Datubāzes

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Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

Martin Kleppmann, Kleppmann Martin

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Practical Data Science : A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets

Andreas Fran�ois Vermeulen, Vermeulen, Andreas Fran�ois

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Think Stats: Exploratory Data Analysis

Allen Downey, Michael Kosta Loukides (editor), Meghan Blanchette (editor)

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Database Reliability Engineering

Laine Campbell

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Data Wrangling With Python

Katharine Jarmul, Jacqueline Kazil

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Database Internals : A Deep Dive into How Distributed Data Systems Work

Alex Petrov

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Data Science from Scratch

Joel Grus, Grus Joel

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Deep Learning from Scratch

Seth Weidman

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Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale

Gwen Shapira, Todd Palino, Rajini Sivaram, Krit Petty, Neha Narkhede

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Deep Learning for Coders With Fastai and PyTorch

Sylvain Gugger, Jeremy Howard

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Learning Spark

Danny Lee, Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Guang-Yeu Lee, Jules Damji

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Learning SQL: Generate, Manipulate, and Retrieve Data

Alan Beaulieu, Beaulieu Alan

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Behavioral Data Analysis with R and Python: Customer-Driven Data for Real Business Results

Florent Buisson

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Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

Peter Bruce, Andrew Bruce, Peter C. Bruce, Peter Gedeck, Bruce C

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Observability Engineering : Achieving Production Excellence

Charity Majors, George Jones, Liz Fong-Jones, George Miranda, Majors Charity

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High Performance MySQL: Proven Strategies for Running MySQL at Scale

Baron Schwartz, Silvia Botros, Jeremy Tinley

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Mastering Apache Pulsar

Jowanza Joseph

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Data Pipelines Pocket Reference : Moving and Processing Data for Analytics

James Densmore

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Tidy Modeling With R

Max Kuhn, Julia Silge, Julia Kuhn

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R for Data Science

Hadley Wickham, Garrett Grolemund, Mine Çetinkaya-Rundel, Wickham Hadley

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gramatuveikals.lv

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

Practical Data Science : A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets

Think Stats: Exploratory Data Analysis

Database Reliability Engineering

Data Wrangling With Python

Database Internals : A Deep Dive into How Distributed Data Systems Work

Data Science from Scratch

Deep Learning from Scratch

Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale

Deep Learning for Coders With Fastai and PyTorch

Learning Spark

Learning SQL: Generate, Manipulate, and Retrieve Data

Behavioral Data Analysis with R and Python: Customer-Driven Data for Real Business Results

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

Observability Engineering : Achieving Production Excellence

High Performance MySQL: Proven Strategies for Running MySQL at Scale

Mastering Apache Pulsar

Data Pipelines Pocket Reference : Moving and Processing Data for Analytics

Tidy Modeling With R

R for Data Science

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