Amazon Web Services (AWS) is the foundation of many cloud-based data engineering platforms. Services like Amazon S3, AWS Glue, AWS EMR, and Amazon Redshift enable data teams to build scalable data lakes, process massive datasets, and run high-performance analytical queries. Master cloud-native architectures, serverless ETL pipelines, and cost-optimization strategies on AWS. Explore our collection of 6 in-depth articles about AWS on DataEngineer Hub. Each tutorial provides practical, hands-on guidance with real-world examples to help you master AWS concepts and best practices.
For years, the pattern was: Airflow sits in one corner of your infrastructure, dbt runs on a server somewhere else, they pass data between each other via manual credential handoffs…
TL;DR→ Delta Lake is easier to start with, especially if you’re already on Databricks→ Iceberg wins on engine flexibility — works natively with Spark, Flink, Trino, Snowflake, and more without…
I passed the SnowPro Gen AI certification not too long ago. Within the same week I was back at my desk staring at a broken pipeline that no multiple-choice question…
Three practical methods to query Snowflake data in DuckDB — via Iceberg tables, ADBC, or a hybrid architecture — with real cost breakdowns showing 70–90% savings on BI and dev workloads.
Building a powerful data pipeline on AWS is one thing. Building one that doesn’t burn a hole in your company’s budget is another. As data volumes grow, the costs associated…
For data engineers, the dream is to build pipelines that are robust, scalable, and cost-effective. For years, this meant managing complex clusters and servers. But with the power of the cloud,…