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Duration 14 hours
Course Outline
Introduction to Databricks and Financial Applications
- Exploring the Databricks ecosystem
- Review of financial data analysis workflows
- Case studies: risk modeling, financial reporting, and audit logging
Initiating Work with Databricks Notebooks
- Creating and navigating through notebooks
- Implementing Python and SQL within Databricks
- Collaborating via comments and version history tracking
Data Ingestion and Cleansing
- Importing financial data from CSV files, databases, and APIs
- Utilizing Spark DataFrames for data preparation and cleaning
- Addressing missing values and identifying outliers
Transformation and Aggregation of Financial Data
- Computing KPIs and financial ratios
- Filtering, grouping, and pivoting data sets
- 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 compliance reviews
Query Optimization and Delta Lake Integration
- Overview of Delta Lake architecture
- Ensuring data reliability through ACID transactions
- Enhancing performance via data partitioning strategies
Collaboration, Scheduling, and Distribution
- Managing access controls and permissions for finance teams
- Setting up job scheduling for automated reporting
- Securing the export of data and results
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental data analysis concepts
- Proficiency in Python or SQL
- Acquaintance with financial data structures and reporting standards
Target Audience
- Financial analysts and business intelligence specialists
- Data analysts operating within the finance industry
- Data engineers providing support to financial teams
