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Duration 14 hours
Course Outline
Introduction to Databricks and Applications in Finance
- Exploring the Databricks ecosystem
- Reviewing workflows for financial data analysis
- Illustrative use cases: risk modeling, financial reporting, and audit trails
Getting Started with Databricks Notebooks
- Creating and navigating through notebooks
- Utilizing Python and SQL within Databricks
- Collaborating via comments and version control history
Data Ingestion and Cleansing
- Importing financial data from CSV files, databases, and APIs
- Leveraging Spark DataFrames for data cleaning and preparation
- Addressing missing values and outliers
Transformation and Aggregation of Financial Data
- Computing KPIs and financial ratios
- Applying filters, grouping, and pivoting datasets
- Manipulating and resampling time-series data
Visualizing Financial Insights
- Building dashboards using Databricks' visualization tools
- Customizing charts for financial reporting purposes
- Exporting visuals for presentations or regulatory compliance reviews
Query Optimization and Delta Lake Usage
- Overview of Delta Lake architecture
- Implementing ACID transactions for data integrity
- Enhancing performance through data partitioning
Collaboration, Scheduling, and Data Sharing
- Managing access rights and permissions for finance teams
- Setting up scheduled jobs for automated reporting
- Securely exporting data and results
Summary and Future Steps
Requirements
- A solid grasp of fundamental data analysis principles
- Practical experience with 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 industry
- Data engineers providing support to financial teams
