Start here in AWS
The most useful guides in this topic, not just the most recent.
AWS Guides & Tutorials
AWS for data engineers — S3, EC2, Lambda, Glue, Redshift, Athena, EMR and the certification roadmap that gets you hired.
AWS articles
AWS Step Functions: Orchestrating Serverless Workflows
AWS Step Functions coordinates other services into a workflow with retries, branching and a visible history. This guide explains state machines and state types, the three integration patterns, error handling, Standard versus Express workflows, billing, and a nightly data pipeline written in Amazon States Language.
S3 vs EBS vs EFS: AWS Storage Compared
S3, EBS and EFS are three different kinds of storage: objects reached over an API, a disk attached to one server, and a file system shared by many. This comparison explains how each is accessed, what it can and cannot do, how it scales and is billed, and which suits a data lake, a database, or shared working files.
Athena Federated Queries Explained
Athena Federated Query lets one SQL statement read from sources outside S3, such as DynamoDB, MySQL or Redshift, and join them with data lake tables. This guide explains connectors, predicate pushdown, passthrough queries, the documented limits, how billing works, and when copying the data is the better design.
Amazon Kinesis Explained: Real-Time Streaming
Amazon Kinesis is AWS's family of streaming services. This guide explains Kinesis Data Streams, shards, partition keys, sequence numbers and retention, shows on 2,000 real taxi events how the choice of partition key spreads or skews load across shards, and covers capacity modes, consumer types and where Firehose fits.
AWS Glue Data Catalog and Crawlers Explained
The Glue Data Catalog is the index of an AWS data lake, and crawlers are the usual way to fill it. This guide explains what the catalog stores, how a crawler classifies files and decides between tables and partitions, what three real files with drifting schemas show, and the settings that keep crawlers predictable.
Amazon EMR Explained: Big Data on AWS
Amazon EMR runs Spark, Hadoop, Hive and other big data frameworks on AWS. This guide explains primary, core and task nodes, why data belongs in S3 and not in the cluster's HDFS, how work is submitted as steps, transient versus long-running clusters, the Serverless and EKS options, and what decides the bill.