Snowflake Data Platform Engineering
Download Snowflake Data Platform Engineering PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Snowflake Data Platform Engineering book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Snowflake Data Platform Engineering
"Snowflake Data Platform Engineering" "Snowflake Data Platform Engineering" is a comprehensive guide to mastering Snowflake, the modern cloud data platform enabling enterprise-grade analytics and data engineering at scale. This book demystifies Snowflake's foundational multi-cluster architecture, detailing the separation of storage and compute, virtual warehouse optimization, secure data management, and cloud provider-agnostic features. Readers are introduced to robust security frameworks, including encryption, RBAC, and data masking, alongside governance strategies vital for regulatory compliance and data protection. Building on architectural insights, the book systematically explores modern ingestion and integration patterns—from batch and bulk loading to real-time streaming with Snowpipe, effective handling of semi-structured data, and seamless connectivity to external data lakes and third-party ETL tools. In-depth chapters on data modeling, schema evolution, transformation, and lineage equip practitioners to implement advanced analytics solutions with agility and performance, harnessing Snowflake’s capabilities for materialized views, procedural SQL, and automated workflows. Best practices in performance tuning, query optimization, and resource governance are paired with detailed troubleshooting techniques for high-impact and cost-effective solutions. Further, the book addresses mission-critical themes such as high availability, disaster recovery, automation with Infrastructure as Code, and extensibility through APIs, Snowpark, and data marketplace integration. Real-world case studies, industry-specific blueprints, and practical lessons offer guidance for both newcomers and seasoned data engineers. "Snowflake Data Platform Engineering" is an essential resource for unlocking the full power, resilience, and innovation potential of the Snowflake ecosystem in today’s cloud-driven landscape.
Data Engineering with Google Cloud Platform
Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book DescriptionWith this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.
Rise of the Data Cloud
The rise of the Data Cloud is ushering in a new era of computing. The world’s digital data is mass migrating to the cloud, where it can be more effectively integrated, managed, and mobilized. The data cloud eliminates data siloes and enables data sharing with business partners, capitalizing on data network effects. It democratizes data analytics, making the most sophisticated data science tools accessible to organizations of all sizes. Data exchanges enable businesses to discover, explore, and easily purchase or sell data—opening up new revenue streams. Business leaders have long dreamed of data driving their organizations. Now, thanks to the Data Cloud, nothing stands in their way.