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Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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AWS Interview Questions for Data Engineers, Part 2: The boto3 Coding Round
Many AWS data engineering interviews include a short coding round with boto3. The tasks look simple, but each hides a trap: pagination, retries, duplicates, request limits and key ordering. Here are five such tasks, run against a local AWS emulator, with the trap and the answer that shows experience.
AWS Glue Studio: Visual ETL for Beginners
AWS Glue Studio lets you build a Spark ETL job by drawing it: source, transform and target nodes on a canvas, with Glue generating the script. This beginner's guide explains the editor, the built-in transforms, data previews and what they cost, the generated script, and when a visual job should become code.
S3 Lifecycle Policies and Storage Cost Savings
S3 Lifecycle rules move objects to cheaper storage classes and delete them on a schedule. This guide explains transition and expiration actions, builds a three-rule configuration for a data lake bucket with boto3, and sets out the charges that catch people out: small objects, minimum durations, versions and archives.
AWS Glue vs EMR vs Lambda for ETL
Glue, EMR and Lambda can each run ETL code on AWS, and they are built for very different sizes of job. This comparison sets out the hard limits of each, how data volume, control and operations decide the choice, how each is billed, and why most real pipelines end up using two or three of them together.
Building a Serverless Analytics Stack on AWS
A serverless analytics stack on AWS has no cluster to run: S3 for storage, Glue for the catalog and transformation, Athena for SQL, Lambda and Step Functions for triggers and orchestration, and a BI tool on top. This guide builds it layer by layer, lists what each service bills for, and says where it stops fitting.
Building an ETL Pipeline on AWS Step by Step
A step-by-step ETL pipeline on AWS, run end to end: extract raw files from S3, validate 2.96 million real rows with named rules, load cleaned Parquet into a partition, register it in the Glue Data Catalog, write a reconciliation report, and rerun without duplicates. Each step is mapped to its production service.