Data Engineering - DriveDataScience

Power BI Deployment and Sharing: Deployment Pipelines, Lakehouse Rebinding, Semantic Model Promotion, Variable Libraries, Power BI Apps, Row-Level Security, Workspace Roles, and What Actually Happens When You Promote to Production

Leave a Comment / Data Engineering, Microsoft Fabric, Power BI

The complete Power BI deployment and sharing guide for data engineers. Fabric Deployment Pipelines from dev to test to prod. What happens during promotion item by item. Deployment rules for environment-specific values. Lakehouse rebinding with parameter rules auto-binding and Variable Libraries. Semantic model automatic rebinding. What does NOT transfer during promotion. Will reports work without attaching the target Lakehouse. Variable Libraries for environment-agnostic code. Five sharing methods with Power BI Apps recommended. Row-level security static vs dynamic with USERPRINCIPALNAME. Workspace roles. Complete end-to-end deployment workflow. Eight common mistakes and seven interview Q and As.

Power BI Deployment and Sharing: Deployment Pipelines, Lakehouse Rebinding, Semantic Model Promotion, Variable Libraries, Power BI Apps, Row-Level Security, Workspace Roles, and What Actually Happens When You Promote to Production Read More »

Legacy Power BI vs Fabric Power BI: What Changed, What Stayed, Semantic Models, Direct Lake, P SKU to F SKU, OneLake, Composite Models, Git Integration, Web Modeling, and the Complete Migration Path

Leave a Comment / Data Engineering, Microsoft Fabric, Power BI

The complete Legacy vs Fabric Power BI comparison for data engineers. What changed and what stayed the same. Datasets renamed to semantic models. Workspaces expanded for all Fabric items. OneLake as unified storage. P SKU to F SKU licensing shift with capacity sharing. Direct Lake replacing Import for Fabric Lakehouses. Import vs DirectQuery vs Direct Lake deep comparison. Default semantic model decoupling. Composite models mixing Direct Lake and Import. Web modeling in the browser. Git integration for version control. Complete migration path from legacy to Fabric. When to convert to Direct Lake and when not to. Eight common mistakes and seven interview Q and As.

Legacy Power BI vs Fabric Power BI: What Changed, What Stayed, Semantic Models, Direct Lake, P SKU to F SKU, OneLake, Composite Models, Git Integration, Web Modeling, and the Complete Migration Path Read More »

Data Modeling for Power BI: Star Schema, Fact and Dimension Tables, Surrogate Keys, Relationships, Cardinality, Cross-Filter Direction, Role-Playing Dimensions, Bridge Tables, and Building Models Data Engineers Are Proud Of

Leave a Comment / Data Engineering, Power BI

The complete Power BI data modeling guide for data engineers. Star schema as the foundation. Fact tables with transaction periodic and accumulating types. Dimension tables with denormalization. Surrogate keys vs natural keys. Relationships and filter propagation. Cardinality one-to-many many-to-many one-to-one. Cross-filter direction single vs bidirectional. Active vs inactive relationships with USERELATIONSHIP. Role-playing dimensions for multiple date roles. Bridge tables for many-to-many resolution. Conformed and shared dimensions. Snowflake vs star schema. Model optimization checklist. Complete example with measures. Eight common mistakes and seven interview Q and As.

Data Modeling for Power BI: Star Schema, Fact and Dimension Tables, Surrogate Keys, Relationships, Cardinality, Cross-Filter Direction, Role-Playing Dimensions, Bridge Tables, and Building Models Data Engineers Are Proud Of Read More »

DAX for Data Engineers: CALCULATE, Filter Context, Row Context, Measures vs Calculated Columns, ALL, FILTER, RELATED, Time Intelligence, Iterator Functions, Variables, and the Formulas You Actually Need

Leave a Comment / Data Engineering, Power BI

The complete DAX guide for data engineers. DAX vs SQL thinking differently. Measures vs calculated columns with the critical distinction. Row context vs filter context explained with analogies. Essential aggregation functions. CALCULATE for modifying filter context. ALL for removing filters and percentage of total. FILTER for custom filter tables. RELATED for star schema navigation. Iterator functions SUMX AVERAGEX RANKX. The Date table for time intelligence. TOTALYTD SAMEPERIODLASTYEAR DATEADD and month-over-month. Variables for clean DAX. Practical patterns including running totals and moving averages. Eight common mistakes and seven interview Q and As.

DAX for Data Engineers: CALCULATE, Filter Context, Row Context, Measures vs Calculated Columns, ALL, FILTER, RELATED, Time Intelligence, Iterator Functions, Variables, and the Formulas You Actually Need Read More »

Power BI Visualizations and Dashboards: Every Chart Type, Bar, Line, Pie, Scatter, Waterfall, Treemap, Funnel, Gauge, KPI Cards, Matrix, Maps, Slicers, Drill-Through, Bookmarks, and Choosing the Right Chart

Leave a Comment / Data Engineering, Power BI

The complete Power BI visualizations guide for data engineers. Every chart type explained with when to use and when not to use. Bar and column charts for comparisons. Line and area charts for trends. Pie and donut for proportions. Scatter and bubble for correlations. Waterfall for cumulative impact. Treemap for hierarchies. Funnel for stages. Gauge and KPI cards for targets. Tables and matrix for details. Maps for geographic data. Chart selection decision guide. Slicers for interactive filtering. Drill-through for detail pages. Bookmarks for navigation. Conditional formatting and tooltips. Reports vs dashboards. Eight common mistakes and seven interview Q and As.

