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Databricks vs Fabric - Coggle Diagram
Databricks vs Fabric
- Databricks: PaaS (more control)
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- Databricks: Delta Lake (on Customer Cloud Storage)
- Fabric: OneLake (Logical, Delta format on ADLS Gen2)
- Data Integration (ETL/ELT):
- Databricks: Notebooks (Spark), Delta Live Tables (DLT - Code-first), Partner Connect
- Fabric: Data Factory Experience (Pipelines, Dataflows Gen2 - Low-code/No-code focus)
- Databricks: Databricks SQL (Serverless/Pro Warehouses)
- Fabric: Warehouse (SQL), Lakehouse SQL Endpoint
- Databricks: Notebooks (Spark), DLT, Jobs (more granular config)
- Fabric: Notebooks (Spark), Spark Job Definitions, Lakehouse (integrated compute)
- Databricks: Structured Streaming (Spark)
- Fabric: Real-Time Analytics Experience (KQL DBs, Eventstream)
- Databricks: Notebooks, MLflow (Mature), AutoML, Feature Store, Model Serving
- Fabric: Notebooks, MLflow (Integrated), SynapseML, Semantic Link (Power BI), Experiments/Models
- Databricks: Connectors (Power BI, Tableau, etc.)
- Fabric: Power BI Deep Integration (DirectLake - High Performance)
- Databricks: Unity Catalog (Unified, Cross-workspace/Cloud)
- Fabric: Purview Integration, OneLake Security, Domains, Info Protection
- Databricks: High (Open Source core - Delta, MLflow), Multi-cloud
- Fabric: Open Formats (Delta/Parquet), Platform Proprietary SaaS, Azure-centric
- Databricks: DBU-based (Compute usage) + Cloud Infra Costs
- Fabric: CU-based (Capacity - Shared/Reserved) + Storage Costs
- Understand Customer Needs First (State, Strategy, Use Cases, Skills)
- Articulate Clear Value Propositions
- Highlight Differentiators (Tailored to Needs)
- Address Potential Concerns Proactively
- Focus on Solutions, Not Just Features
- Be Objective & Credible (Advise Best Fit)
- Leverage Demos Effectively
- VII. Enablement Plan (4-6 Weeks)
- Objective: Achieve Proficiency in Comparison & Positioning
- Foundational Knowledge (Concepts, Arch, Value Props)
- Feature Deep Dive (Compare Components)
- Hands-On Experience (Trials, Labs, Basic Tasks)
- Pre-Sales Practice (Positioning, Objections, Demos)
- Key Resources: Official Docs, MS Learn/Databricks Academy, Blogs, Labs, Internal Assets
- Outcome: Confident & Credible Advisor on Both Platforms
- Ongoing: Continuous Learning (Blogs, Webinars, Community)
- Core Philosophy: Open Lakehouse, Unify Data/AI
- Architecture: PaaS, Delta Lake, Unity Catalog, Spark Engines
- Focus: Openness, Flexibility, Multi-Cloud (AWS, Azure, GCP), Performance
- Key Components: Workspaces (SQL, DS/Eng, ML), Clusters, DBFS
- Core Philosophy: Unified Analytics, SaaS, Simplified
- Architecture: SaaS, OneLake (Logical ADLS Gen2), Integrated Experiences
- Focus: Simplification, Azure Ecosystem, Power BI Integration, Persona-based
- Key Components: Workspaces, Capacities, Experiences (Data Factory, Synapse DW/Eng/DS/RTA, Power BI), OneLake (Shortcuts)
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- Performance (Optimized Engine)
- Unified Governance (Unity Catalog)
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- Deep Power BI Integration (DirectLake)
- Simplified Architecture (OneLake)
- Persona-Optimized Experiences
- Azure Ecosystem Integration
- IV. Weaknesses / Considerations
- Potential Complexity / Learning Curve
- Integration Effort (vs. built-in)
- Cost Management (Granular)
- BI Integration (less native than Fabric)
- Platform Maturity (Evolving)
- Vendor Lock-in (Platform level)
- Less Infrastructure Control/Flexibility
- Governance Maturity (vs. Unity Catalog)
- V. Ideal Use Cases / Positioning
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- Advanced/Large-Scale ML/AI
- Mature Cross-Cloud Governance Needed Now
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- Azure-Centric / Microsoft Shop
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- Faster Time-to-Value (Common Patterns)
- Data Democratization Goal
- License Consolidation Attractive
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