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Snowflake Data Engineering
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- A practical introduction to data engineering on the powerful Snowflake cloud data platform.Data engineers create the pipelines that ingest raw data, transform it, and funnel it to the analysts and professionals who need it. The Snowflake cloud data platform provides a suite of productivity-focused tools and features that simplify building and maintaining data pipelines. In Snowflake Data Engineering, Snowflake Data Superhero Maja Ferle shows you how to get started.In Snowflake Data Engineering you will learn how to:Ingest data into Snowflake from both cloud and local file systemsTransform data using functions, stored procedures, and SQLOrchestrate data pipelines with streams and tasks, and monitor their executionUse Snowpark to run Python code in your pipelinesDeploy Snowflake objects and code using continuous integration principlesOptimize performance and costs when ingesting data into SnowflakeSnowflake Data Engineering reveals how Snowflake makes it easy to work with unstructured data, set up continuous ingestion with Snowpipe, and keep your data safe and secure with best-in-class data governance features. Along the way, you’ll practice the most important data engineering tasks as you work through relevant hands-on examples. Throughout, author Maja Ferle shares design tips drawn from her years of experience to ensure your pipeline follows the best practices of software engineering, security, and data governance.Foreword by Joe Reis.About the technologyPipelines that ingest and transform raw data are the lifeblood of business analytics, and data engineers rely on Snowflake to help them deliver those pipelines efficiently. Snowflake is a full-service cloud-based platform that handles everything from near-infinite storage, fast elastic compute services, inbuilt AI/ML capabilities like vector search, text-to-SQL, code generation, and more. This book gives you what you need to create effective data pipelines on the Snowflake platform.About the bookSnowflake Data Engineering guides you skill-by-skill through accomplishing on-the-job data engineering tasks using Snowflake. You’ll start by building your first simple pipeline and then expand it by adding increasingly powerful features, including data governance and security, adding CI/CD into your pipelines, and even augmenting data with generative AI. You’ll be amazed how far you can go in just a few short chapters!What's insideIngest data from the cloud, APIs, or Snowflake MarketplaceOrchestrate data pipelines with streams and tasksOptimize performance and costAbout the readerFor software developers and data analysts. Readers should know the basics of SQL and the Cloud.About the authorMaja Ferle is a Snowflake Subject Matter Expert and a Snowflake Data Superhero who holds the SnowPro Advanced Data Engineer and the SnowPro Advanced Data Analyst certifications.Table of ContentsPart 11 Data engineering with Snowflake2 Creating your first data pipelinePart 23 Best practices for data staging4 Transforming data5 Continuous data ingestion6 Executing code natively with Snowpark7 Augmenting data with outputs from large language models8 Optimizing query performance9 Controlling costs10 Data governance and access controlPart 311 Designing data pipelines12 Ingesting data incrementally13 Orchestrating data pipelines14 Testing for data integrity and completeness15 Data pipeline continuous integration
| Publisher | Manning Publications |
| Publication date | January 28, 2025 |
| Language | English |
| Print length | 368 pages |
| ISBN-10 | 1633436853 |
| ISBN-13 | 978-1633436855 |
| Item Weight | 1.4 pounds (640 grams) |
| Dimensions | 7.38 x 0.83 x 9.25 inches (18.7 x 2.1 x 23.5 cm) |
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Snowflake Data Engineering
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