Power BI Visualizations and Dashboards: Every Chart Type, Bar, Line, Pie, Scatter, Waterfall, Treemap, Funnel, Gauge, KPI Cards, Matrix, Maps, Slicers, Drill-Through, Bookmarks, and Choosing the Right Chart Read More »

Power BI Architecture for Data Engineers: Desktop vs Service, Semantic Models, Import vs DirectQuery vs Direct Lake, Gateways, Data Refresh, Workspaces, Licensing, and Where Data Engineering Meets BI

Leave a Comment / Data Engineering, Power BI

The complete Power BI architecture guide for data engineers. Desktop vs Service and when to use each. Semantic models with tables relationships measures and RLS. Import mode with VertiPaq engine and scheduled refresh. DirectQuery with live source queries. Direct Lake with Fabric OneLake Delta tables. Three storage modes compared with decision guide. On-premises gateway types and when needed. Data refresh patterns including incremental refresh. Workspaces and roles. Pro vs PPU vs Premium vs Fabric licensing. Where data engineering meets Power BI. Eight common mistakes and seven interview Q and As.

Power BI Architecture for Data Engineers: Desktop vs Service, Semantic Models, Import vs DirectQuery vs Direct Lake, Gateways, Data Refresh, Workspaces, Licensing, and Where Data Engineering Meets BI Read More »

YAML Pipelines Deep Dive for Data Engineers: Triggers, Variables, Parameters, Conditions, Templates, Multi-Stage Deployments, Approval Gates, Matrix Strategy, Each Loops, and Production Pipeline Patterns

Leave a Comment / Azure, Data Engineering, DevOps

The complete YAML Pipelines deep dive for data engineers. Triggers for push PR scheduled and pipeline chaining. Variables at every scope with Key Vault integration. Parameters with types and allowed values. Conditions for runtime and compile-time control. Templates for reusable steps jobs and stages. Multi-stage pipelines from dev to prod. Environments with approval gates. Matrix strategy for parallel execution. Each loops for dynamic stage generation. Pipeline artifacts for passing data between stages. Real-world CI and CD examples for Databricks and Terraform. Eight common mistakes and seven interview Q and As.

YAML Pipelines Deep Dive for Data Engineers: Triggers, Variables, Parameters, Conditions, Templates, Multi-Stage Deployments, Approval Gates, Matrix Strategy, Each Loops, and Production Pipeline Patterns Read More »

Terraform for Data Engineers: HCL, Providers, Resources, Variables, State Management, Provisioning Storage Accounts, Databricks Workspaces, Unity Catalog, Key Vaults, and Multi-Environment Deployment

Leave a Comment / Azure, Data Engineering, DevOps

The complete Terraform guide for data engineers. HCL fundamentals and file layout. Providers for Azure Databricks and Snowflake. Resources for creating infrastructure. Data sources for reading existing resources. Variables with types and validation. Outputs and locals. The init plan apply destroy workflow. Remote state management with Azure Blob backend. Provisioning ADLS Gen2 storage accounts Key Vaults and Databricks workspaces. Configuring clusters SQL Warehouses Unity Catalog storage credentials and external locations. Multi-environment deployment with tfvars. Complete data platform project structure. Eight common mistakes and seven interview Q and As.

Terraform for Data Engineers: HCL, Providers, Resources, Variables, State Management, Provisioning Storage Accounts, Databricks Workspaces, Unity Catalog, Key Vaults, and Multi-Environment Deployment Read More »

Terraform Modules and CI/CD for Data Engineers: Writing Reusable Modules, Module Composition, Registry and Versioning, Deploying Through Azure DevOps YAML Pipelines, Plan-Apply Pattern, Pipeline Templates, and Terraform vs DABs

Leave a Comment / Azure, Data Engineering, DevOps

The complete Terraform modules and CI/CD guide. Why modules matter for infrastructure at scale. Building reusable modules for storage accounts Databricks workspaces and Key Vaults. Module inputs outputs and contracts. Module composition for data platforms. Registry and versioning. Deploying Terraform through Azure DevOps YAML pipelines with plan-apply pattern. Pipeline templates for Terraform. Multi-environment CI/CD with each loops. Terraform vs DABs decision guide. Eight common mistakes and seven interview Q and As.

Terraform Modules and CI/CD for Data Engineers: Writing Reusable Modules, Module Composition, Registry and Versioning, Deploying Through Azure DevOps YAML Pipelines, Plan-Apply Pattern, Pipeline Templates, and Terraform vs DABs Read More »

CI/CD for Databricks with Azure DevOps: Service Principals, DABs Deployment, YAML Pipelines, Notebook Testing, Unity Catalog Promotion, Workspace-First Development, and Production Deployment Patterns

Leave a Comment / Azure, Data Engineering, DevOps

The complete CI/CD guide for Databricks with Azure DevOps. Service principal setup for non-human authentication. Databricks CLI installation in pipelines. DABs deployment through YAML pipelines with validate deploy and run. Multi-environment databricks.yml with targets for dev staging and prod. CI pipeline with bundle validation linting and unit tests. CD pipeline with approval gates. Notebook testing with local PySpark and integration tests. Unity Catalog promotion across environments. Workspace-first development with Git-backed deployment. Complete end-to-end pipeline. Eight common mistakes and seven interview Q and As.

CI/CD for Databricks with Azure DevOps: Service Principals, DABs Deployment, YAML Pipelines, Notebook Testing, Unity Catalog Promotion, Workspace-First Development, and Production Deployment Patterns Read More »