{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-synapsevscode-synapse","slug":"fabric-data-engineering-vs-code","name":"Fabric Data Engineering VS Code","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=SynapseVSCode.synapse","page_url":"https://unfragile.ai/fabric-data-engineering-vs-code","categories":["code-editors"],"tags":["Azure","Data Engineering and Data Science","Fabric","icon-theme","jupyter","keybindings","language-model-tools","Machine Learning","python","remote-menu","Spark","Synapse","theme","tools"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-synapsevscode-synapse__cap_0","uri":"capability://code.generation.editing.remote.spark.notebook.execution.with.local.editing","name":"remote-spark-notebook-execution-with-local-editing","description":"Enables developers to author Jupyter notebooks locally in VS Code while executing code cells against remote Microsoft Fabric Spark pools, with bidirectional synchronization of notebook state and output. The extension intercepts notebook cell execution requests, serializes them to the remote Spark cluster via the Fabric platform API, and streams execution results back to the local notebook interface for real-time display.","intents":["I want to write PySpark code in my local VS Code environment but run it against a shared Fabric Spark cluster without uploading files manually","I need to iterate on data transformation logic quickly with immediate feedback from a remote cluster","I want to keep my notebooks in version control while executing against production Fabric resources"],"best_for":["data engineers building ETL pipelines on Microsoft Fabric","teams collaborating on shared Spark clusters with local development workflows","organizations standardized on VS Code for data engineering work"],"limitations":["Requires active network connection to Microsoft Fabric platform — no offline notebook editing or local Spark execution fallback","Notebook format must be compatible with Fabric Notebooks; standard Jupyter .ipynb compatibility unknown","Remote execution latency adds 500ms–2s per cell execution depending on cluster startup state and network conditions","Debugging requires remote Spark pool support; local breakpoints may not map correctly to distributed execution context"],"requires":["VS Code 1.60+ (minimum version not explicitly documented)","Jupyter VS Code Extension Pack (mandatory dependency)","Java Development Kit (JDK, not JRE — specific version unknown)","Active Microsoft Fabric workspace with provisioned Spark pool","Network access to Microsoft Fabric platform endpoints"],"input_types":["Jupyter notebook cells (Python/PySpark code)","Notebook metadata and cell state","Spark pool connection configuration"],"output_types":["Cell execution results (stdout/stderr)","Structured data outputs (DataFrames, tables)","Execution logs and error messages","Notebook state synchronization"],"categories":["code-generation-editing","automation-workflow","remote-execution"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-synapsevscode-synapse__cap_1","uri":"capability://search.retrieval.lakehouse.structure.exploration.and.table.inspection","name":"lakehouse-structure-exploration-and-table-inspection","description":"Provides a sidebar explorer view that displays the hierarchical structure of connected Fabric Lakehouses, allowing developers to browse tables, folders, and metadata without leaving VS Code. The extension queries Fabric platform metadata APIs to populate a tree view of lakehouse assets and enables inline table data preview and schema inspection through context menu actions.","intents":["I want to explore available tables and datasets in my Fabric Lakehouse without switching to the web UI","I need to quickly inspect table schemas and sample data while writing transformation code","I want to discover available data assets and understand their structure before writing queries"],"best_for":["data engineers exploring unfamiliar Fabric Lakehouses","teams building data pipelines who need quick schema reference during development","analysts discovering available datasets for analysis"],"limitations":["Metadata refresh rate unknown — may not reflect real-time changes to lakehouse structure","Table preview limited to sample rows (exact row count limit not documented)","No support for querying or filtering table data directly from explorer — requires notebook cells for analysis","Schema inspection may not include partition information or data statistics"],"requires":["Active connection to Microsoft Fabric workspace","Read permissions on target Lakehouse","Network access to Fabric metadata APIs"],"input_types":["Fabric Lakehouse connection configuration","User-selected table or folder paths"],"output_types":["Hierarchical tree view of lakehouse structure","Table schema information (column names, types)","Sample table data (preview rows)","Metadata (table size, row count — if available)"],"categories":["search-retrieval","data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-synapsevscode-synapse__cap_10","uri":"capability://code.generation.editing.jupyter.notebook.format.compatibility.and.conversion","name":"jupyter-notebook-format-compatibility-and-conversion","description":"Handles conversion and compatibility between standard Jupyter notebook format (.ipynb) and Fabric Notebook format, enabling seamless editing of Fabric notebooks in VS Code's native Jupyter editor. The extension transparently converts between formats during load/save operations, preserving cell metadata, execution state, and Fabric-specific properties.","intents":["I want to edit Fabric notebooks using VS Code's native Jupyter editor without format conversion","I need to import standard Jupyter notebooks into Fabric and maintain compatibility","I want to export Fabric notebooks to standard Jupyter format for sharing or archiving"],"best_for":["data engineers transitioning from standard Jupyter to Fabric