In today’s data-driven world, effective data management is crucial for organizations aiming to make well-informed, data-driven decisions. As the importance of data continues to grow, so does the significance of robust data management practices. This includes the processes of ingesting, storing, organizing, and maintaining the data generated and collected by an organization. Within the realm of data management, schema evolution stands out as one of the most critical aspects. Businesses evolve over time, leading to changes in data and, consequently, changes in corresponding schemas. Even though a schema may be initially defined for your data, evolving business requirements inevitably demand schema modifications. Yet, modifying data structures is no straightforward task, especially when dealing with distributed systems and teams. It’s essential that downstream consumers of the data can seamlessly adapt to new schemas. Coordinating these changes becomes a critical challenge to minimize downtime and prevent production issues. Neglecting robust data management and schema evolution strategies can result in service disruptions, breaking data pipelines, and incurring significant future costs. In the context of Apache Kafka, schema evolution is managed through a schema registry. As producers share data with consumers via Kafka, the schema is stored in this registry. The Schema Registry enhances the reliability, flexibility, and scalability of systems and applications by providing a standardized approach to manage and validate schemas used by both producers and consumers. This blog post will walk you through the steps of utilizing Amazon MSK in combination with AWS Glue Schema Registry and Terraform to build a cross-account streaming pipeline for Kafka, complete with built-in schema evolution. This approach provides a comprehensive solution to address your dynamic and evolving data requirements.
Amazon Ion is one of the data serialization formats you can use when exporting data from DynamoDB to S3. Recently, I tried to select data from one of these exports with Athena after using a Glue Crawler to create the schema and table. It didn’t work
Working with CSV files and Big Data tools such as AWS Glue and Athena can lead to interesting challenges. In this blog I will explain to you how to solve a particular problem that I encountered in a project - the HIVE_PARTITION_SCHEMA_MISMATCH.
This is the story of how we accidentally learned more about the internals of Glue Crawlers than we ever wanted to know. Once upon a time (a few days ago), André and I were debugging a crawler that didn’t do what it was supposed to. Before we dive in
There are many components under the Glue umbrella that can fit together into a cohesive big picture. In this introduction to Glue I’m explaining my version of this big picture.
When discussing high performant real-time event streaming, Apache Kafka is a tool that immediately comes to mind. Optimized for ingesting and transforming real-time streaming data in a reliable and scalable manner, a great number of companies today rely o