Snowflake Data Engineering
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Snowflake Data Engineering
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Author : Maja Ferle
language : en
Publisher: Simon and Schuster
Release Date : 2025-01-28
Snowflake Data Engineering written by Maja Ferle and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-28 with Computers categories.
Snowflake 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! --
Snowflake Data Engineering
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Author : Maja Ferle
language : en
Publisher: Simon and Schuster
Release Date : 2025-01-28
Snowflake Data Engineering written by Maja Ferle and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-28 with Computers categories.
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 code in your pipelines • Deploy Snowflake objects and code using continuous integration principles • Optimize performance and costs when ingesting data into Snowflake Snowflake 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 technology Pipelines 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 book Snowflake 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 inside • Ingest data from the cloud, APIs, or Snowflake Marketplace • Orchestrate data pipelines with streams and tasks • Optimize performance and cost About the reader For software developers and data analysts. Readers should know the basics of SQL and the Cloud. About the author Maja 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 Contents Part 1 1 Data engineering with Snowflake 2 Creating your first data pipeline Part 2 3 Best practices for data staging 4 Transforming data 5 Continuous data ingestion 6 Executing code natively with Snowpark 7 Augmenting data with outputs from large language models 8 Optimizing query performance 9 Controlling costs 10 Data governance and access control Part 3 11 Designing data pipelines 12 Ingesting data incrementally 13 Orchestrating data pipelines 14 Testing for data integrity and completeness 15 Data pipeline continuous integration
Snowflake Data Platform Engineering
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-09
Snowflake Data Platform Engineering written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-09 with Computers categories.
"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
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Author : Adi Wijaya
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-03-31
Data Engineering With Google Cloud Platform written by Adi Wijaya and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-31 with Computers categories.
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.
Snowflake Snowpro Advanced Data Engineer Dea C02 Certification Practice 300 Questions Answer
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Author : Rashmi Shah
language : en
Publisher: QuickTechie.com | A career growth machine
Release Date :
Snowflake Snowpro Advanced Data Engineer Dea C02 Certification Practice 300 Questions Answer written by Rashmi Shah and has been published by QuickTechie.com | A career growth machine this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
The Advanced Snowflake Data Engineer: A Comprehensive Guide to DEA-C02 Certification, available through QuickTechie.com, is the definitive resource for data professionals seeking to validate their advanced knowledge and skills in applying comprehensive data engineering principles using Snowflake. This book is specifically tailored for individuals with two or more years of hands-on experience as a Data Engineer in a production environment, building upon the foundational expertise gained from the SnowPro Core Certification. This comprehensive guide takes readers beyond the basics, diving deep into the intricate world of advanced data engineering on the Snowflake Data Cloud. It equips professionals to architect, implement, and manage robust, scalable, and highly performant data pipelines that span various data sources and destinations. From sourcing data from diverse origins like Data Lakes, APIs, and on-premises systems, to designing end-to-end near real-time streams and evaluating complex performance metrics, this book provides the practical knowledge and strategic insights essential for a senior Snowflake Data Engineer. Key Learning Objectives and Comprehensive Coverage: The book's content is meticulously aligned with the SnowPro® Advanced: Data Engineer Certification (DEA-C02) exam, ensuring comprehensive and targeted preparation across all critical domains: Data Movement (26%): Covers mastering techniques for sourcing data from a wide array of origins, including cloud-based Data Lakes (S3, ADLS, GCS), various APIs, and traditional on-premises data sources into Snowflake. It delves into external stage concepts, designing and implementing continuous data ingestion with Snowpipe, utilizing Snowflake connectors and integrations, applying data loading best practices for various file formats (Parquet, ORC, JSON, Avro, XML), error handling, data validation during ingest, and understanding data replication for cross-cloud or cross-region data movement. Performance Optimization (21%): Develops expertise in Virtual Warehouse optimization, including sizing, scaling policies, multi-cluster warehouses, and workload management for data engineering tasks. It focuses on query performance tuning by utilizing Query Profile, optimizing SQL queries, understanding query history and execution plans, comprehending Snowflake's storage architecture with Micro-partitions and Clustering, leveraging the Search Optimization Service for point lookups, and designing and using Materialized Views for query acceleration. Storage and Data Protection (14%): Provides insights into Snowflake's storage layer, data compression, and cost implications. It details utilizing data retention policies for data recovery and protection through Time Travel and Fail-safe, understanding data encryption at rest and in transit, and implementing secure data sharing for consumers within and outside an organization. Data Governance (14%): Explores designing and implementing robust Role-Based Access Control (RBAC) for data engineering roles, managing object access and security through row access policies, dynamic data masking, and external functions for tokenization/obfuscation. It also covers managing and monitoring credit consumption with Resource Monitors and implementing data classification and tagging for governance and compliance. Data Transformation (25%): Addresses designing and implementing various ELT/ETL patterns in Snowflake. It covers advanced SQL constructs, window functions, User-Defined Functions (UDFs), User-Defined Table Functions (UDTFs), leveraging Snowpark with Python (or other languages) for complex, programmatic transformations, orchestrating complex data pipelines with Stored Procedures, and scheduling with Tasks. Additionally, it focuses on implementing data quality checks and validation rules within pipelines. Who This Book Is For: This book is specifically designed for the SnowPro® Advanced: Data Engineer candidate and other professionals, including: Experienced Data Engineers: Those responsible for designing, building, and maintaining complex data pipelines, ETL/ELT processes, and data integration solutions on Snowflake. Data Architects: Individuals involved in designing enterprise-level data platforms on Snowflake, requiring a deep understanding of data movement, storage, and transformation best practices. Cloud Engineers/DevOps Specialists: Professionals who manage the operational aspects and infrastructure of Snowflake data solutions. Professionals aiming for the SnowPro® Advanced: Data Engineer Certification (DEA-C02): This book serves as an essential guide for in-depth preparation. Individuals with 2 or more years of hands-on experience as a Data Engineer in a production environment. Exam Details and How This Book Prepares You: The book's structure and content are precisely mapped to the SnowPro® Advanced: Data Engineer Certification (DEA-C02) exam, ensuring comprehensive and targeted preparation. It covers all relevant topics with conceptual explanations, practical examples, and potentially practice questions integrated within chapters to reinforce understanding. The guide addresses various question types, including Multiple Select, Multiple Choice, and Interactive questions, through detailed explanations of concepts and their practical applications. It prepares candidates for the 115-minute time limit and aims to equip them with the knowledge required to confidently achieve and exceed the 750+ passing score (scaled from 0-1000). The content is solely in English and assumes the reader is SnowPro Core Certified, building directly on that foundational knowledge with advanced data engineering concepts. Key Features of This Book: This essential guide, available through QuickTechie.com, offers several key features: Comprehensive Coverage: Aligned meticulously with the DEA-C02 exam blueprint, ensuring no critical topic is left out. Practical Examples and Use Cases: Numerous real-world scenarios and code examples demonstrate the application of data engineering principles in Snowflake. Best Practices for Production Systems: Provides insights and recommendations for building scalable, robust, and maintainable data pipelines in production environments. Focus on Performance and Optimization: Dedicated sections and tips for evaluating, troubleshooting, and enhancing the performance of Snowflake data engineering workloads. Strategic Guidance: Beyond technical details, the book provides strategic advice on designing end-to-end data solutions. This book, presented by QuickTechie.com, is an essential investment for any data engineer serious about mastering Snowflake and achieving the prestigious SnowPro® Advanced: Data Engineer Certification, solidifying their role as a leader in modern cloud data engineering.
Data Pipelines Pocket Reference
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Author : James Densmore
language : en
Publisher: O'Reilly Media
Release Date : 2021-02-10
Data Pipelines Pocket Reference written by James Densmore and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-10 with Computers categories.
Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting
Snowflake Cookbook
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Author : Hamid Mahmood Qureshi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-02-25
Snowflake Cookbook written by Hamid Mahmood Qureshi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-25 with Computers categories.
Develop modern solutions with Snowflake's unique architecture and integration capabilities; process bulk and real-time data into a data lake; and leverage time travel, cloning, and data-sharing features to optimize data operations Key Features Build and scale modern data solutions using the all-in-one Snowflake platform Perform advanced cloud analytics for implementing big data and data science solutions Make quicker and better-informed business decisions by uncovering key insights from your data Book Description Snowflake is a unique cloud-based data warehousing platform built from scratch to perform data management on the cloud. This book introduces you to Snowflake's unique architecture, which places it at the forefront of cloud data warehouses. You'll explore the compute model available with Snowflake, and find out how Snowflake allows extensive scaling through the virtual warehouses. You will then learn how to configure a virtual warehouse for optimizing cost and performance. Moving on, you'll get to grips with the data ecosystem and discover how Snowflake integrates with other technologies for staging and loading data. As you progress through the chapters, you will leverage Snowflake's capabilities to process a series of SQL statements using tasks to build data pipelines and find out how you can create modern data solutions and pipelines designed to provide high performance and scalability. You will also get to grips with creating role hierarchies, adding custom roles, and setting default roles for users before covering advanced topics such as data sharing, cloning, and performance optimization. By the end of this Snowflake book, you will be well-versed in Snowflake's architecture for building modern analytical solutions and understand best practices for solving commonly faced problems using practical recipes. What you will learn Get to grips with data warehousing techniques aligned with Snowflake's cloud architecture Broaden your skills as a data warehouse designer to cover the Snowflake ecosystem Transfer skills from on-premise data warehousing to the Snowflake cloud analytics platform Optimize performance and costs associated with a Snowflake solution Stage data on object stores and load it into Snowflake Secure data and share it efficiently for access Manage transactions and extend Snowflake using stored procedures Extend cloud data applications using Spark Connector Who this book is for This book is for data warehouse developers, data analysts, database administrators, and anyone involved in designing, implementing, and optimizing a Snowflake data warehouse. Knowledge of data warehousing and database and cloud concepts will be useful. Basic familiarity with Snowflake is beneficial, but not necessary.
