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The Ultimate Guide to Snowpark
The Ultimate Guide to Snowpark

The Ultimate Guide to Snowpark: Design and deploy Snowflake Snowpark with Python for efficient data workloads

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Profile Icon Shankar Narayanan SGS Profile Icon Vivekanandan SS
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Full star icon Full star icon Full star icon Full star icon Full star icon 5 (7 Ratings)
Paperback May 2024 254 pages 1st Edition
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Arrow left icon
Profile Icon Shankar Narayanan SGS Profile Icon Vivekanandan SS
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€31.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (7 Ratings)
Paperback May 2024 254 pages 1st Edition
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€17.99 €25.99
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€31.99
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Renews at €18.99p/m
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The Ultimate Guide to Snowpark

Discovering Snowpark

Snowpark is the recent major innovation released by Snowflake that provides an intuitive set of libraries and runtimes for querying and processing data at scale in Snowflake. This chapter aims to guide you through Snowpark to understand its unique capabilities. In addition, the chapter helps you learn how to utilize Python with Snowpark and implement it in various workloads such as data engineering, data science, and data applications. By the end of this chapter, you will have grasped Snowpark’s capabilities and benefits, including faster data processing, scalability, and reduced costs.

In this chapter, we’re going to cover the following main topics:

  • Introducing Snowpark
  • Leveraging Python for Snowpark
  • Understanding Snowpark for different workloads
  • Realizing the value of using Snowpark

Introducing Snowpark

Snowflake, founded in 2012, started its journey to the data cloud by completely re-engineering the world of data and rethinking how a reliable, secure, high-performance, and scalable data-processing system should be architected for the cloud. It started with offering cloud-based data warehousing through a managed Software as a Service (SaaS) platform to load, analyze, and process large volumes of data. The success of Snowflake lies in the fact that it is a cloud-native managed solution that is built on top of the major public cloud providers such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform by automatically providing a reliable, secure, high-performance, and scalable data processing system for organizations without the need to deploy hardware or install or configure any software.

As with any cloud data warehousing, Snowflake supports American National Standards Institute (ANSI) SQL as the language of choice. Although SQL is a powerful declarative...

Leveraging Python for Snowpark

In June 2022, Snowflake made a significant announcement, revealing the much-anticipated Snowpark for Python. This new release has rapidly emerged as the preferred programming language for Snowpark, providing users with a more extensive range of options for programming data in Snowflake. Moreover, Snowpark has simplified managing data architectures, enabling users to operate more quickly and efficiently.

Snowpark for Python is a cutting-edge, enterprise-grade, open-source innovation integrated into the Snowflake data cloud. As a result, the platform delivers a seamless, unified experience for data scientists and developers. In addition, the Snowpark for Python package is built upon the Snowflake Python connector. The Python connector enables users to execute SQL commands and other essential functions in Snowflake and Snowpark for Python empowers users to undertake more advanced data applications.

For instance, the platform permits users to run user...

Understanding Snowpark for different workloads

The release of Snowpark transformed Snowflake into a complete data platform designed to support various workloads. Snowpark supports multiple workloads, such as data science and ML, data engineering, and data applications.

Data science and ML

Python is the favorite language for data scientists. Snowpark for Python supports popular libraries and frameworks such as pandas, NumPy, and scikit-learn, making it the ideal framework for data scientists to perform ML development in Snowflake. In addition, data scientists can use the DataFrames API to interact with data inside Snowflake and perform batch training and inference inside Snowflake. Developers can also use Snowpark for feature engineering, ML model inference, and end-to-end ML pipelines. Snowpark also provides a SnowparkML library to support data science and ML in Snowpark.

Data engineering

Data cleansing and ELT workloads are complex, and building a data pipeline with just...

Realizing the value of using Snowpark

The traditional big data approach has been in the industry for a long time and is unsuitable for modern cloud-based scalable workloads. Traditional architecture has many challenges, such as the following:

  • De-coupling the compute and data into separate systems
  • Running separate processing clusters for different languages
  • Complexity in managing the system
  • Data silos and data duplication
  • Lack of unified security and governance

Snowflake solves the traditional system’s challenges using Snowpark, providing tremendous value to the data ecosystem and Snowflake users. The following diagram shows the difference between a traditional approach and Snowflake’s streamlined approach:

Figure 1.1 – Traditional versus Snowflake approach

Figure 1.1 – Traditional versus Snowflake approach

As you can see from the difference between both approaches, Snowpark’s streamlined approach benefits both the business and the developers by...

Summary

Snowflake’s Snowpark perfectly coalesces SQL and Python, running complex data processing jobs in the Snowflake data cloud and enabling data engineers, data scientists, and developers to take advantage of Snowflake. In this chapter, we have seen the benefits of Snowpark and why Python is the preferred development language. We also covered different workloads that Snowpark supports.

In the next chapter, we will examine configuring and operating with Snowpark in detail and learn how to use Snowpark for various workloads.

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Key benefits

  • Get to grips with Snowflake 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

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 Snowflake 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.