notebooks","teams sharing notebooks across Jupyter and Fabric ecosystems","users requiring notebook portability and format flexibility"],"limitations":["Fabric-specific notebook properties may be lost during conversion to standard Jupyter format","Conversion fidelity unknown — some metadata or cell types may not round-trip correctly","No support for notebook versioning or format migration history","Large notebooks may have conversion performance issues (not documented)"],"requires":["Jupyter VS Code Extension Pack (mandatory dependency)","VS Code 1.60+ (minimum version not explicitly documented)"],"input_types":["Jupyter notebook files (.ipynb)","Fabric notebook files (format unknown)"],"output_types":["Converted notebook in target format","Preserved cell content, metadata, and execution state"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-synapsevscode-synapse__cap_2","uri":"capability://code.generation.editing.spark.job.definition.development.and.remote.execution","name":"spark-job-definition-development-and-remote-execution","description":"Allows developers to create, edit, and execute Spark Job Definitions (compiled Spark applications) locally in VS Code, with deployment and execution against remote Fabric Spark pools. The extension provides syntax highlighting and validation for job definition files, handles packaging and submission to the Fabric platform, and streams job execution logs back to the VS Code terminal.","intents":["I want to develop and test Spark job definitions locally before deploying to production Fabric clusters","I need to schedule and monitor Spark job execution from VS Code without using the web UI","I want to version control Spark job definitions alongside my data engineering code"],"best_for":["data engineers building production Spark applications on Fabric","teams automating ETL workflows with scheduled Spark jobs","developers migrating from on-premises Spark to Fabric"],"limitations":["Job definition format and supported languages (PySpark, Scala, Java) not fully documented","No local Spark execution — requires remote cluster for testing, increasing iteration time","Job scheduling and monitoring capabilities unknown — may require Fabric web UI for advanced scheduling","Dependency management (JAR files, Python packages) approach not documented"],"requires":["VS Code 1.60+ (minimum version not explicitly documented)","Java Development Kit (JDK, not JRE)","Active Microsoft Fabric workspace with Spark pool","Appropriate permissions to create and execute Spark jobs in Fabric"],"input_types":["Spark job definition files (format unknown)","Application code (PySpark/Scala/Java)","Job configuration and parameters"],"output_types":["Job execution logs (stdout/stderr)","Job status and completion results","Performance metrics (if available from Fabric)"],"categories":["code-generation-editing","automation-workflow","remote-execution"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-synapsevscode-synapse__cap_3","uri":"capability://code.generation.editing.remote.spark.notebook.debugging.with.breakpoints","name":"remote-spark-notebook-debugging-with-breakpoints","description":"Enables developers to set breakpoints in notebook cells and debug code execution on remote Spark pools, with variable inspection and step-through execution. The extension uses VS Code's debug protocol to communicate with the remote Spark cluster's debug server, mapping local breakpoints to distributed execution contexts and streaming variable state back to the debugger UI.","intents":["I want to debug PySpark code running on a remote cluster without adding print statements","I need to inspect variable values and data structures during notebook execution","I want to step through complex transformation logic to understand where failures occur"],"best_for":["data engineers troubleshooting complex PySpark transformations","teams debugging distributed data processing logic","developers new to Spark who need visibility into execution flow"],"limitations":["Debugging distributed Spark execution may not capture all executor-side state — only driver-side variables accessible","Breakpoint mapping to distributed context unknown — may miss breakpoints in executor code","Debug session latency unknown — stepping through code may be slow due to network round-trips","No support for conditional breakpoints or watch expressions (not documented)"],"requires":["Remote Spark pool with debug server support (feature support unknown)","Network access to Spark cluster debug endpoints","Appropriate firewall rules allowing debug protocol communication"],"input_types":["Notebook cell code","Breakpoint locations (line numbers)","Variable inspection requests"],"output_types":["Variable values and types","Stack traces and execution context","Execution pause/resume signals"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-synapsevscode-synapse__cap_4","uri":"capability://tool.use.integration.fabric.workspace.and.spark.pool.connection.management","name":"fabric-workspace-and-spark-pool-connection-management","description":"Provides configuration and connection management for Microsoft Fabric workspaces and Spark pools through VS Code settings and command palette, handling authentication, workspace selection, and pool configuration. The extension stores connection credentials securely using VS Code's credential storage API and manages active connections for notebook and job execution.","intents":["I want to connect to my Fabric workspace from VS Code without manually configuring authentication","I need to switch between multiple Fabric workspaces or Spark pools for different projects","I want to securely store and manage Fabric credentials without exposing them in configuration files"],"best_for":["data engineers working with multiple Fabric workspaces","teams managing shared Spark pool resources","organizations requiring secure credential management in development