Modern Data Engineering for Cloud Etl, Migration, and Scalable Analytics

The ETL migration and scalable analytics of cloud-native design has revolutionized the current data engineering through the faster transition to cloud-native designs. The resource usage in cloud systems is Pay-as-you-use, which is convenient in real time and bulk processing of data demanded by the businesses. The innovations enable the agile pipelines, migration of legacy systems and analytics migration at scale migration. Nonetheless, they still struggle with such problems as the ability to integrate heterogeneous data, schema change, low-latency processing, regulatory compliance and cost control. These are the main issues that will have to be addressed in order to realize the potential of cloud data systems. This review is going to investigate the trends of cloud-native ETLs, tools and strategies such as the data lakehouses and streaming architecture. The interactions of the modern pipelines are shown through experimental modeling whereas the constraints of the system can be stated by the comparative benchmarks. The analysis also determines the most significant gaps and future opportunities like serverless orchestration, fed ETL, and combined use of batch-stream processing to enhance the interoperability, efficiency and scalability of cloud data engineering.

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