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It also outlines data sharing, marketplace and partner onboarding, plus accelerators like Lakebridge and solution accelerators for migration and common analytics tasks. Additional sections cover data engineering (Lakeflow, connectors, declarative pipelines, jobs, designer, auto loader), machine learning (MLflow 3.0, AutoML, feature store, runtime), warehousing and federation, BI/Genie visualization workflows, and AI areas including model serving, foundation model serving, vector search, and app building.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/databricks-cheat-sheet-42026/574167/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/databricks-cheat-sheet-42026/574167.png","ImageObject",442,249,{"name":88,"@type":89},"Rizky","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-16","2026-10-14",true,{"@type":98,"interactionType":99,"userInteractionCount":47},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What is the difference between serverless compute and classic compute in Databricks?","Question",{"text":108,"@type":109},"Serverless compute is on-demand and automatically managed, scaling with workload requirements. Classic compute is provisioned and requires users to create, configure, and manage resources.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How does Delta Sharing support secure collaboration?",{"text":113,"@type":109},"Delta Sharing enables secure data sharing within and outside Databricks. Clean Rooms use Delta Sharing and serverless compute to provide a privacy-protecting environment for multiple parties working on sensitive enterprise data.",{"name":115,"@type":106,"acceptedAnswer":116},"Which Databricks services help with machine learning lifecycle and automation?",{"text":117,"@type":109},"MLflow 3.0 simplifies and manages the full ML lifecycle, including experiment tracking and deployment. AutoML rapidly builds, selects, and optimizes models without extensive coding.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},574167,1792020306,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":47,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":9,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":139,"read_time":4},962085564807,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Orange text = serverless only  \nCHEAT SHEET  \nThe Databricks Data Intelligence Platform  \nThe Databricks Data Intelligence Platform is a multicloud unified, open analytics platform for building, deploying, sharing and maintaining enterprise-grade data, analytics and AI solutions at scale.  \n Compute   \nServerless – Serverless compute is on-demand, automatically managed compute that scales based on your workload requirements.  \nClassic – Classic compute is provisioned compute resources that you create, configure and manage for your workloads.  \nSQL Warehouse – SQL warehouses are either classic or serverless and are optimized compute resources for specific use cases and advanced functionality.  \nLakebase – A fully managed Postgres OLTP database engine that allows users to create and manage transactional databases in Databricks-managed storage, seamlessly integrate real-time OLTP workloads with your lakehouse, and support AI/BI workloads that require lower latency queries via reverse ETL patterns.  \nServerless GPU Compute – On-demand, fully managed GPU infrastructure for running ML and AI workloads without manual resource management.  \n Data Sharing and Collaboration  Delta Sharing – The secure data sharing platform that lets users share data and AI assets within and outside Databricks. Clean Rooms – Uses Delta Sharing and serverless compute to provide a secure and privacy-protecting environment where multiple parties can work together on sensitive enterprise data without direct access.  \nMarketplace – Open forum for exchanging third-party data products.  \nPartner Connect – Allows users to create trial accounts with select Databricks technology partners and connect their Databricks workspace to partner solutions from the Databricks UI.  \n Accelerators  \nLakebridge – A unified tool that automates and simplifies the migration of legacy data warehouses and ETL workloads to Databricks SQL.  \nSolution Accelerators – Pre-built, customizable templates and best practices designed to help organizations quickly solve common industry and data analytics challenges on the Databricks Platform.  \n Data Engineering   \nLakeflow – An end-to-end data engineering solution that empowers data engineers, software developers, SQL developers, analysts and data scientists to deliver high-quality data for downstream analytics, AI and operational applications.  \n● Connect – Simplifies data ingestion with connectors popular for enterprise applications, databases, cloud storage, message buses and local files.  \n● Spark Declarative Pipelines – A declarative framework that lowers complexity of building and managing efficient batch and streaming data pipelines by automatically orchestrating the execution of flows, sinks, streaming tablesand materialized views.  \n● Jobs – Provides reliable orchestration and production monitoring for any data and AI workload (notebooks, pipelines, managed connectors, SQL queries, machine learning training, model deployment and inference).  \n● Designer – An AI-powered, visual no-code ETL builder that lets users create production-grade pipelines with  \ndrag-and-drop and natural language.  \nAuto Loader – Incrementally and efficiently processes new data files as they arrive in cloud storage.  \n Machine Learning   \nMLflow 3.0 – Open source tool that simplifies and manages the complete machine learning lifecycle, including experiment tracking, model management and deployment, on Databricks and beyond.  \nAutoML – Automated machine learning capabilities that rapidly build, select and optimize models without extensive coding. Feature Store – A centralized repository for storing, sharing and governing machine learning features to streamline model development and reuse.  \nMachine Learning Runtime – An optimized, up-to-date runtime environment tailored for scalable machine learning and AI workloads on Databricks.  \n Data Warehousing  Databricks SQL (DBSQL)–Collection of services that bring data warehousing capabilities and performance to users’ existing da","cbCaibtBMd9aXl9X","https://ap.wps.com/l/cbCaibtBMd9aXl9X","pdf",1302711,"English","# Compute\n## Serverless and Classic\n## SQL Warehouse\n# Data Sharing and Collaboration\n## Delta Sharing and Clean Rooms\n## Marketplace and Partner Connect\n# Accelerators\n## Lakebridge and Solution Accelerators\n# Data Engineering\n## Lakeflow and Core Components\n# Machine Learning\n## MLflow 3.0 and AutoML\n# Data Warehousing\n## Databricks SQL and Federation\n# Business Intelligence and Visualization\n## Genie and AI/BI Dashboards\n# AI\n## Model Serving and Vector Search","[{\"question\":\"What is the difference between serverless compute and classic compute in Databricks?\",\"answer\":\"Serverless compute is on-demand and automatically managed, scaling with workload requirements. Classic compute is provisioned and requires users to create, configure, and manage resources.\"},{\"question\":\"How does Delta Sharing support secure collaboration?\",\"answer\":\"Delta Sharing enables secure data sharing within and outside Databricks. Clean Rooms use Delta Sharing and serverless compute to provide a privacy-protecting environment for multiple parties working on sensitive enterprise data.\"},{\"question\":\"Which Databricks services help with machine learning lifecycle and automation?\",\"answer\":\"MLflow 3.0 simplifies and manages the full ML lifecycle, including experiment tracking and deployment. AutoML rapidly builds, selects, and optimizes models without extensive coding.\"}]","Databricks Cheat Sheet - 4.2026 | PDF",1791985497]