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AI-Powered Data Engineering with Snowflake · LearnSpace
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AI-Powered Data Engineering with Snowflake

Курс от Edureka
Начальный≈ 7.4 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Build practical expertise in AI-powered data engineering using Snowflake, generative AI, and modern cloud data architecture. You will learn how data engineers design, build, improve, and manage intelligent data pipelines that support analytics, AI applications, and enterprise decision-making. You will begin by exploring the modern data engineering landscape, the role of generative AI, and Snowflake as a cloud data platform. Using the GlobalMart environment, you will build basic pipelines and use AI to understand datasets, generate SQL, explain pipeline code, assess data quality, and support schema mapping. You will also organise multi-source data through Bronze, Silver, and Gold layers and create reliable transformation and documentation workflows. You will then explore intelligent data access using embeddings, semantic search, and retrieval-augmented generation. You will combine structured and unstructured data, build a RAG assistant for data knowledge, and enable natural language analytics over trusted data. You will also prepare pipelines for production by implementing error handling, retry logic, logging, monitoring, evaluation, governance, access control, and orchestration with Snowflake Tasks. By the end of this course, you will be able to: - Explain modern data engineering concepts and the role of generative AI. - Build and manage data pipelines using Snowflake. - Organise data using Bronze, Silver, and Gold architecture. - Use AI for schema understanding, transformation, profiling, and quality checks. - Generate and explain SQL and pipeline code using generative AI. - Build semantic search and RAG workflows for enterprise data. - Combine structured and unstructured data for intelligent analytics. - Implement monitoring, governance, evaluation, and workflow orchestration. - Deploy a production-ready AI-enhanced data engineering solution. Designed for aspiring data engineers, data analysts, cloud professionals, software developers, and students entering the data field, the course prepares you to build reliable, scalable, and intelligent data solutions using Snowflake and generative AI.

Навыки, которые вы освоите

Data TransformationData PipelinesRetrieval-Augmented GenerationData GovernanceData QualityGenerative AIResponsible AIEmbeddingsData EngineeringData EntryBusiness LogicData SecurityData ProcessingSQLModel DeploymentData CollectionData ArchitectureSnowflake SchemaData ManagementPython Programming

Программа курса

4 модулей · 54 учебных материалов

01Foundations of AI-Enhanced Data Engineering15 материалов
Course IntroductionВидеоCourse Syllabus: AI-Powered Data Engineering with SnowflakeЧтениеThe Modern Data Engineering LandscapeВидеоThe Role of Generative AI in Modern Data EngineeringЧтение

Учитесь у экспертов

Edureka

Преподаватель курса

AI-Powered Data Engineering with Snowflake
В каталоге вашей программы

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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 7.4 ч

4 модулей

Язык: Английский

Часть программы вашего университета
Setting Up Snowflake and the GlobalMart EnvironmentВидео
Understanding Snowflake as a Cloud Data PlatformЧтение
GenAI Concepts & AI-Enhanced ArchitectureВидео
Building a Basic Data Pipeline in SnowflakeВидео
Modern Data Engineering and AI FoundationsЗадание
Using GenAI to Understand and Describe Your DataВидео
Responsible Use of AI in Data WorkflowsЧтение
Natural Language to SQL on SnowflakeВидео
AI-Assisted Data Profiling and Quality AssessmentВидео
Using AI to Generate and Explain Pipeline CodeВидео
Knowledge Check: Foundations of AI-Enhanced Data EngineeringЗадание
02 AI-Enhanced Data Ingestion, Transformation and Quality14 материалов
Data Ingestion Patterns & AI-Assisted Schema UnderstandingВидеоDesigning Reliable Ingestion Workflows for AI-Ready DataЧтениеIngesting Multi-Source Data into the Bronze LayerВидеоCloud Data Platform Storage Design for GenAI WorkflowsВидеоBronze, Silver and Gold Layers in Data EngineeringЧтениеAI-Assisted Schema Mapping and Conflict ResolutionВидеоData Ingestion, Storage, and TransformationЗаданиеBuilding the Silver Layer - Curated TablesВидеоAI-Assisted Data Transformation and QualityВидеоAI-Assisted Data Quality ChecksВидеоEnsuring Data Reliability in AI-Enhanced PipelinesЧтениеBuilding the Gold Layer and Automated DocumentationВидеоReflecting on AI-Enhanced Data Pipeline DesignDIALOGUEKnowledge Check: AI-Enhanced Data Ingestion, Transformation and QualityЗадание
03Intelligent Data Access with Generative AI12 материалов
From Data Pipelines to Intelligent Data AccessВидеоCombining Structured and Unstructured Data for AI AnalyticsЧтениеSemantic Search Over Data DocumentationВидеоEmbeddings & Semantic Search for Data EngineersВидеоHow Embeddings Enable Semantic Data SearchЧтениеIntelligent Data Access and RAGЗаданиеBuilding a RAG Assistant for Data KnowledgeВидеоRAG for Data Platforms - Grounding AI in Trusted DataВидеоNatural Language Analytics on the Gold LayerВидеоBuilding Trusted Retrieval Workflows for Enterprise DataЧтениеCombining Structured and Unstructured Data AccessВидеоKnowledge Check: Intelligent Data Access with Generative AI Задание
04 Production, Governance, and Enterprise Deployment13 материалов
Production Operations for AI-Enhanced Data EngineeringВидеоOperational Best Practices for AI-Enhanced Data PipelinesЧтениеError Handling, Retry Logic, Logging and MonitoringВидеоQuality, Evaluation, and Governance for AI PipelinesВидеоGovernance and Access Control in AI Data PlatformsЧтениеProduction Operations and AI GovernanceЗаданиеOrchestrating AI-Enhanced Pipelines with Snowflake TasksВидеоEvaluating AI Outputs in Data Engineering WorkflowsЧтение GlobalMart Production Go-LiveВидеоPractice Project: Building an AI-Enhanced Data Intelligence PlatformЧтениеTrusted AI Banking Data ChallengeDIALOGUEEnd Course Knowledge Check: AI-Powered Data Engineering with SnowflakeЗаданиеCourse SummaryВидео