Explore identity and access management concepts, create user groups and policies, and implement encryption with CMS keys, plus set up server-side and client-side encryption and an SNS topic for notifications.

Explore identity and access management in AWS, detailing authentication, authorization, and identities (users, groups, roles). Learn how policies define allowed actions on resources like glue jobs.

Explore the key management service by enabling encryption at rest and in transit, compare client-side and server-side encryption, and create a symmetric customer-managed key with a resource policy.

Configure the AWS CLI using IAM user credentials, generate and manage access keys, and verify the connection before exploring commands for IAM, cloud formation, and S3 services.

Explore how cloud formation uses templates as infrastructure as code to create, update, and delete AWS resources with JSON templates, enabling consistent, parameterized deployments across environments via stacks.

Create your first glue database and explore the glue data catalog through hands-on labs. Schedule and orchestrate glue jobs with triggers and workflows to visualize the end-to-end pipeline.

Explore the aws glue data catalog essentials, including databases, tables, partitions, and connections, and learn how metadata supports etl jobs and redshift workflows.

Learn how AWS Glue crawler accesses data sources, extracts metadata, creates and updates table definitions in the cloud catalog, and manages schemas and partitions with classifiers as needed.

Explore how AWS Glue crawler classifiers infer schemas and create tables in the Glue catalog, and build custom classifiers to handle csv, json, xml with grok patterns.

Drive a second AWS Glue crawler lab to create and update a table in the AWS Glue catalog, expanding from three to six columns as json data uploads occur.

Create an AWS Glue crawler to ingest historical data while excluding current data, generate historical and historical year tables with partitions by extracted date and year, enabling partition-aware queries.

Create and configure AWS Glue jobs for ETL with script location, artifact buckets, cloud formation, and optional libraries; choose Python shell or Spark jobs, set bookmarks, parameters, and autoscale.

Upload cloud formation templates to S3 using the AWS CLI, syncing local templates to the target bucket and organizing them in a templates folder for future Glue jobs.

Deploy your first AWS Glue blue job by preparing source and target, using CloudFormation templates, debugging deployments, and validating results via job output and CloudWatch logs.

Align for deployment, deploy the AWS Glue job, fix the script despite failures, and ensure the AWS Glue job workflow runs the job and crawler to create data catalog table.

Design a Glue streaming loader job to read from a Kinesis stream and write to an S3 bucket, using a visual canvas and a shared script for subsequent jobs.

Launch a spark streaming Glue transformation to read streaming data, update country names from codes, and write CSV output to the transformation output location with a kinesis checkpoint.

Explore data quality fundamentals, configure a data quality rule set on the existing blue catalog table, run the Glue job, evaluate outcomes, and enable CloudWatch alerts for failures.