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Artículo: AMZ-B0FSLP68NH
CLOUD DATA WAREHOUSING GUIDE WITH SNOWFLAKE, BIGQUERY, AND SCHEMA DESIGN: 75 ETL Projects for Analytics Platforms
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Kindle
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0.84 kg
0.84 kg
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Amazon
Amazon
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USA
USA
Sobre este producto
- In an age where data drives decision-making and business innovation, mastering cloud data warehousing is essential for unlocking actionable insights from vast datasets. "Cloud Data Warehousing Guide with Snowflake, BigQuery, and Schema Design: 75 ETL Projects for Analytics Platforms" by Joss Amber provides a comprehensive, practical roadmap to building and optimizing modern data warehouses using leading cloud platforms. Begin with the essentials of cloud data warehousing, tracing its evolution from on-premises systems to scalable cloud solutions, and delving into core concepts like OLAP, data lakes, ETL/ELT processes, and comparative analyses of Snowflake and BigQuery. Explore schema design fundamentals, including normalization, star and snowflake schemas, partitioning strategies, and managing schema evolution, complete with real-world case studies like e-commerce applications. Get hands-on with Snowflake and BigQuery through dedicated chapters on their architectures, key features—such as time travel, cloning, data sharing, BigQuery ML, GIS, and federated queries—and practical integration with Python, BI tools, and cost optimization techniques. Labs guide you in setting up your first warehouses and exploring public datasets. Transition to ETL/ELT fundamentals, covering batch and streaming ingestion, transformation best practices with SQL, Python, and Spark, workflow orchestration using Airflow and Dagster, data quality assurance, and security compliance. The heart of the book lies in its 75 ETL projects, structured across beginner, intermediate, and advanced levels. Tackle beginner projects on data ingestion, basic transformations, schema exercises, analytics integration, and error handling; intermediate challenges involving streaming ETL, optimization, multi-cloud integrations, and dbt with version control; and advanced scenarios focusing on scalability, AI integration, security, industry-specific case studies, and custom automation. Advance further with topics on performance tuning, data governance, hybrid architectures, emerging trends like lakehouses and zero-ETL, legacy migrations, best practices for scalable warehouses, troubleshooting, career advancement in data engineering, and the future of cloud data warehousing. Appendices offer a glossary, resources for further reading, and access to code repositories and datasets to enhance your learning. Ideal for data engineers building robust pipelines, analysts seeking deeper insights, developers transitioning to cloud analytics, or architects designing future-proof systems, this guide equips you with the knowledge, tools, and projects to harness Snowflake and BigQuery effectively. Transform your data into a strategic asset—dive in and elevate your analytics capabilities today.
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