The Ultimate Guide to Snowpark : Design and Deploy Snowflake Snowpark with Python for Efficient Data Workloads

The Ultimate Guide to Snowpark Design and Deploy Snowflake Snowpark with Python for Efficient Data Workloads

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 Snowp...

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Detalles Bibliográficos
Otros Autores: Narayanan, Shankar, author (author), Vivekanandan, author (writer of foreword), Hollan, Jeff, writer of foreword
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England : Packt Publishing Ltd [2024]
Edición:First edition
Materias:
Big data.
Cloud computing.
Computer architecture.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009825869006719
Tabla de Contenidos:
  • 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
  • Summary
  • Part 2: Snowpark Data Workloads
  • Chapter 3: Simplifying Data Processing Using Snowpark
  • Technical requirements
  • 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
  • Summary
  • Chapter 4: Building Data Engineering Pipelines with Snowpark
  • Technical requirements
  • 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
  • Summary
  • Chapter 5: Developing Data Science Projects with Snowpark
  • Technical requirements
  • 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
  • Summary
  • Chapter 6: Deploying and Managing ML Models with Snowpark
  • Technical requirements
  • 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
  • Summary
  • Part 3: Snowpark Applications
  • Chapter 7: Developing a Native Application with Snowpark
  • Technical requirements
  • 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
  • Summary
  • Chapter 8: Introduction to Snowpark Container Services
  • Technical requirements
  • 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
  • Summary
  • Index
  • Other Books You May Enjoy.

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