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Duration 14 hours
Course Outline
Fundamentals of Databricks in Financial Applications
- Exploring the Databricks ecosystem.
- Reviewing financial data analysis processes.
- Case studies: risk modeling, financial reporting, and audit trail analysis.
Initiating Work with Databricks Notebooks
- Building and navigating through notebooks.
- Utilizing Python and SQL within Databricks.
- Collaborating via comments and tracking version history.
Data Acquisition and Sanitization
- Ingesting financial data from CSV files, databases, and APIs.
- Employing Spark DataFrames for data cleansing and preparation.
- Addressing missing values and anomalous data points.
Transforming and Summarizing Financial Data
- Computing KPIs and financial ratios.
- Filtering, grouping, and pivoting datasets for analysis.
- Managing time series data and resampling techniques.
Visualizing Financial Insights
- Constructing dashboards using Databricks visualization tools.
- Tailoring charts for financial reporting purposes.
- Exporting visuals for presentations or regulatory compliance reviews.
Query Optimization and Leveraging Delta Lake
- Overview of Delta Lake architecture.
- Ensuring data reliability through ACID transactions.
- Enhancing performance via data partitioning strategies.
Teamwork, Automation, and Distribution
- Configuring access controls and permissions for finance teams.
- Scheduling automated reporting jobs.
- Safely exporting data and analytical results.
Recap and Future Directions
Requirements
- A solid grasp of fundamental data analysis principles.
- Practical experience with either Python or SQL.
- Knowledge of financial data structures and reporting standards.
Intended Audience
- Financial analysts and business intelligence specialists.
- Data analysts employed within the financial sector.
- Data engineers providing support to financial teams.
