I Heart Logs Event Data Stream Processing And Data Integration Pdf
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I Heart Logs
Why a book about logs? That’s easy: the humble log is an abstraction that lies at the heart of many systems, from NoSQL databases to cryptocurrencies. Even though most engineers don’t think much about them, this short book shows you why logs are worthy of your attention. Based on his popular blog posts, LinkedIn principal engineer Jay Kreps shows you how logs work in distributed systems, and then delivers practical applications of these concepts in a variety of common uses—data integration, enterprise architecture, real-time stream processing, data system design, and abstract computing models. Go ahead and take the plunge with logs; you’re going love them. Learn how logs are used for programmatic access in databases and distributed systems Discover solutions to the huge data integration problem when more data of more varieties meet more systems Understand why logs are at the heart of real-time stream processing Learn the role of a log in the internals of online data systems Explore how Jay Kreps applies these ideas to his own work on data infrastructure systems at LinkedIn
Frontiers of Cyberlearning
This book demonstrates teachers’ and learners’ experiences with big data in education; education and cloud computing; and new technologies for teacher support. It also discusses the advantages of using these frontier technologies in teaching and learning and predicts the future challenges. As such, it enables readers to better understand how technologies can improve learning and teaching experiences. It is intended for graduates and scholars in educational technology disciplines and anyone interested in the applications of frontier technologies in education.
Data Science
Data science didefinisikan suatu ilmu yang mengkolaborasikan beberapa bidang ilmu tertentu dengan keahlian dibidang pemrograman atau informatika, matematika dan statiska dengan tujuan untuk menyelesaikan suatu masalah, membantu pengambilan keputusan serta mendapatkan informasi yang tedapat didalam data terutama data berskala besar. Proses data science umumnya dimulai dari mengumpulkan data, kemudian diikuti dengen pembersihan data untuk menghilangkan noice dan kesalahan. Setelah itu, data diolah dan diubah ke dalam format yang dapat digunakan oleh algoritma data science. Langkah berikutnya adalah eksplorasi data menggunakan berbagai teknik seperti clustering, klasifikasi, dan asosiasi, untuk menemukan pola – pola penting. Hasil dari data science dapat diterapkan dalam berbagai bidang seperti keuangan, ritel, bisnis, pendidikan, perbankan, dan di perfilman, mengoptimalkan operasi, dan meningkatkan kreativitas dan kecerdikan dalam menggunakan kemampuan untuk membangun dan memberikan solusi yang cerdas di setiap permasalahan. Meskipun menawarkan banyak manfaat, tantang seperti privasi data dan interpretasi hasil yang tepat tetap menjadi perhatian utama.