Analytics Engineering with AWS for Financial Data Processing

Overview

Discover how analytics engineers at Banco Itaú leverage AWS tools to build data pipelines and extract valuable insights from financial data in South America's largest financial institution.

Syllabus

• Introduction to Analytics Engineering
Overview of analytics engineering roles and responsibilities
Importance of data pipelines in financial institutions
Introduction to financial data processing
• AWS Fundamentals
Overview of AWS services and architecture
Setting up an AWS account and environment
Security best practices on AWS
• Data Storage Solutions
Introduction to Amazon S3
Using AWS RDS for relational databases
Implementing Amazon Redshift for data warehousing
• Data Ingestion and Transformation
Using AWS Glue for ETL processes
Streaming data with Amazon Kinesis
Batch data processing with AWS Step Functions and Lambda
• Building Data Pipelines
Designing data pipelines for financial data
Orchestrating workflows with AWS Data Pipeline
Monitoring and logging pipeline activities
• Data Analytics and Insights
Using Amazon Athena for SQL queries on data stored in S3
Data visualization with Amazon QuickSight
Running complex analytics with AWS EMR and Spark
• Security and Compliance in Financial Data Processing
Understanding AWS Identity and Access Management (IAM)
Data encryption and protection on AWS
Ensuring compliance with financial regulations
• Case Study: Banco Itaú
Real-world example of data pipeline architecture at Banco Itaú
Challenges and solutions in handling financial data
Extracting and analyzing valuable insights
• Best Practices and Future Trends
Optimizing performance and cost-efficiency
Staying up-to-date with emerging technologies
Career paths and professional development in analytics engineering
• Final Project
Building a comprehensive data pipeline using AWS tools
Processing and analyzing sample financial data
Presenting project outcomes and insights

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Subjects

Data Science