You can author, run, and monitor AWS Glue ETL jobs. You can choose either Amazon SageMaker notebook or Apache Zeppelin notebook as an environment.ĪWS Glue Studio is a new visual interface for AWS Glue that supports extract-transform-and-load (ETL) developers. With Glue DataBrew, you can visualize, clean, and normalize data directly from your data lake, data warehouses, and databases.Ĭurate data: You can use either Glue development endpoint or AWS Glue Studio to curate your data.ĪWS Glue development endpoint is an environment that you can use to develop and test your AWS Glue scripts.
You can use a visual interface, reducing the time it takes to prepare data by up to 80%.
Glue DataBrew: AWS Glue DataBrew allows data scientists and data analysts to clean and normalize data. Data quality – AWS Glue helps you author and monitor data quality rules With AWS Glue Schema Registry, you can manage and enforce schemas on your data streaming applications. Glue Schema Registry: The AWS Glue Schema Registry allows you to centrally discover, control, and evolve data stream schemas. Glue Data Catalog: The Data Catalog serves as the central metadata catalog for the entire data landscape. Catalog – AWS Glue simplifies data discovery and governance Extract, transform, and load (ETL) jobs that you define in AWS Glue use these Data Catalog tables as sources and targets. Upon completion, the crawler creates or updates one or more tables in your Data Catalog. A crawler can crawl multiple data stores in a single pass. Glue crawlers: You can use a crawler to populate the AWS Glue Data Catalog with tables.
You can also subscribe to several connectors offered in the AWS Marketplace. For data stores that are not natively supported such as SaaS applications, you can use connectors.
#Aws data lakehouse drivers#
AWS Glue also allows you to use custom JDBC drivers in your extract, transform, and load (ETL) jobs. You can use Amazon Redshift, Amazon RDS, Amazon Aurora, Microsoft SQL Server, MySQL, MongoDB, or PostgreSQL using JDBC connections. Glue connector: AWS Glue provides built-in support for the most commonly used data stores. AWS Glue integration components Connect – AWS Glue allows you to connect to various data sources anywhere The following diagram illustrates the various components of the AWS Glue integration system.įigure 1. So you can start analyzing your data and putting it to use in minutes, rather than months. AWS Glue provides all of the capabilities needed for data integration. It can be used for analytics, machine learning, and application development. Components of the AWS Glue integration systemĪWS Glue is a serverless data integration service that facilitates the discovery, preparation, and combination of data.
#Aws data lakehouse how to#
We will also discuss how to derive persona-centric insights from your Lake House using AWS Glue. In this blog post, we illustrate the AWS Glue integration components that you can use to accelerate building a Lake House architecture on AWS.
To overcome these issues and easily move data around, a Lake House approach on AWS was introduced. These issues are preventing customers from getting deeper insights. Often these disjointed efforts to build separate data stores end up creating data silos, data integration complexities, excessive data movement, and data consistency issues. Customers are building databases, data warehouses, and data lake solutions in isolation from each other, each having its own separate data ingestion, storage, management, and governance layers.
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