AWS Data Engineer RoadMap

Описание: 1. Understand the Role of an AWS Data Engineer
Key Responsibilities:
Build and maintain data pipelines.
Manage and optimize AWS data services.
Implement ETL/ELT processes.
Ensure data security and governance.
Domains to Master:
Data modeling and architecture.
Cloud-native tools on AWS.
2. Develop Foundational Skills
Programming:
Learn Python and SQL (essential for data manipulation and ETL jobs).
Understand Shell scripting for automation.
Databases:
Relational: MySQL, PostgreSQL, Amazon RDS.
NoSQL: DynamoDB, MongoDB.
3. AWS Fundamentals
Core AWS Services:
Compute: EC2, Lambda.
Storage: S3, EBS, Glacier.
Networking: VPC, Route 53.
AWS IAM: Security, roles, and permissions.
4. AWS Data Services
Master these AWS services:
Data Storage:
S3 (data lake), Glacier (archival).
RDS, DynamoDB, and Redshift.
Data Processing:
AWS Glue (ETL/ELT jobs).
EMR (big data processing).
AWS Lambda (serverless data processing).
Data Streaming:
Kinesis (real-time data streams).
MSK (Managed Kafka).
Data Integration:
AWS Data Pipeline.
Step Functions (workflow orchestration).
Data Analytics:
Amazon Athena (query S3 data).
Quicksight (data visualization).
5. Big Data Frameworks
Learn frameworks commonly used with AWS:
Apache Spark (via EMR or Glue).
Apache Hadoop.
Apache Kafka (stream processing).
6. Data Modeling & Warehousing
Learn about data modeling techniques:
Star Schema and Snowflake Schema.
Normalization and Denormalization.
AWS Redshift:
Understand Redshift architecture and optimization techniques.
Learn about materialized views, sort keys, and distribution keys.
7. Data Pipeline Development
ETL/ELT Tools:
AWS Glue, Apache Airflow (MWAA on AWS).
AWS Step Functions for orchestration.
Pipeline Optimization:
Optimize for performance and cost.
Use partitioning, compression, and caching.
8. Data Security & Governance
Security Practices:
Encryption (KMS).
Data masking.
VPC and IAM policies.
Governance Tools:
AWS Lake Formation.
AWS Glue Data Catalog.
9. Monitoring and Optimization
Learn monitoring and troubleshooting tools:
AWS CloudWatch (logs and metrics).
AWS CloudTrail (auditing).
AWS Cost Explorer for budgeting.
10. Data Visualization
Gain skills in data visualization:
AWS Quicksight for building dashboards.
Integration with Tableau or Power BI for advanced visualizations.
11. CI/CD for Data Pipelines
Learn automation with DevOps tools:
AWS CodePipeline.
Terraform for infrastructure as code.
Git for version control.
12. Prepare for AWS Certifications
Start with certifications to validate your skills:
AWS Certified Cloud Practitioner (foundational).
AWS Certified Solutions Architect – Associate.
AWS Certified Data Analytics – Specialty.
AWS Certified Big Data – Specialty (deprecated but still a valuable study guide).
13. Hands-On Projects
Build a Data Lake on S3 and query it using Athena.
Create an ETL Pipeline using AWS Glue and Lambda.
Implement Real-Time Data Processing with Kinesis.