Primeros pasos
What Is Microsoft Fabric? A Practical Overview
Understand how OneLake, workspaces, Lakehouses, Warehouses, notebooks, pipelines, SQL endpoints and semantic models fit together.
Planificado · Ver en la hoja de ruta →Ruta de aprendizaje guiada
Aprende Microsoft Fabric construyendo
Empieza con un espacio de trabajo y tu primer notebook, y avanza por Lakehouses, tablas Delta, Warehouses, Git, CI/CD e ingeniería a escala de producción.
El contenido técnico está previsto actualmente en inglés. La interfaz de aprendizaje está disponible en español.
01—12
Sigue la ruta esencial desde un espacio vacío hasta un flujo de datos preparado para producción.
Primeros pasos
Understand how OneLake, workspaces, Lakehouses, Warehouses, notebooks, pipelines, SQL endpoints and semantic models fit together.
Planificado · Ver en la hoja de ruta →Primeros pasos
Create a development workspace with sensible capacity, access and ownership decisions from the start.
Planificado · Ver en la hoja de ruta →Tus primeros datos
Build a Lakehouse and understand its Files, Tables and SQL analytics endpoint surfaces.
Planificado · Ver en la hoja de ruta →Tus primeros datos
Use PySpark to load, inspect and summarize a dataset in your first Fabric notebook.
Planificado · Ver en la hoja de ruta →Explora los datos
Practice the DataFrame operations used to explore and shape data in Fabric notebooks.
Planificado · Ver en la hoja de ruta →Explora los datos
Query notebook data with Spark SQL and decide when SQL or PySpark is the clearer tool.
Planificado · Ver en la hoja de ruta →Explora los datos
Persist a DataFrame as a managed Delta table and query it from Spark and SQL.
Planificado · Ver en la hoja de ruta →Aprende a estructurar capas de datos en bruto, conformados y de servicio sin sobredimensionar la plataforma.
Fundamentos
What the Bronze, Silver and Gold layers are for, how data quality and incremental processing work across them in Microsoft Fabric, and when fewer layers are the better design.
Leer tutorial →Fundamentos
A critical look at the Medallion pattern: when two layers or a single curated model are enough, and the cost that each unnecessary layer adds.
Planificado · Ver en la hoja de ruta →Diseño de capas
How to design a Bronze layer that preserves source fidelity: landing formats, ingestion metadata, schema drift, retention and replay.
Planificado · Ver en la hoja de ruta →Diseño de capas
How to turn source-shaped Bronze data into clean, validated, conformed Silver entities that several consumers can share.
Planificado · Ver en la hoja de ruta →Diseño de capas
How to design the Gold layer that reports, semantic models and APIs consume: modelling choices, stable contracts, serving engines and how to change it without breaking consumers.
Planificado · Ver en la hoja de ruta →Patrones de ingeniería
How changes propagate from Bronze to Silver to Gold without full reloads: change detection between layers, affected keys and recomputing only impacted aggregates.
Planificado · Ver en la hoja de ruta →Patrones de ingeniería
Which write pattern belongs in which layer: append in Bronze, MERGE for Silver entities and history, and targeted overwrite or MERGE for Gold.
Planificado · Ver en la hoja de ruta →Patrones de ingeniería
Why the right partitioning often differs by layer, from load-date partitions in Bronze to unpartitioned Gold tables, and how to decide.
Planificado · Ver en la hoja de ruta →Patrones de ingeniería
Where small files come from in each layer and how to plan OPTIMIZE, V-Order and VACUUM per layer instead of applying one schedule everywhere.
Planificado · Ver en la hoja de ruta →Proyecto final
A complete, production-minded Medallion pipeline in Fabric: incremental ingestion, quality gates, MERGE, maintenance, logging and deployment. It builds on the first end-to-end pipeline tutorial.
Planificado · Ver en la hoja de ruta →Lakehouse y Warehouse
The criteria I use to choose between a Fabric Lakehouse and a Fabric Warehouse: team skills, write patterns, T-SQL needs and governance.
Planificado · Ver en la hoja de ruta →Construye con datos
Move CSV or Parquet through a notebook-driven Bronze, Silver and Gold flow into a queryable serving layer.
Planificado · Ver en la hoja de ruta →Notebooks parametrizados
Pass dates and business identifiers from a pipeline into one reusable Fabric notebook.
Planificado · Ver en la hoja de ruta →Primeros pasos
Connect a Fabric workspace to a repository and understand branches, synchronization, versioned artifacts and current limitations.
Planificado · Ver en la hoja de ruta →Git y CI/CD
Use Fabric Git integration deliberately across branches, workspace artifacts and team workflows.
Planificado · Ver en la hoja de ruta →Git y CI/CD
How to promote Fabric items between environments: configuration per environment, data separation, approvals and rollback.
Planificado · Ver en la hoja de ruta →Git y CI/CD
A practical deployment approach for Fabric items using Git integration, deployment pipelines and automation, including what still needs manual steps.
Planificado · Ver en la hoja de ruta →Git y CI/CD
Separate deployable workspace artifacts from data, connections and environment-specific configuration.
Planificado · Ver en la hoja de ruta →Ingeniería de producción
Design an execution log that makes notebook and pipeline runs traceable in production.
Planificado · Ver en la hoja de ruta →Ingeniería de producción
Use a repeatable workflow to diagnose Spark, schema, parameter, dependency and pipeline failures.
Planificado · Ver en la hoja de ruta →Rendimiento y escala
Patterns for loading only new and changed data into Fabric, with watermarks, change tracking and Delta MERGE, and how to handle late or corrected data.
Planificado · Ver en la hoja de ruta →Rendimiento y escala
Build a practical baseline for file layout, Spark execution and Delta writes at multi-million-row scale.
Planificado · Ver en la hoja de ruta →Rendimiento y escala
Plan storage, partitioning, incremental processing and capacity for billion-row Fabric workloads.
Planificado · Ver en la hoja de ruta →Rendimiento y escala
Criteria for choosing a partition column: cardinality, query filters, write patterns and data volume per partition.
Planificado · Ver en la hoja de ruta →Rendimiento y escala
How file size affects read parallelism, metadata overhead and write cost, and how to pick a target size for your engines.
Planificado · Ver en la hoja de ruta →Rendimiento y escala
How Delta MERGE works internally and the patterns that keep it fast: narrowing the target, partition and file pruning, and source deduplication.
Planificado · Ver en la hoja de ruta →Rendimiento y escala
Where small files come from in Delta tables, how they slow down reads, writes and the transaction log, and how to measure the problem.
Planificado · Ver en la hoja de ruta →Rendimiento y escala
A step-by-step workflow for slow Fabric workloads: deciding whether the problem is capacity, Spark, SQL, storage layout or the query itself.
Planificado · Ver en la hoja de ruta →Certification
Connect the official DP-600 or DP-700 skills outline to hands-on Fabric projects, troubleshooting, and production practice.