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AWS CLI & Boto3 Command Reference

A production-grade reference repository for Data Engineers, Cloud Engineers, Analytics Engineers, and DevOps Engineers working with AWS.

This repository covers AWS services commonly used in data engineering workflows through the AWS CLI v2 and Python SDK (Boto3) — with real-world examples, security guidance, troubleshooting, and best practices.


Quick Start

# Verify AWS CLI v2
aws --version

# Configure credentials (or use SSO / IAM roles)
aws configure

# Verify identity
aws sts get-caller-identity
# Verify Boto3
import boto3
print(boto3.__version__)

session = boto3.Session()
print(session.client("sts").get_caller_identity())

Repository Structure

Service Description Path
S3 Object storage, data lakes, ETL staging s3/
IAM Identity, access, roles, policies iam/
EC2 Compute instances for pipelines ec2/
VPC Networking, subnets, endpoints vpc/
RDS Relational databases rds/
DynamoDB NoSQL key-value store dynamodb/
Lambda Serverless compute lambda/
Glue ETL, crawlers, Data Catalog glue/
Athena Serverless SQL on S3 athena/
Redshift Data warehouse redshift/
CloudWatch Monitoring, logs, alarms cloudwatch/
STS Temporary credentials, assume role sts/
Secrets Manager Secret rotation and retrieval secrets_manager/
KMS Encryption keys kms/
ECR Container registry ecr/
ECS Container orchestration ecs/
EKS Managed Kubernetes eks/
EMR Big data processing (Spark, Hive) emr/
SNS Pub/sub notifications sns/
SQS Message queues sqs/
EventBridge Event bus, scheduling eventbridge/
Step Functions Workflow orchestration step_functions/
Lake Formation Data lake governance lake_formation/
AWS Backup Centralized backup aws_backup/
Organizations Multi-account management organizations/
Config Resource compliance config/
CloudFormation Infrastructure as code cloudformation/
Boto3 SDK patterns and utilities boto3/
Terraform CLI + Terraform integration terraform_integration/
Troubleshooting Common errors and fixes troubleshooting/

Documentation Format

Every service file includes:

  1. Service Overview — what it does, use cases, when to use it
  2. AWS CLI Commands — purpose, syntax, parameters, examples, sample output
  3. Advanced Commands — JMESPath, pagination, filtering, batch ops
  4. Boto3 Examples — basic, production-ready, error handling, logging
  5. Security Considerations — least privilege, encryption, credentials
  6. Troubleshooting — common errors and resolutions
  7. Best Practices — cost, security, performance, production tips

Data Engineering Use Cases

Pattern Services
Data lake ingestion S3, Glue, Lake Formation
Batch ETL Glue, EMR, Step Functions
Streaming pipelines Kinesis, Lambda, SQS, EventBridge
Ad-hoc analytics Athena, Redshift
Orchestration Step Functions, EventBridge
Secrets & encryption Secrets Manager, KMS, IAM
Monitoring pipelines CloudWatch, SNS

Prerequisites

  • AWS CLI v2 installed (installation guide)
  • Python 3.9+ with boto3 and botocore
  • Valid AWS credentials (IAM user, SSO, or assumed role)
  • Appropriate IAM permissions for the operations documented

Contributing

When adding new commands or examples:

  • Use AWS CLI v2 syntax
  • Include real-world data engineering context
  • Add sample output where helpful
  • Document required IAM permissions
  • Follow the existing file structure and section headings

License

See LICENSE.

About

A hands-on AWS Data Engineering Lab covering AWS CLI, Boto3, data engineering workflows, cloud infrastructure, security, automation, monitoring, analytics, and real-world AWS service implementations.

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