Data Engineering for Machine Learning Pipelines, Narayanan P.K., 2024

Data Engineering for Machine Learning Pipelines, Narayanan P.K., 2024.
    
   This book is designed to serve you as a desk reference. Whether you are a beginner or a professional, this book will help you with the fundamentals and provide you with job-ready skills to deliver high-value proposition to the business. This book is written with an aim to bring back the knowledge gained from traditional textbook learning, providing an organized exploration of key data engineering concepts and practices. I encourage you to own a paper copy. This book will serve you well for the next decade in terms of concepts, tools, and practices.




Missing Values in Data Pipelines.
Missing values can also occur during the process of data cleaning and transportation. When a filter or a conditional transformation is employed on a dataset, the output of such operation may lead to missing values, indicating those specific variables did not match the filtering criteria. Similarly, when data is migrated or transported from a source system to a staging layer in an analytical system, there may be cases of missing values due to inconsistencies in data formats or lack of support for a specific encoding type.

For instance, if a source system is using an ISO-8859 encoding and a target system is using a UTF-8 encoding, then characters may be misinterpreted during the migration process; in some fields, data may be truncated to certain characters or cut off, resulting in either missing values or data that does not make sense.

Contents.
About the Author.
About the Technical Reviewer.
Introduction.
Chapter 1: Core Technologies in Data Engineering.
Chapter 2: Data Wrangling using Pandas.
Chapter 3: Data Wrangling using Rust’s Polars.
Chapter 4: GPU Driven Data Wrangling Using CuDF.
Chapter 5: Getting Started with Data Validation using Pydantic and Pandera.
Chapter 6: Data Validation using Great Expectations.
Chapter 7: Introduction to Concurrency Programming and Dask.
Chapter 8: Engineering Machine Learning Pipelines using DaskML.
Chapter 9: Engineering Real-time Data Pipelines using Apache Kafka.
Chapter 10: Engineering Machine Learning and Data REST APIs using FastAPI.
Chapter 11: Getting Started with Workflow Management and Orchestration.
Chapter 12: Orchestrating Data Engineering Pipelines using Apache Airflow.
Chapter 13: Orchestrating Data Engineering Pipelines using Prefect.
Chapter 14: Getting Started with Big Data and Cloud Computing.
Chapter 15: Engineering Data Pipelines Using Amazon Web Services.
Chapter 16: Engineering Data Pipelines Using Google Cloud Platform.
Chapter 17: Engineering Data Pipelines Using Microsoft Azure.
Index.



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