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Course Outline
Introduction to Databricks and Financial Use Cases
- Understanding the Databricks ecosystem.
- Overview of financial data analysis workflows.
- Use case examples: risk modeling, financial reporting, and audit logs.
Getting Started with Databricks Notebooks
- Creating and navigating notebooks.
- Utilizing Python and SQL within Databricks.
- Collaborating through comments and version history.
Data Ingestion and Cleaning
- Importing financial data from CSV files, databases, and APIs.
- Using Spark DataFrames for cleaning and preparation.
- Handling missing values and outliers.
Transforming and Aggregating Financial Data
- Calculating KPIs and financial ratios.
- Filtering, grouping, and pivoting datasets.
- Time series manipulation and resampling.
Visualizing Financial Insights
- Creating dashboards using Databricks visual tools.
- Customizing charts for finance reporting.
- Exporting visuals for presentations or regulatory review.
Optimizing Queries and Using Delta Lake
- Introduction to Delta Lake architecture.
- ACID transactions and data reliability.
- Improving performance with data partitioning.
Collaboration, Scheduling, and Sharing
- Managing access and permissions for finance teams.
- Scheduling jobs for automated reporting.
- Exporting data and results securely.
Summary and Next Steps
Requirements
- Knowledge of data analysis concepts.
- Experience with Python or SQL.
- Familiarity with financial data types and reporting standards.
Audience
- Financial analysts and business intelligence professionals.
- Data analysts operating within the finance sector.
- Data engineers supporting financial teams.
14 Hours