Who is this book 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.

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
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Table of Contents

13 Chapters
Part 1: Snowpark Foundation and Setup Chevron down icon Chevron up icon
Chapter 1: Discovering Snowpark Chevron down icon Chevron up icon
Chapter 2: Establishing a Foundation with Snowpark Chevron down icon Chevron up icon
Part 2: Snowpark Data Workloads Chevron down icon Chevron up icon
Chapter 3: Simplifying Data Processing Using Snowpark Chevron down icon Chevron up icon
Chapter 4: Building Data Engineering Pipelines with Snowpark Chevron down icon Chevron up icon
Chapter 5: Developing Data Science Projects with Snowpark Chevron down icon Chevron up icon
Chapter 6: Deploying and Managing ML Models with Snowpark Chevron down icon Chevron up icon
Part 3: Snowpark Applications Chevron down icon Chevron up icon
Chapter 7: Developing a Native Application with Snowpark Chevron down icon Chevron up icon
Chapter 8: Introduction to Snowpark Container Services Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Karthik Rajashekaran Jul 25, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
An In-Depth and Practical Resource for Snowpark EnthusiastsI recently had the opportunity to dive into "The Ultimate Guide to Snowpark: Design and deploy Snowpark with Python for efficient data workloads," and it has proven to be an invaluable resource for both beginners and seasoned professionals working with Snowflake's Snowpark. This book is a comprehensive guide that effectively bridges the gap between theoretical knowledge and practical implementation.Content and Structure:The book is well-structured, starting with the basics of Snowpark and gradually advancing to more complex topics.Technical Accuracy:The book excels in its technical accuracy. Each chapter is meticulously written, ensuring that the information is up-to-date with the latest version of Snowpark and Python. The authors have done an excellent job of explaining complex concepts in a clear and concise manner, making it easy for readers to follow along and apply the knowledge to their projects.Hands-On Approach:One of the standout features of this book is its hands-on approach. The numerous code snippets and practical examples provide readers with the opportunity to practice and experiment with Snowpark. The step-by-step tutorials are particularly useful for those who learn by doing. By the end of the book, readers will have built several projects that showcase the power and flexibility of Snowpark.Integration with Python:The integration of Snowpark with Python is one of the book's main highlights. It covers the nuances of using Python for data workloads within the Snowpark environment, including tips on optimizing performance and writing efficient code. The book also touches on common pitfalls and how to avoid them, which is invaluable for anyone looking to use Snowpark in a production environment.Final Thoughts:"The Ultimate Guide to Snowpark" is a must-have for anyone looking to harness the power of Snowflake's Snowpark for their data processing needs. Whether you're a data engineer, data scientist, or a developer, this book provides the knowledge and tools needed to design and deploy efficient data workloads using Python and Snowpark. It’s a comprehensive guide that balances theory with practical application, making it an essential addition to your technical library.Highly recommended for anyone serious about mastering Snowpark and taking their data processing skills to the next level.
Amazon Verified review Amazon
Vinoth Nageshwaran Aug 01, 2024
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The Ultimate Guide to Snowpark" is an outstanding resource for anyone looking to master Snowpark's capabilities within the Snowflake environment. The book is meticulously crafted, balancing foundational knowledge with advanced techniques, and it is filled with practical examples and code snippets that make learning engaging and effective. It's an indispensable tool for data engineers, data scientists, and developers aiming to leverage Snowpark for scalable and efficient data workloads
Amazon Verified review Amazon
JW Jun 13, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A Comprehensive Guide to Mastering SnowparkIf you’re looking to dive deep into Snowpark, “The Ultimate Guide to Snowpark” by Shankar Narayanan SGS and Vivekanandan Srinivasan is the perfect resource. This book offers detailed insights and practical examples that make it indispensable for data engineers and data scientists.The authors cover a wide range of topics, from setting up the development environment to building robust data pipelines and developing data science projects. Each chapter is meticulously crafted, providing clear explanations and hands-on guidance.One of the standout features is the thorough coverage of Snowpark’s capabilities, including data processing, machine learning data management, and even developing native applications. Shankar’s technical expertise and Vivekanandan’s machine learning background shine through, making complex concepts accessible and actionable.Whether you’re new to Snowpark or looking to enhance your skills, this book and the associated Git Repository is an invaluable asset. Highly recommended for anyone serious about leveraging Snowpark within Snowflake.
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Jay Jul 12, 2024
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Excellent resource covering all essential aspects of Snowflake Snowpark clearly and concisely. The practical examples and insightful explanations make it accessible to both beginners and experienced practitioners. If you're serious about Snowflake Snowpark, this book📘 is for you!
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yoganath Jun 07, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Highly recommended! What I really liked about this book is that it not only explains the theoretical concepts of Snowflake but also provides practical examples and code snippets for configuration and coding styles. It helped me understand how to perform complex data tasks without moving data and optimize overall performance by reducing latency. An invaluable resource for data engineers and scientists.
Amazon Verified review Amazon
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