The Ultimate Guide to Snowpark : Design and Deploy Snowflake Snowpark with Python for Efficient Data Workloads / Shankar Narayanan SGS, Vivekanandan SS ; foreword by Jeff Hollan.

Format:

Book

Author/Creator:

Narayanan, Shankar, author.

Vivekanandan, author.

Contributor:

Hollan, Jeff, writer of foreword.

Language:

English

Subjects (All):

Big data.

Cloud computing.

Computer architecture.

Physical Description:

1 online resource (254 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2024]

Biography/History:

SGS Shankar Narayanan: Shankar Narayanan is a Technical Architect with over a decade of diverse experience leading and delivering large-scale Data and Cloud implementations for Fortune 500 companies across various industries. He has successfully implemented Snowflake Data Cloud for many organizations leading the customers to adopt Snowflake. He holds a bachelor's and master's degree in Computer science and holds many certifications in multi-cloud platforms and Snowflake. He is an award-winning blogger and actively contributes to various technical publications and open-source projects. For his technical contribution to the community, He has been selected as SAP Community Topic leader by SAP and is selected as one of the 72 Snowflake Data Heroes by Snowflake. SS Vivekanandan: Vivekanandan spearheads the GenAI enablement team at Verizon, leveraging over a decade of expertise in Data Science and Big Data. His professional journey spans across building analytics solutions and products across diverse domains, and proficient in cloud analytics and data warehouses. He holds a bachelor's degree in Industrial engineering from Anna University, a long distance program in Big Data analytics from IIM, Bangalore, and a master's in Data Science from Eastern University. As a seasoned trainer, he imparts his knowledge, specializing in Snowflake and GenAI, also a data science guest faculty and advisor for various educational institutes. His solution is ranked in the top 1 percentile in Kaggle Kernels globally.

Summary:

Develop robust data pipelines, deploy mature machine learning models, and build secure data apps with Snowpark using Python Key Features Get to grips with Snowpark's basic and advanced features Implement workloads in domains like data engineering, data science, and data applications using Snowpark with Python Deploy Snowpark in production with practical examples and best practices Purchase of the print or Kindle book includes a free PDF eBook Book Description Snowpark is a powerful framework that helps you unlock numerous possibilities within the Snowflake Data Cloud. However, without proper guidance, leveraging the full potential of Snowpark with Python can be challenging. Packed with practical examples and code snippets, this book will be your go-to guide to using Snowpark with Python successfully. The Ultimate Guide to Snowpark helps you develop an understanding of Snowpark and how it enables you to implement workloads in data engineering, data science, and data applications within the Data Cloud. From configuration and coding styles to workloads such as data manipulation, collection, preparation, transformation, aggregation, and analysis, this guide will equip you with the right knowledge to make the most of this framework. You'll discover how to build, test, and deploy data pipelines and data science models. As you progress, you'll deploy data applications natively in Snowflake and operate large language models (LLMs) using Snowpark container services. By the end of this book, you'll be able to leverage Snowpark's capabilities and propel your career as a Snowflake developer to new heights. What you will learn Harness Snowpark with Python for diverse workloads Develop robust data pipelines with Snowpark using Python Deploy mature machine learning models Explore the process of developing, deploying, and monetizing native apps using Snowpark Deploy and operate containers in Snowpark Discover the pathway to adopting Snowpark effectively in production Who this book is for This book is for data engineers, data scientists, developers, and data practitioners seeking an in-depth understanding of Snowpark's features and best practices for deploying various workloads in Snowpark using the Python programming language. Basic knowledge of SQL, proficiency in Python, an understanding of data engineering and data science basics, and familiarity with the Snowflake Data Cloud platform are required to get the most out of this book.

Contents:

Cover

Title Page

Copyright

Dedication

Foreword

Contributors

Table of Contents

Preface

Part 1: Snowpark Foundation and Setup

Chapter 1: Discovering Snowpark

Introducing Snowpark

Leveraging Python for Snowpark

Capabilities of Snowpark for Python

Why Python for Snowpark

Understanding Snowpark for different workloads

Data science and ML

Data engineering

Data governance and security

Data applications

Realizing the value of using Snowpark

Summary

Chapter 2: Establishing a Foundation with Snowpark

Technical requirements

Configuring the Snowpark development environment

Snowpark Python worksheet

Snowpark development in a local environment

Operating with Snowpark

The Python Engine

Client APIs

UDFs

Establishing a project structure for Snowpark

Part 2: Snowpark Data Workloads

Chapter 3: Simplifying Data Processing Using Snowpark

Data ingestion

Important note on datasets

Ingesting a CSV file into Snowflake

Ingesting JSON into Snowflake

Ingesting Parquet files into Snowflake

Ingesting images into Snowpark

Data exploration and transformation

Data exploration

Data transformations

Data grouping and analysis

Data grouping

Data analysis

Chapter 4: Building Data Engineering Pipelines with Snowpark

Developing resilient data pipelines with Snowpark

Traditional versus modern data pipelines

Data engineering with Snowpark

Implementing programmatic ELT with Snowpark

Deploying efficient DataOps in Snowpark

Developing a data engineering pipeline

Overview of tasks in Snowflake

Compute models for tasks

Task graphs

Managing tasks and task graphs with Python

Implementing logging and tracing in Snowpark

Event tables.

Setting up logging in Snowpark

Handling exceptions in Snowpark

Setting up tracing in Snowpark

Comparison of logs and traces

Chapter 5: Developing Data Science Projects with Snowpark

Data science in Data Cloud

Data science and ML concepts

The Data Cloud paradigm

Why Snowpark for data science and ML?

Introduction to Snowpark ML

End-to-end ML with Snowpark

Exploring and preparing data

Missing value analysis

Outlier analysis

Correlation analysis

Leakage variables

Feature engineering

Training ML models in Snowpark

The efficiency of Snowpark ML

Chapter 6: Deploying and Managing ML Models with Snowpark

Deploying ML models in Snowpark

Snowpark ML model registry

Managing Snowpark model data

Snowpark Feature Store

Benefits of Feature Store

Feature stores versus data warehouses

When to utilize versus when to avoid feature stores

Part 3: Snowpark Applications

Chapter 7: Developing a Native Application with Snowpark

Introduction to the Native Apps Framework

Snowflake's native application Landscape

Native App Framework components

Streamlit in Snowflake

Benefits of Native Apps

Developing the native application

The Streamlit editor

Running the Streamlit application

Developing with the Native App Framework

Publishing the native application

Setting the default release directive

Creating a listing for your application

Managing the native application

Viewing installed applications

Viewing README for applications

Managing access to the application

Removing an installed application

Chapter 8: Introduction to Snowpark Container Services

Introduction to Snowpark Container Services.

Data security in Snowpark Container Services

Components of Snowpark Containers

Setting up Snowpark Container Services

Creating Snowflake objects

Setting up the services

Setting up the filter service

Building the Docker image

Deploying the service

Setting up a Snowpark Container Service job

Setting up the job

Deploying the job

Executing the job

Deploying LLMs with Snowpark

Preparing the LLM

Registering the model

Deploying the model to Snowpark Container Services

Running the model

Index

Other Books You May Enjoy.

Notes:

Includes index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781805124450

1805124455

OCLC:

1434176588