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 systems - Transform data using functions, stored procedures, and SQL
- Orchestrate data pipelines with streams and tasks, and monitor their execution
- Use Snowpark to run Python, Java, and Scala code in your pipelines
- Deploy Snowflake objects and code using continuous integration principles
- Optimize performance and costs when ingesting data into Snowflake With this practical guide you'll build the skills you need to create effective data pipelines on the Snowflake platform. You'll see 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. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the book Snowflake Data Engineering teaches data engineering skills using the day-to-day tasks you'll face on the job. You'll start working hands-on right from chapter two by building your very first simple pipeline on the Snowflake platform. Then, you'll improve your pipeline with increasingly complex elements, including performance optimization and augmenting your data with generative AI. 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. About the reader For software developers and data analysts who have a working knowledge of data warehousing and the ETL process. Readers should know basic SQL and be able to configure cloud object stores on a cloud platform such as AWS, Azure, or GCP that Snowflake supports. About the author Maja Ferle is a seasoned data architect with more than 30 years of experience in data analytics, data warehousing, business intelligence, data engineering, data modeling, and database administration. She holds the SnowPro Advanced Data Engineer and the SnowPro Advanced Data Analyst certifications. She is also a Snowflake Subject Matter Expert and a Snowflake Data Superhero.
- Orchestrate data pipelines with streams and tasks, and monitor their execution
- Use Snowpark to run Python, Java, and Scala code in your pipelines
- Deploy Snowflake objects and code using continuous integration principles
- Optimize performance and costs when ingesting data into Snowflake With this practical guide you'll build the skills you need to create effective data pipelines on the Snowflake platform. You'll see 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. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the book Snowflake Data Engineering teaches data engineering skills using the day-to-day tasks you'll face on the job. You'll start working hands-on right from chapter two by building your very first simple pipeline on the Snowflake platform. Then, you'll improve your pipeline with increasingly complex elements, including performance optimization and augmenting your data with generative AI. 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. About the reader For software developers and data analysts who have a working knowledge of data warehousing and the ETL process. Readers should know basic SQL and be able to configure cloud object stores on a cloud platform such as AWS, Azure, or GCP that Snowflake supports. About the author Maja Ferle is a seasoned data architect with more than 30 years of experience in data analytics, data warehousing, business intelligence, data engineering, data modeling, and database administration. She holds the SnowPro Advanced Data Engineer and the SnowPro Advanced Data Analyst certifications. She is also a Snowflake Subject Matter Expert and a Snowflake Data Superhero.
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