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Duration 14 hours
Course Outline
Overview of Databricks and Its Application in Finance
- Exploring the Databricks ecosystem
- Review of financial data analysis workflows
- Case studies: risk modeling, financial reporting, and audit logs
Initiating Workflows with Databricks Notebooks
- Creation and navigation of notebooks
- Integration of Python and SQL within Databricks
- Collaboration through comments and version control
Data Ingestion and Data Cleansing
- Importing financial data from CSV files, databases, and APIs
- Utilizing Spark DataFrames for data preparation and cleaning
- Addressing missing values and outliers
Transforming and Aggregating Financial Metrics
- Computation of KPIs and financial ratios
- Application of filtering, grouping, and pivoting techniques
- Manipulation and resampling of time series data
Visualization of Financial Insights
- Building dashboards using Databricks visual tools
- Tailoring charts for financial reporting needs
- Exporting visuals for presentations or regulatory compliance reviews
Query Optimization and Delta Lake Integration
- Foundations of Delta Lake architecture
- ACID transactions ensuring data reliability
- Enhancing performance through data partitioning
Collaboration, Automation, and Secure Sharing
- Managing access controls and permissions for finance teams
- Scheduling automated jobs for reporting workflows
- Secure export of data and analysis results
Recap and Future Directions
Requirements
- Foundational understanding of data analysis principles
- Proficiency in Python or SQL
- Knowledge of financial data structures and reporting standards
Target Audience
- Financial analysts and Business Intelligence specialists
- Data analysts operating within the finance sector
- Data engineers providing support to financial teams
