Claude for Modern Data Science, GenAI, and Agent AI on Azure + Databricks

Topics Covered 

• Evolution of modern data engineering 
• Core components of an Azure-based data platform 
• Batch, incremental, and analytics-oriented data pipelines 
• Role of Azure Data Factory, Data Lake, Databricks, and Power BI 
• Where Claude fits within the end-to-end engineering lifecycle 
• How Claude supports design, development, debugging, and documentation

What Participants Will Do

• Understand the progressive project scenario 
• Map the end-to-end architecture for the project 
• Identify where manual engineering effort is highest 
• Explore how Claude can reduce friction across the lifecycle 

Hands-On 

• Review a business requirement and translate it into a high-level pipeline design
• Use Claude to convert a problem statement into engineering tasks, stages, and architecture thinking 

Session Outcome 

Participants will understand the complete architecture for the program and how Claude can act as a practical accelerator across the data engineering lifecycle. 

Topics Covered 

• Understanding structured, semi-structured, and API-driven sources 
• Batch ingestion patterns on Azure 
• Introduction to ingestion with Azure Data Factory 
• Schema inference and early-stage source profiling 
• Common ingestion challenges such as missing fields, drift, and inconsistent formats What Can Be Achieved with Claude 
• Faster interpretation of source files and API payloads 
• Assistance in understanding column meaning and source-level anomalies ● Drafting mapping logic for ingestion 
• Identifying ingestion risks early in the design process 
• Accelerating first-pass ingestion logic 

Hands-On 

• Ingest sample source data into the lake 
• Review source structure and ingestion requirements 
• Use Claude to help interpret schema and field mappings 
• Build the first ingestion workflow for the project 

Session Outcome 

Participants will be able to design and implement the ingestion layer more confidently and understand how Claude helps reduce the time spent on source interpretation and ingestion planning.

Topics Covered 

• Role of Azure Data Lake Storage in modern architectures 
• Bronze, Silver, and Gold design principles 
• Raw versus curated storage 
• Delta Lake concepts in Azure Databricks 
• Organizing data for scalability, usability, and downstream processing 
• Table design, layer responsibilities, and schema evolution thinking 

What Can Be Achieved with Claude 

• Better reasoning about which data belongs in which layer 
• Support in designing table structures and naming conventions 
• Assistance in deciding how raw data should evolve into curated structures ● Faster drafting of DDL and storage planning approaches 
• Better documentation of storage strategy 

Hands-On 

• Create Bronze and Silver storage plans for the project 
• Build initial Delta tables 
• Use Claude to refine storage logic, layer responsibilities, and schema planning 

Session Outcome 

Participants will understand how to structure data in a scalable way and how Claude can support storage design decisions and engineering consistency. 

Topics Covered 

• Data transformation patterns in SQL and PySpark 
• Cleansing, standardization, deduplication, joins, and aggregations 
• Building Silver-layer pipelines in Databricks 
• Engineering for readability, maintainability, and correctness
• Common transformation challenges in real-world projects 

What Can Be Achieved with Claude 

• Faster generation of transformation logic from business rules 
• Support in converting plain-language requirements into SQL or PySpark ● Help with code explanation and refinement 
• Better productivity when handling repetitive transformation work 
• Faster iteration during development 

Hands-On 

• Build transformation logic in Databricks for the progressive project 
• Use Claude to draft and refine SQL and PySpark transformations 
• Validate logic against the target business requirement 

Session Outcome 

Participants will be able to use Claude as an engineering support layer while building transformations in Databricks, helping them reduce effort and improve productivity.

Topics Covered 

• Importance of data quality in modern pipelines 
• Common validation dimensions: completeness, uniqueness, consistency, accuracy, and freshness 
• Designing quality checks in engineering workflows 
• Data validation within transformation pipelines 
• Failure patterns and exception handling 

What Can Be Achieved with Claude 

• Assistance in identifying quality risks based on schema and business rules ● Faster creation of validation rules and edge-case checks 
• Better coverage of test scenarios for transformation logic 
• Support in documenting assumptions and expected values
• Improved debugging when validation fails 

Hands-On 

• Add validation checks to transformed datasets 
• Create business-rule-driven quality checks 
• Use Claude to propose test cases, edge conditions, and rule improvements 

Session Outcome 

Participants will understand how Claude can improve the reliability of pipelines by supporting stronger testing and validation practices. 

Topics Covered 

• Orchestration principles in Azure Data Factory 
• Coordinating ingestion, storage, transformation, and validation 
• Pipeline dependencies and task sequencing 
• Observability, troubleshooting, and failure analysis 
• Practical debugging patterns in orchestrated workflows 

What Can Be Achieved with Claude 

• Faster understanding of broken logic or failed pipeline steps 
• Support in interpreting error messages and logs 
• Better reasoning about dependency sequencing and recovery approaches ● Assistance in documenting pipeline flow and control logic 
• Reduced effort in repetitive troubleshooting 

Hands-On 

• Build orchestration for the progressive project 
• Connect ingestion, transformation, and validation steps 
• Use Claude to analyze failures, interpret issues, and suggest corrections

Session Outcome 

Participants will be able to design more coordinated workflows and use Claude effectively during debugging and pipeline troubleshooting. 

Topics Covered 

• Preparing Gold-layer datasets for business use 
• Basics of analytics-ready modeling 
• KPI-oriented dataset design 
• Building reporting-friendly structures 
• Delivering clean outputs for visualization tools such as Power BI 

What Can Be Achieved with Claude 

• Assistance in translating business questions into dataset requirements ● Support in defining dimensions, measures, and reporting logic 
• Better dataset documentation for business stakeholders 
• Improved consistency in naming, metric logic, and semantic clarity 
• Faster preparation of datasets for visualization consumption 

Hands-On 

• Create Gold-layer outputs for the project 
• Prepare reporting-ready curated datasets 
• Use Claude to help define business-friendly field meanings, reporting logic, and KPI interpretation 

Session Outcome 

Participants will understand how Claude can support the transition from engineering outputs to analytics-ready and visualization-ready data delivery.

Topics Covered 

• Performance tuning considerations in Databricks 
• Improving maintainability and readability of pipeline logic 
• Technical documentation and engineering handover 
• Data dictionaries, metadata understanding, and governance support 
• Bringing the full pipeline together end to end 

What Can Be Achieved with Claude 

• Faster review of engineering logic and readability improvements 
• Support in identifying optimization opportunities 
• Better creation of technical documentation and project summaries 
• Assistance in producing data dictionaries and column-level explanations ● Reduced documentation overhead for engineering teams 

Hands-On 

• Review and optimize the end-to-end project 
• Create technical documentation and dataset summaries 
• Use Claude to generate project explanation, transformation summaries, and governance-oriented documentation 

Session Outcome 

Participants will complete the full project, understand where Claude improves engineering maturity, and leave with a clearer model for enterprise adoption.