Data Engineering With Alteryx
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Author : Paul Houghton
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-06-30
Data Engineering With Alteryx written by Paul Houghton and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-30 with Computers categories.
Build and deploy data pipelines with Alteryx by applying practical DataOps principles Key Features • Learn DataOps principles to build data pipelines with Alteryx • Build robust data pipelines with Alteryx Designer • Use Alteryx Server and Alteryx Connect to share and deploy your data pipelines Book Description Alteryx is a GUI-based development platform for data analytic applications. Data Engineering with Alteryx will help you leverage Alteryx's code-free aspects which increase development speed while still enabling you to make the most of the code-based skills you have. This book will teach you the principles of DataOps and how they can be used with the Alteryx software stack. You'll build data pipelines with Alteryx Designer and incorporate the error handling and data validation needed for reliable datasets. Next, you'll take the data pipeline from raw data, transform it into a robust dataset, and publish it to Alteryx Server following a continuous integration process. By the end of this Alteryx book, you'll be able to build systems for validating datasets, monitoring workflow performance, managing access, and promoting the use of your data sources. What you will learn • Build a working pipeline to integrate an external data source • Develop monitoring processes for the pipeline example • Understand and apply DataOps principles to an Alteryx data pipeline • Gain skills for data engineering with the Alteryx software stack • Work with spatial analytics and machine learning techniques in an Alteryx workflow Explore Alteryx workflow deployment strategies using metadata validation and continuous integration • Organize content on Alteryx Server and secure user access Who this book is for If you're a data engineer, data scientist, or data analyst who wants to set up a reliable process for developing data pipelines using Alteryx, this book is for you. You'll also find this book useful if you are trying to make the development and deployment of datasets more robust by following the DataOps principles. Familiarity with Alteryx products will be helpful but is not necessary.
Rise Of The Data Cloud
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Author : Frank Slootman
language : en
Publisher: AuthorHouse
Release Date : 2020-12-18
Rise Of The Data Cloud written by Frank Slootman and has been published by AuthorHouse this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-18 with Business & Economics categories.
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.
Foundations Of Data Engineering Concepts Principles And Practices
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Author : Dr. RVS Praveen
language : en
Publisher: Addition Publishing House
Release Date : 2024-09-23
Foundations Of Data Engineering Concepts Principles And Practices written by Dr. RVS Praveen and has been published by Addition Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-23 with Antiques & Collectibles categories.
Foundations of Data Engineering: Concepts, Principles and Practices" offers a comprehensive introduction to the processes and systems that make data-driven decision-making possible. In today’s data-centric world, companies rely heavily on vast amounts of data to inform strategies, optimize operations, and innovate. This book explains the essential building blocks of data engineering, covering topics like data pipelines, ETL (Extract, Transform, Load) processes, data storage, and distributed computing. The text is structured to guide readers through the end-to-end lifecycle of data, from ingestion to transformation and analysis. It emphasizes best practices in designing robust, scalable data pipelines that ensure high-quality, reliable data is delivered to downstream analytics and machine learning systems. Topics such as batch and real-time data processing are covered, with in-depth discussions on tools and technologies like Apache Kafka, Hadoop, Spark, and cloud-based solutions like Google Cloud and AWS. For those new to the field or looking to expand their knowledge, this book also addresses the importance of data governance, ensuring data integrity, security, and compliance. Readers will gain insights into the challenges of big data and how modern engineering approaches can handle growing data volumes efficiently. With case studies and practical examples throughout, "Foundations of Data Engineering: Concepts, Principles and Practices" is a valuable resource for aspiring data engineers, analysts, and anyone involved in the data ecosystem looking to build scalable, reliable data solutions.