Tag: serverless spark
Schema Evolution in AWS Glue: Best Practices and Implementation Strategies
Schema evolution, the process of managing changes to the structure of data over time, poses significant challenges in data integration…
Data Discovery in AWS Glue
Data discovery is a crucial first step in any data integration or analytics project. It involves identifying, profiling, and cataloging…
Understanding the Limitations of AWS Glue
AWS Glue is a fully managed extract, transform, and load (ETL) service provided by Amazon Web Services (AWS), designed to…
Data Serialization and Deserialization in PySpark with AWS Glue
Introduction to Data Serialization and Deserialization in PySpark Data serialization and deserialization are essential processes in PySpark, especially when working…
Optimizing data queries with AWS Glue and Amazon Athena
AWS Glue, a serverless data integration service, and Amazon Athena, an interactive query service, together offer a seamless solution for…
Mastering data partitioning in AWS Glue
This article explores how AWS Glue handles data partitioning during processing, supplemented by a real-world example. Understanding data partitioning in…
Ensuring data integrity with AWS Glue: A practical guide to data validation
In the world of big data, ensuring the accuracy and integrity of data during ingestion is paramount. AWS Glue, a…
Navigating job dependencies in AWS glue – Managing ETL workflows
AWS Glue manages dependencies between jobs using triggers. Triggers can start jobs based on the completion status of other jobs,…
AWS Glue : Handling Errors and Retries in AWS Glue
AWS Glue is a fully managed ETL service that simplifies and automates data processing tasks. While AWS Glue is designed…
How does AWS Glue support data migration from legacy systems to cloud
AWS Glue supports data migration from legacy systems to cloud through various features and functionalities. Here are some of the…
