Data Engineering with A Focus on Scalable Platforms and Real-Time Analytics
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The book covers data engineering types and their integration with scalable infrastructure and real-time analytics. It also shows how data engineering has evolved from traditional ETL processes to cloud-based distributed infrastructures and data ingestion, transformation, orchestration, and governance. It gives details of the roles and responsibilities of a data engineer, the role of big data technologies like Apache Kafka, Spark and cloud solutions, and the importance of data quality, security, and compliance. Moreover, it also considered design scalability in the data platform paradigms, comp...
The book covers data engineering types and their integration with scalable infrastructure and real-time analytics. It also shows how data engineering has evolved from traditional ETL processes to cloud-based distributed infrastructures and data ingestion, transformation, orchestration, and governance. It gives details of the roles and responsibilities of a data engineer, the role of big data technologies like Apache Kafka, Spark and cloud solutions, and the importance of data quality, security, and compliance. Moreover, it also considered design scalability in the data platform paradigms, comparing SQL and NoSQL databases and the dictionary of the best practices of building resiliency, and fault tolerance data architecture. It goes on to discuss AI-driven automation, edge computing, and quantum computing among other emerging trends. This book aims and help professionals optimize data pipelines, support business intelligence, and facilitate real-time decision-making in the modern data ecosystems by addressing both the foundational principles and advanced strategies.
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