AWS - Glue

Algunas características de Glue:

Servicio totalmente administrado para implementar ETL (Extract, Transform, Load).
Provee un contexto para trabajar basados en Python, Spark y Scala.
Se encarga de crear catálogos de metadatos para que otros servicios puedan consultar la información.
Utiliza unidades de procesamiento llamadas DPU equivalente a 4 vCPU y 16GB RAM.
Los Crawler van a escanear e identificar la información para ponerla en el catálogo.
Los Classifier van a clasificar la data para ponerla en el catálogo.

La nueva funcionalidad de Glue para medir calidad de datos “Data Quality” es una buena opcion cuando no se puede pagar herramientas costosas.

Glue lo vamos a usar cuando queremos hacer transformación de la información. Glue organiza la información cruda en tablas.

Dentro de este módulo vamos a ver las herramientas que proporciona la nube de AWS para poder transformar nuestra data.

Hola, es posible dejar disponible un mismo Glue Catalog para distintas cuentas de AWS ?

AWS Glue is a fully managed ETL (extract, transform, and load) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores. AWS Glue consists of a central data repository known as the AWS Glue Data Catalog, an ETL engine that automatically generates Python code, and a flexible scheduler that handles dependency resolution, job monitoring, and retries. AWS Glue is serverless, so there’s no infrastructure to set up or manage. Use the AWS Glue console to discover your data, transform it, and make it available for search and querying. You can also use the AWS Glue API operations to interface with AWS Glue

AWS Glue is designed to work with semi-structured data. It introduces a component called a dynamic frame, which you can use in your ETL scripts. A dynamic frame is similar to an Apache Spark dataframe, which is a data abstraction used to organize data into rows and columns, except that each record is self-describing so no schema is required initially. With dynamic frames, you get schema flexibility and a set of advanced transformations specifically designed for dynamic frames. You can convert between dynamic frames and Spark dataframes, so that you can take advantage of both AWS Glue and Spark transformations to do the kinds of analysis that you want.

Cuando usar Glue?
AWS Glue simplifies many tasks when you are building a data warehouse or data lake:

Discovers and catalogs metadata about your data stores into a central catalog. You can process semi-structured data, such as clickstream or process logs.

Populates the AWS Glue Data Catalog with table definitions from scheduled crawler programs. Crawlers call classifier logic to infer the schema, format, and data types of your data. This metadata is stored as tables in the AWS Glue Data Catalog and used in the authoring process of your ETL jobs.

Generates ETL scripts to transform, flatten, and enrich your data from source to target.

Detects schema changes and adapts based on your preferences.

Triggers your ETL jobs based on a schedule or event. You can initiate jobs automatically to move your data into your data warehouse or data lake. Triggers can be used to create a dependency flow between jobs.

Gathers runtime metrics to monitor the activities of your data warehouse or data lake.

Handles errors and retries automatically.

Scales resources, as needed, to run your jobs.

Un tool más dentro del abanico de opciones de servicios administrados que tiene AWS.! Excelente.

Servicios administrados de AWS.