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Duration 14 hours
Course Outline
Foundations of Databricks in Finance
- Navigating the Databricks ecosystem
- Examining workflows for financial data analysis
- Case studies: risk modeling, financial reporting, and audit trails
Working with Databricks Notebooks
- Setting up and managing notebooks
- Integrating Python and SQL within Databricks
- Enhancing collaboration through comments and version control
Data Ingestion and Data Cleaning
- Importing financial data from CSV files, databases, and APIs
- Utilizing Spark DataFrames for data cleansing and preparation
- Managing missing values and identifying outliers
Data Transformation and Aggregation
- Computing KPIs and financial metrics
- Applying filters, grouping, and pivot operations on datasets
- Manipulating and resampling time series data
Visualizing Financial Data
- Building dashboards using Databricks visual tools
- Tailoring charts for financial reporting needs
- Exporting visuals for presentations or compliance reviews
Query Optimization and Delta Lake
- Exploring the Delta Lake architecture
- Ensuring data reliability through ACID transactions
- Boosting performance via data partitioning
Team Collaboration, Automation, and Sharing
- Configuring access controls and permissions for finance teams
- Scheduling jobs for automated reporting
- Securely exporting data and analysis results
Wrap-up and Future Directions
Requirements
- A solid grasp of fundamental 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 financial sector
- Data engineers providing support to financial teams
