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Databricks For Data Engineers: The Complete Platform
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.41 GB | Duration: 4h 0m
Delta Lake, PySpark, Unity Catalog, MLflow, Workflows, DABs - the full lakehouse stack from basics to a real capstone[/center].
What you'll learn
Navigate the Databricks workspace and write production notebooks in SQL and Python
Build a complete medallion pipeline (bronze to silver to gold) with Delta Lake
Implement batch and streaming ingestion using Auto Loader, Structured Streaming, and DLT
Handle SCDs (Type 1 and Type 2) with Delta Lake MERGE at scale
Configure Unity Catalog for governance: schemas, grants, lineage, and data discovery
Deploy production jobs with Databricks Workflows, Git integration, alerts, and retry logic
Requirements
Basic SQL knowledge (SELECT, JOIN, GROUP BY)
Familiarity with Python fundamentals (variables, functions, loops)
No prior Databricks experience needed - we start from scratch
Description
Databricks replaced five tools with one platform - and this course makes you productive on every layer of it.You will not just watch demos. You will build a complete medallion pipeline on Delta Lake, master PySpark transformations, configure serverless compute, optimize tables with Liquid Clustering, deploy ML models with MLflow, control costs like a pro, and define everything as code with Databricks Asset Bundles and Terraform. The capstone is a full retail analytics platform you build end-to-end.What makes this course different:Story-driven lessons. Every module opens with a real-world scenario - not a feature list.Code-first. Over 40% of slides are runnable code or live demos.Monday-morning implementable. Everything you learn maps directly to production work.Zero hallucination. Real syntax, real APIs, real Databricks features.Modules that connect. Each module builds on the last and sets up the next.The platform you will master: Delta Lake (ACID, time travel, MERGE, schema evolution), the medallion architecture (bronze/silver/gold), batch and streaming ingestion (Auto Loader, Structured Streaming, Delta Live Tables), Unity Catalog governance (catalogs, grants, lineage, tags), Delta optimization (OPTIMIZE, Z-ORDER, Liquid Clustering, deletion vectors, Photon), production deployments (Workflows, Repos, alerts, retries), PySpark deep dive (DataFrames, joins, window functions, UDFs, Pandas API), serverless compute and SQL warehouses, MLflow and model serving, cost optimization (DBU model, spot instances, AQE, cluster policies), and infrastructure as code (DABs and Terraform).The capstone: You build a complete retail analytics platform - bronze ingestion via Auto Loader, silver transformations in PySpark, gold aggregations and ML features, MLflow-tracked experiments, a serving endpoint, and a Lakeview dashboard - all deployed via DABs.Who this is for
ata engineers moving from Snowflake, Redshift, BigQuery, or on-prem warehouses to DatabricksAnalytics engineers who want the full lakehouse picture - not just SQLPlatform engineers responsible for governance, cost, and reproducible deploymentsETL developers ready to graduate from notebook prototypes to production pipelinesAnyone preparing for the Databricks Certified Data Engineer Professional exam who wants depth, not just factsBy the end of this course, you will be able to design, build, optimize, govern, deploy, and cost-tune a complete Databricks lakehouse - confidently and from scratch.Enroll now. The lakehouse is where modern data engineering lives. Make it your home.What You'll LearnNavigate the Databricks workspace and write production notebooks in SQL and PythonBuild a complete medallion pipeline (bronze, silver, gold) on Delta LakeImplement batch and streaming ingestion with Auto Loader, Structured Streaming, and Delta Live TablesHandle slowly changing dimensions (Type 1 and Type 2) with Delta Lake MERGE at scaleConfigure Unity Catalog for governance: catalogs, schemas, grants, lineage, and tagsOptimize Delta tables with OPTIMIZE, VACUUM, Z-ORDER, Liquid Clustering, and PhotonDeploy production jobs with Databricks Workflows, Git integration, alerts, and retry logicChoose the right compute for the right workload - classic, serverless, or SQL warehouseMaster PySpark DataFrames, joins, window functions, and UDFs for complex transformationsConfigure and right-size Serverless SQL Warehouses for production BI workloadsApply Liquid Clustering, deletion vectors, and automated maintenance for Delta performanceTrack ML experiments, manage models in the registry, and deploy real-time serving endpointsCut Databricks bills with right-sizing, spot instances, Photon, and cluster policiesDefine your entire Databricks environment as code using Asset Bundles and TerraformBuild a real retail analytics capstone that integrates every layer of the platformRequirementsComfort with SQL - SELECT, JOIN, GROUP BY, CTEsBasic Python - variables, functions, lists, dictionariesFamiliarity with the idea of cloud storage (S3, ADLS, or GCS)A free Databricks Community Edition or trial workspace (links provided in the course)No prior Spark or Databricks experience required - we start from the workspace tourWho This Course Is ForData engineers moving to Databricks from Snowflake, Redshift, or BigQueryAnalytics engineers who want the full lakehouse stack - not just SQLPlatform engineers responsible for governance, cost, and deploymentsETL developers ready to ship production pipelines on DatabricksEngineers preparing for the Databricks Certified Data Engineer Professional exam
Data engineers who want to master the Databricks platform end to end,SQL analysts moving into data engineering and looking for a modern lakehouse stack,Engineers migrating from Snowflake, Hadoop, or Spark to Databricks and need a fast ramp
Databricks For Data Engineers: The Complete Platform
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.41 GB | Duration: 4h 0m
Delta Lake, PySpark, Unity Catalog, MLflow, Workflows, DABs - the full lakehouse stack from basics to a real capstone[/center].
