Learn AWS Data Engineering

Explore S3, a serverless object storage service, with durability and automatic scaling, plus features like S3 Select for data preview and SQL querying, lifecycle tiers, and event notifications.

Learn how Amazon Athena, a serverless service, enables querying data from S3 using SQL, supporting CSV, JSON, and Parquet, with charges based on the data retrieved.

Discover AWS Glue, an ETL service that discovers data, catalogs schemas with Glue Catalog, and runs Python or Spark transforms to load into Redshift or S3, with Athena and QuickSight.

Explore aws lambda, a serverless compute service that runs code without provisioning servers, scales automatically, supports multiple languages including Java, and limits execution to 15 minutes.

Explore aws data engineering with rds and aurora, a fully managed, scalable relational database in vpc, plus redshift for data warehousing and s3 querying with spectrum.

Explore Amazon SQS standard and FIFO queues for sending, storing, and delivering messages with visibility timeouts, dead-letter queues, and per-message delays to support scalable microservices and serverless architectures.

Discover how Amazon SNS enables publish-subscribe messaging from a sender to multiple subscribers, including email and SMS, with decoupled architecture and message retention to prevent data loss.

Explore Amazon Kinesis, a real-time data ingestion service for IoT, logs, and click streams, with analytics and loading to S3, Redshift, or Elasticsearch via Firehose.

Explore serverless data warehousing on AWS by loading CSV into S3, transforming to Parquet with Glue, and querying via Athena, using crawlers, databases, and tables.

Ingest data from a rest api by fetching weather data with Python, save responses to an S3 bucket, transform to parquet with Glue, and validate results in Athena.

Load data from S3 into a Postgres database using AWS Glue crawlers and a transfer job. Map columns, create a transformed sales data table, and validate the loaded data.

Explains incremental data loading by comparing a csb file with the database to extract new records, save them to a 063 file in s3, and load them into the table.

Ingest data from an sftp server by downloading a file with a Python script using the paramedical library and saving it to an S3 bucket.