environments"],"limitations":["Authentication mechanism not documented — unclear if using OAuth, service principals, or managed identity","Credential storage relies on VS Code's credential store — security depends on OS-level credential management","No support for multi-factor authentication (MFA) or conditional access policies (not documented)","Connection timeout and retry logic unknown — may fail silently on network issues"],"requires":["Microsoft Fabric account with workspace access","VS Code credential storage support (available on Windows, macOS, Linux with appropriate OS credential managers)","Network access to Fabric authentication endpoints"],"input_types":["Fabric workspace ID or name","Spark pool name or ID","Authentication credentials (type unknown)"],"output_types":["Active connection status","Available workspaces and pools (for selection)","Connection configuration (stored securely)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-synapsevscode-synapse__cap_5","uri":"capability://automation.workflow.notebook.synchronization.between.local.and.remote.fabric","name":"notebook-synchronization-between-local-and-remote-fabric","description":"Automatically synchronizes notebook content between local VS Code workspace and remote Fabric platform, ensuring consistency across development and execution environments. The extension detects local notebook changes, uploads them to Fabric, and pulls remote updates (from collaborative edits or platform changes) back to the local workspace using a merge-based synchronization strategy.","intents":["I want to keep my local notebook files in sync with Fabric without manual uploads","I need to collaborate with teammates on notebooks while maintaining local version control","I want to ensure my local notebook reflects the latest state on the Fabric platform"],"best_for":["teams collaborating on shared Fabric notebooks","data engineers using Git for notebook version control","organizations requiring audit trails of notebook changes"],"limitations":["Merge conflict resolution strategy unknown — may overwrite local or remote changes without warning","Synchronization frequency not documented — may have latency between local edits and remote updates","No support for selective sync or partial notebook uploads","Conflict handling with concurrent edits from multiple users unknown"],"requires":["Active connection to Fabric workspace","Write permissions on target notebooks","Network connectivity for sync operations"],"input_types":["Local notebook files (.ipynb or Fabric format)","Remote notebook state from Fabric platform"],"output_types":["Synchronized notebook content","Sync status and conflict indicators","Change history (if tracked)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-synapsevscode-synapse__cap_6","uri":"capability://code.generation.editing.syntax.highlighting.and.language.support.for.fabric.artifacts","name":"syntax-highlighting-and-language-support-for-fabric-artifacts","description":"Provides syntax highlighting, code completion, and language support for Fabric-specific file formats (notebooks, Spark job definitions, Lakehouse metadata) within VS Code's editor. The extension registers custom language modes and uses TextMate grammars or language server protocols to enable intelligent code editing for PySpark, Scala, and SQL within Fabric contexts.","intents":["I want proper syntax highlighting for PySpark code in Fabric notebooks","I need code completion and IntelliSense for Fabric-specific APIs and libraries","I want to catch syntax errors in Spark job definitions before remote execution"],"best_for":["data engineers writing PySpark and Scala code in Fabric notebooks","teams standardizing on VS Code for Fabric development","developers new to Spark who benefit from IDE-like code assistance"],"limitations":["Code completion accuracy depends on available Fabric SDK stubs — may be incomplete for newer APIs","No real-time linting or error checking during editing (relies on remote execution for validation)","Language server support unknown — may not provide full IntelliSense for distributed Spark APIs","SQL syntax highlighting may not account for Fabric-specific SQL dialect extensions"],"requires":["VS Code 1.60+ (minimum version not explicitly documented)","Language-specific extensions for Python, Scala, or SQL (may be dependencies)"],"input_types":["Notebook cell code (Python, Scala, SQL)","Spark job definition files"],"output_types":["Syntax-highlighted code","Code completion suggestions","Error and warning indicators"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-synapsevscode-synapse__cap_7","uri":"capability://automation.workflow.fabric.platform.command.palette.integration","name":"fabric-platform-command-palette-integration","description":"Exposes Fabric platform operations (workspace selection, job submission, notebook execution, data exploration) through VS Code's command palette, enabling keyboard-driven workflows without UI navigation. The extension registers custom commands that map to Fabric platform APIs and execute asynchronously, with progress indicators and result notifications in VS Code's status bar.","intents":["I want to execute Fabric operations using keyboard shortcuts without leaving the editor","I need quick access to common Fabric tasks (run notebook, submit job, explore data) from the command palette","I want to automate Fabric workflows using VS Code's command execution system"],"best_for":["power users and keyboard-driven developers","teams automating Fabric workflows through VS Code extensions or scripts","developers who prefer command-line interfaces over GUI navigation"],"limitations":["Specific command names and parameters not documented — requires trial-and-error or documentation lookup","No command history or favorites system (not documented)","Complex operations may require multiple command palette invocations","Error messages may be unclear or require Fabric platform knowledge