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Duration 14 hours
Course Outline
Introduction to Databricks and Applications in Finance
- Exploring the Databricks ecosystem
- Review of financial data analysis workflows
- Real-world examples: risk modeling, financial reporting, and audit logs
Initiating Work with Databricks Notebooks
- Setting up and navigating notebook interfaces
- Implementing Python and SQL within Databricks
- Collaborating via comments and tracking version history
Data Ingestion and Cleansing
- Importing financial data from CSV files, databases, and APIs
- Utilizing Spark DataFrames for data preparation and cleaning
- Managing missing values and outliers
Transforming and Aggregating Financial Datasets
- Computing KPIs and key financial ratios
- Applying filters, grouping, and pivoting techniques
- Manipulating and resampling time series data
Visualizing Financial Insights
- Building dashboards using Databricks visual tools
- Tailoring charts for financial reporting needs
- Exporting visuals for presentations or regulatory reviews
Query Optimization and Delta Lake Usage
- Understanding Delta Lake architecture
- ACID transactions ensuring data reliability
- Enhancing performance through data partitioning
Collaboration, Scheduling, and Distribution
- Managing access controls and permissions for finance teams
- Scheduling automated jobs for reporting
- Securely exporting data and results
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental data analysis principles
- Practical experience with Python or SQL
- Knowledge of financial data categories and reporting standards
Target Audience
- Financial analysts and business intelligence experts
- Data analysts operating within the financial sector
- Data engineers providing support to finance teams
