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Building ETL Pipelines with SQL - Portuguese BR

Este curso ensina como criar ETL pipelines prontos para produção usando SQL puro na Databricks Data Intelligence Platform. Os alunos aprendem Streaming Tables com Auto Loader para ingestão incremental, Materialized Views com atualização incremental para transformações Prata para Ouro, AUTO CDC (FLOW AUTO CDC) para gerenciamento declarativo de dimensões SCD Tipo 1 e Tipo 2, e Lakeflow Jobs com tarefas de arquivo SQL para orquestração em produção. O curso segue um dataset de varejo realista através da arquitetura medalhão (Bronze → Prata → Ouro).


Observação: a Databricks Academy está mudando para um formato baseado em cadernos para as aulas presenciais no ambiente Databricks, deixando de usar apresentações de slides para as palestras. Você pode acessar os cadernos das palestras no ambiente de laboratório Vocareum.

Skill Level
Associate
Duration
4h
Prerequisites

O conteúdo foi desenvolvido para os participantes com as seguintes competências/conhecimentos/habilidades: 

• Navegando no Databricks workspace (barra lateral, Catalog Explorer, SQL Editor)

• Noções básicas do Unity Catalog (catálogos, esquemas, tabelas, volumes)

• SQL intermediário (SELECT, JOIN, GROUP BY, CAST, COALESCE, CREATE TABLE)

• Conceitos de data warehousing (tabelas de fatos/dimensões, esquemas em estrela, arquitetura medallion)

• Noções básicas de fluxos de trabalho ETL

Outline

SQL ETL no Databricks

• SQL ETL on Databricks: The Big Picture

• Demo: Exploring the Course Dataset and SQL Editor

• Lab: Using the SQL Editor and Genie Code


Streaming Tables e Materialized Views

• Building SQL ETL Pipelines

• Demo: Building a Silver-to-Gold Pipeline

• Lab: Building a Customer Feedback Pipeline


Auto CDC

• AUTO CDC Streaming Dimension Updates

• Demo: Building Slowly Changing Dimensions with AUTO CDC

• Lab: Building Slowly Changing Dimensions


Orquestração com Lakeflow Jobs

• Orchestrating SQL Pipelines with Lakeflow Jobs

• Demo: Building a Lakeflow Job for the ETL Pipeline

• Lab: Orchestrating SQL Pipelines with Lakeflow Jobs

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