![]() ![]() It also integrates directly with Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum. The AWS Glue Data Catalog is compatible with Apache Hive Metastore and supports popular tools such as Hive, Presto, Apache Spark, and Apache Pig. AWS Glue crawls your data sources and constructs a data catalog using pre-built classifiers for popular data formats and data types, including CSV, Apache Parquet, JSON, and more.īecause AWS Glue is integrated with Amazon S3, Amazon RDS, Amazon Athena, Amazon Redshift, and Amazon Redshift Spectrum-the core components of a modern data architecture-it works seamlessly to orchestrate the movement and management of your data. AWS Glue featuresĪWS Glue is a fully managed data catalog and ETL (extract, transform, and load) service that simplifies and automates the difficult and time-consuming tasks of data discovery, conversion, and job scheduling. This post walks you through the process of using AWS Glue to crawl your data on Amazon S3 and build a metadata store that can be used with other AWS offerings. AWS Glue automatically crawls your Amazon S3 data, identifies data formats, and then suggests schemas for use with other AWS analytic services. AWS Glue significantly reduces the time and effort that it takes to derive business insights quickly from an Amazon S3 data lake by discovering the structure and form of your data. For more information about building data lakes on AWS, see What is a Data Lake?īecause one of the main challenges of using a data lake is finding the data and understanding the schema and data format, Amazon recently introduced AWS Glue. Although Amazon S3 provides the foundation of a data lake, you can add other services to tailor the data lake to your business needs. For example, Amazon S3 is a highly durable, cost-effective object start that supports Open Data Formats while decoupling storage from compute, and it works with all the AWS analytic services. Many organizations understand the benefits of using Amazon S3 as their data lake. Because data can be stored as-is, there is no need to convert it to a predefined schema. A data lake allows organizations to store all their data-structured and unstructured-in one centralized repository. A data lake is an increasingly popular way to store and analyze data that addresses the challenges of dealing with massive volumes of heterogeneous data. ![]()
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