Description
From transactional systems and Internet of Things devices to social media and cloud apps, businesses are producing unprecedented amounts of data in this era of digital transformation. These data sources come from a wide variety of sources. Architecture of scalable data platforms that are capable of rapidly ingesting, processing, and analyzing this huge and diverse data while assuring flexibility, robustness, and real-time insights is the primary emphasis of modern data transformation approaches. When it comes to achieving elasticity, high availability, and operational efficiency, this paradigm shift places an emphasis on cloud-native techniques, which make use of microservices, serverless architectures, and containerization. Utilizing complex ETL/ELT pipelines, data lakehouse designs, metadata management, and governance frameworks that strike a balance between performance and compliance and security needs are some of the most important solutions. Through the implementation of contemporary data transformation methods, companies have the ability to democratize data access, speed up analytics, allow decision-making that is driven by artificial intelligence and machine learning, and cultivate a culture of data driven innovation. Building scalable, cloud-native data platforms that enable businesses to turn raw data into actionable insight in a changing business landscape is the topic of this book, which investigates the ideas, architectures, and best practices for doing so.
English
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