to debug"],"requires":["VS Code 1.60+ (minimum version not explicitly documented)","Active Fabric workspace connection"],"input_types":["Command name and parameters (user-entered via command palette)","Current file or selection context"],"output_types":["Command execution results","Status notifications","Output in VS Code terminal or panel"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-synapsevscode-synapse__cap_8","uri":"capability://safety.moderation.telemetry.collection.with.user.opt.out","name":"telemetry-collection-with-user-opt-out","description":"Collects usage telemetry data (command execution, feature usage, error rates) and sends it to Microsoft for product improvement, with a user-configurable opt-out setting. The extension respects VS Code's telemetry framework and allows disabling data collection via the `telemetry.enableTelemetry` setting without affecting core functionality.","intents":["I want to understand how the extension is being used to improve product development","I need to disable telemetry collection for privacy or compliance reasons","I want to ensure my usage data is not collected without explicit consent"],"best_for":["Microsoft product teams analyzing extension usage patterns","organizations with strict data privacy policies","users concerned about data collection and privacy"],"limitations":["Telemetry data types and retention policies not documented","No granular control over which data is collected (all-or-nothing opt-out)","No transparency into what data is sent or how it's used","Telemetry disabling may affect product improvement and support capabilities"],"requires":["VS Code 1.60+ (minimum version not explicitly documented)"],"input_types":["User setting: `telemetry.enableTelemetry` (boolean)"],"output_types":["Telemetry data sent to Microsoft (format and contents unknown)","Confirmation of telemetry setting change"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-synapsevscode-synapse__cap_9","uri":"capability://automation.workflow.theme.and.keybinding.customization.for.fabric.development","name":"theme-and-keybinding-customization-for-fabric-development","description":"Provides custom VS Code themes and keybinding presets optimized for Fabric data engineering workflows, including color schemes for notebook cells, Spark output, and Lakehouse explorer. The extension registers theme contributions and keybinding configurations that can be selected via VS Code's preferences, with support for user customization and extension of default bindings.","intents":["I want a color scheme optimized for reading Spark output and notebook results","I need keyboard shortcuts tailored to common Fabric data engineering tasks","I want to customize the VS Code appearance for Fabric-specific workflows"],"best_for":["data engineers spending long hours in VS Code with Fabric notebooks","teams standardizing on consistent keybindings for Fabric workflows","users with accessibility needs requiring specific color contrasts or font sizes"],"limitations":["Theme customization limited to extension-provided presets — no live theme editor","Keybinding conflicts with other extensions may require manual resolution","No support for per-workspace theme or keybinding overrides","Theme and keybinding changes require VS Code restart (not documented)"],"requires":["VS Code 1.60+ (minimum version not explicitly documented)"],"input_types":["User preference selections (theme, keybinding preset)","Custom keybinding overrides (JSON configuration)"],"output_types":["Applied theme (colors, fonts, UI styling)","Active keybindings (keyboard shortcut mappings)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.60+ (minimum version not explicitly documented)","Jupyter VS Code Extension Pack (mandatory dependency)","Java Development Kit (JDK, not JRE — specific version unknown)","Active Microsoft Fabric workspace with provisioned Spark pool","Network access to Microsoft Fabric platform endpoints","Active connection to Microsoft Fabric workspace","Read permissions on target Lakehouse","Network access to Fabric metadata APIs","Java Development Kit (JDK, not JRE)","Active Microsoft Fabric workspace with Spark pool"],"failure_modes":["Requires active network connection to Microsoft Fabric platform — no offline notebook editing or local Spark execution fallback","Notebook format must be compatible with Fabric Notebooks; standard Jupyter .ipynb compatibility unknown","Remote execution latency adds 500ms–2s per cell execution depending on cluster startup state and network conditions","Debugging requires remote Spark pool support; local breakpoints may not map correctly to distributed execution context","Metadata refresh rate unknown — may not reflect real-time changes to lakehouse structure","Table preview limited to sample rows (exact row count limit not documented)","No support for querying or filtering table data directly from explorer — requires notebook cells for analysis","Schema inspection may not include partition information or data statistics","Fabric-specific notebook properties may be lost during conversion to standard Jupyter format","Conversion fidelity unknown — some metadata or cell types may not round-trip correctly","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.59,"quality":0.47,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:36.253Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=fabric-data-engineering-vs-code","compare_url":"https://unfragile.ai/compare?artifact=fabric-data-engineering-vs-code"}},"signature":"E5pRJFGRNCZXATMx/ASkP5pWNeCAbYvuNYdW/23nMXK9dnT/SnmGcDFGoybq+FEa6XOVWjBIgCr7BEYx2xhvBQ==","signedAt":"2026-06-22T05:34:48.024Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/fabric-data-engineering-vs-code","artifact":"https://unfragile.ai/fabric-data-engineering-vs-code","verify":"https://unfragile.ai/api/v1/verify?slug=fabric-data-engineering-vs-code","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}