What you'll learn
Navigate the Databricks workspace and write production notebooks in SQL and Python
Build a complete medallion pipeline (bronze to silver to gold) with Delta Lake
Implement batch and streaming ingestion using Auto Loader, Structured Streaming, and DLT
Handle SCDs (Type 1 and Type 2) with Delta Lake MERGE at scale
Configure Unity Catalog for governance: schemas, grants, lineage, and data discovery
Deploy production jobs with Databricks Workflows, Git integration, alerts, and retry logic
Requirements
Basic SQL knowledge (SELECT, JOIN, GROUP BY)
Familiarity with Python fundamentals (variables, functions, loops)
No prior Databricks experience needed - we start from scratch
Description
Databricks replaced five tools with one platform - and this course makes you productive on every layer of it.You will not just watch demos. You will build a complete medallion pipeline on Delta Lake, master PySpark transformations, configure serverless compute, optimize tables with Liquid Clustering, deploy ML models with MLflow, control costs like a pro, and define everything as code with Databricks Asset Bundles and Terraform. The capstone is a full retail analytics platform you build end-to-end.What makes this course different:Story-driven lessons. Every module opens with a real-world scenario - not a feature list.Code-first. Over 40% of slides are runnable code or live demos.Monday-morning implementable. Everything you learn maps directly to production work.Zero hallucination. Real syntax, real APIs, real Databricks features.Modules that connect. Each module builds on the last and sets up the next.The platform you will master: Delta Lake (ACID, time travel, MERGE, schema evolution), the medallion architecture (bronze/silver/gold), batch and streaming ingestion (Auto Loader, Structured Streaming, Delta Live Tables), Unity Catalog governance (catalogs, grants, lineage, tags), Delta optimization (OPTIMIZE, Z-ORDER, Liquid Clustering, deletion vectors, Photon), production deployments (Workflows, Repos, alerts, retries), PySpark deep dive (DataFrames, joins, window functions, UDFs, Pandas API), serverless compute and SQL warehouses, MLflow and model serving, cost optimization (DBU model, spot instances, AQE, cluster policies), and infrastructure as code (DABs and Terraform).The capstone: You build a complete retail analytics platform - bronze ingestion via Auto Loader, silver transformations in PySpark, gold aggregations and ML features, MLflow-tracked experiments, a serving endpoint, and a Lakeview dashboard - all deployed via DABs.Who this is for
ata engineers moving from Snowflake, Redshift, BigQuery, or on-prem warehouses to DatabricksAnalytics engineers who want the full lakehouse picture - not just SQLPlatform engineers responsible for governance, cost, and reproducible deploymentsETL developers ready to graduate from notebook prototypes to production pipelinesAnyone preparing for the Databricks Certified Data Engineer Professional exam who wants depth, not just factsBy the end of this course, you will be able to design, build, optimize, govern, deploy, and cost-tune a complete Databricks lakehouse - confidently and from scratch.Enroll now. The lakehouse is where modern data engineering lives. Make it your home.What You'll LearnNavigate the Databricks workspace and write production notebooks in SQL and PythonBuild a complete medallion pipeline (bronze, silver, gold) on Delta LakeImplement batch and streaming ingestion with Auto Loader, Structured Streaming, and Delta Live TablesHandle slowly changing dimensions (Type 1 and Type 2) with Delta Lake MERGE at scaleConfigure Unity Catalog for governance: catalogs, schemas, grants, lineage, and tagsOptimize Delta tables with OPTIMIZE, VACUUM, Z-ORDER, Liquid Clustering, and PhotonDeploy production jobs with Databricks Workflows, Git integration, alerts, and retry logicChoose the right compute for the right workload - classic, serverless, or SQL warehouseMaster PySpark DataFrames, joins, window functions, and UDFs for complex transformationsConfigure and right-size Serverless SQL Warehouses for production BI workloadsApply Liquid Clustering, deletion vectors, and automated maintenance for Delta performanceTrack ML experiments, manage models in the registry, and deploy real-time serving endpointsCut Databricks bills with right-sizing, spot instances, Photon, and cluster policiesDefine your entire Databricks environment as code using Asset Bundles and TerraformBuild a real retail analytics capstone that integrates every layer of the platformRequirementsComfort with SQL - SELECT, JOIN, GROUP BY, CTEsBasic Python - variables, functions, lists, dictionariesFamiliarity with the idea of cloud storage (S3, ADLS, or GCS)A free Databricks Community Edition or trial workspace (links provided in the course)No prior Spark or Databricks experience required - we start from the workspace tourWho This Course Is ForData engineers moving to Databricks from Snowflake, Redshift, or BigQueryAnalytics engineers who want the full lakehouse stack - not just SQLPlatform engineers responsible for governance, cost, and deploymentsETL developers ready to ship production pipelines on DatabricksEngineers preparing for the Databricks Certified Data Engineer Professional examData engineers who want to master the Databricks platform end to end,SQL analysts moving into data engineering and looking for a modern lakehouse stack,Engineers migrating from Snowflake, Hadoop, or Spark to Databricks and need a fast ramp
Code:
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https://www.udemy.com/course/databricks-for-data-engineers-the-complete-platform/Code:
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