the benefits of real-time data processing. By adopting a unified data processing engine, organizations can streamline their data processing workflows, improve efficiency, and unlock new opportunities for data analysis and insights.