- Platform Tutorials
- Introduction to Platform
- A customer experience powered by Experience Platform
- Behind the scenes: A customer experience powered by Experience Platform
- Experience Platform overview
- Key capabilities
- Application and Intelligent Services
- Integrations with Experience Cloud applications
- Key use cases
- Basic architecture
- User interface
- Roles and project phases
- Introduction to Real-Time CDP
- Getting started: Data Architects and Data Engineers
- Authenticate to Experience Platform APIs
- Import sample data to Experience Platform
- Administration
- Audiences and Segmentation
- Introduction to Audience Portal and Composition
- Upload audiences
- Audience rule builder overview
- Create audiences
- Create content-based audiences
- Create conversion audiences
- Create audiences from existing audiences
- Create sequential audiences
- Create dynamic audiences
- Create multi-entity audiences
- Create audiences with B2B data
- Demo of streaming segmentation
- Evaluate an audience rule
- Create a dataset to export data
- Segment Match connection setup
- Segment Match data governance
- Segment Match configuration flow
- Segment Match pre-share insights
- Segment Match receiving data
- Audit logs
- Data Collection
- Dashboards
- Data Governance
- Data Hygiene
- Data Ingestion
- Overview
- Batch ingestion overview
- Create and populate a dataset
- Map a CSV file to XDM
- Sources overview
- Ingest data from Adobe Analytics
- Ingest data from Audience Manager
- Ingest data from cloud storage
- Ingest data from CRM
- Ingest data from databases
- Streaming ingestion overview
- Stream data with HTTP API
- Stream data using Source Connectors
- Web SDK tutorials
- Mobile SDK tutorials
- Data Science Workspace
- Overview
- Architecture
- Load data in JupyterLab notebooks
- Query and discover data in JupyterLab notebooks
- Exploratory Data Analysis
- Recipes, models, and services overview
- Build a model using the recipe builder template
- Analyze model performance
- Create and publish a trained model (UI)
- Schedule automated training and scoring for a service
- Enrich Real-Time Customer Profiles with machine learning insights
- Package source files into a recipe
- Import a packaged recipe (UI)
- Import a packaged recipe (API)
- Destinations
- Destinations overview
- Connecting to destinations
- Create destinations and activate data
- Activate profiles and segments to a destination
- Integrate with Google Customer Match
- Configure the Azure Blob destination
- Configure the Marketo destination
- Configure file-based cloud storage or email marketing destinations
- Configure a social destination
- Adobe Target and Custom Personalization
- Activate data to non-Adobe applications webinar
- Identities
- Intelligent Services
- Monitoring
- Partner data support
- Profiles
- Privacy and Security
- Introduction to Privacy Service
- Identity data in Privacy requests
- Privacy JavaScript library
- Privacy labels in Adobe Analytics
- Getting started with the Privacy Service API
- Privacy Service UI
- Privacy Service API
- Subscribe to Privacy Events
- Set up customer-managed keys
- 10 considerations for Responsible Customer Data Management
- Elevating the Marketer’s Role as a Data Steward
- Queries
- Overview
- Query Service UI
- Query Service API
- Explore Data
- Prepare Data
- Adobe Defined Functions
- Data usage patterns
- Run queries
- Generate datasets from query results
- Tableau
- Analyze and visualize data
- Build dashboards using BI tools
- Recharge your customer data
- Connect clients to Query Service
- Validate data in the datalake
- Schemas
- Overview
- Building blocks
- Plan your data model
- Convert your data model to XDM
- Create schemas
- Create schemas for B2B data
- Create classes
- Create field groups
- Create data types
- Configure relationships between schemas
- Use enumerated fields and suggested values
- Copy schemas between sandboxes
- Update schemas
- Create an ad hoc schema
- Sources
- Experience Cloud Integrations
- Industry Trends
Query and discover data in Data Science Workspace
Adobe Experience Platform allows you to use Structured Query Language (SQL) in Data Science Workspace by integrating Query Service into JupyterLab as a standard feature. This tutorial demonstrates some common use cases to explore, transform, and analyze data.
For more information, please visit the Query Service in Data Science Workspace documentation.
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