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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
- Case studies: risk modeling, financial reporting, and audit logging
Initiating Workflows with Databricks Notebooks
- Developing and navigating notebooks
- Applying Python and SQL within Databricks
- Collaborating using comments and version control
Integrating and Refining Data
- Importing financial data from CSVs, databases, and APIs
- Leveraging Spark DataFrames for data preparation and cleansing
- Addressing missing values and anomalies
Processing and Summarizing Financial Data
- Computing KPIs and financial ratios
- Applying filters, grouping, and pivoting to datasets
- Manipulating and resampling time series data
Visualizing Financial Insights
- Building dashboards using Databricks' visualization tools
- Tailoring charts for financial reporting needs
- Exporting visuals for presentations or regulatory compliance
Enhancing Query Performance and Utilizing Delta Lake
- Overview of Delta Lake architecture
- Ensuring data reliability through ACID transactions
- Boosting performance via data partitioning
Team Collaboration, Automation, and Data Sharing
- Managing access controls and permissions for finance teams
- Scheduling automated jobs for reporting
- Securely exporting data and results
Conclusion and Future Directions
Requirements
- A foundational understanding of 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 financial sector
- Data engineers who support finance teams
