Category: AWS Glue

In today’s data-driven world, the ability to seamlessly integrate and utilize diverse data sources is critical for gaining actionable insights and driving innovation. As organizations increasingly rely on data stored across various platforms, such as Snowflake, Amazon Simple Storage Service (Amazon S3), and various software as a service (SaaS) applications, the challenge of bringing these […]

In this post, we show how to capture the data quality metrics for data assets produced in Amazon Redshift. With Amazon DataZone, the data owner can directly import the technical metadata of a Redshift database table and views to the Amazon DataZone project’s inventory. As these data assets gets imported into Amazon DataZone, it bypasses the AWS Glue Data Catalog, creating a gap in data quality integration. This post proposes a solution to enrich the Amazon Redshift data asset with data quality scores and KPI metrics.

In this blog post, we explore how to use the SFTP Connector for AWS Glue from the AWS Marketplace to efficiently process data from Secure File Transfer Protocol (SFTP) servers into Amazon Simple Storage Service (Amazon S3), further empowering your data analytics and insights.

Glue Data Catalog views is a new feature of the AWS Glue Data Catalog that customers can use to create a common view schema and single metadata container that can hold view-definitions in different dialects that can be used across engines such as Amazon Redshift and Amazon Athena. In this blog post, we will show how you can define and query a Data Catalog view on top of open source table formats such as Iceberg across Athena and Amazon Redshift. We will also show you the configurations needed to restrict access to the underlying database and tables. To follow along, we have provided an AWS CloudFormation template.

We are excited to announce the general availability of anomaly detection capabilities in AWS Glue Data Quality. In this post, we demonstrate how this feature works with an example. We provide an AWS Cloud Formation template to deploy this setup and experiment with this feature.

In today’s landscape, data streams continuously from countless sources such as social media interactions to Internet of Things (IoT) device readings. This torrent of real-time information presents both a challenge and an opportunity for businesses. To harness the power of this data effectively, organizations need robust systems for ingesting, processing, and analyzing streaming data at […]

In this post, we cover how to enable trusted identity propagation with AWS IAM Identity Center, Amazon Redshift, and AWS Lake Formation residing on separate AWS accounts and set up cross-account sharing of an S3 data lake for enterprise identities using AWS Lake Formation to enable analytics using Amazon Redshift. Then we use Amazon QuickSight to build insights using Redshift tables as our data source.

As builders, sometimes you want to dissect a customer experience, find problems, and figure out ways to make it better. That means going a layer down to mix and match primitives together to get more comprehensive features and more customization, flexibility, and freedom. In this post, we introduce Log Lake, a do-it-yourself data lake based on logs from CloudWatch and AWS CloudTrail.

The post illustrates the construction of a comprehensive CDC system, enabling the processing of CDC data sourced from Amazon Relational Database Service (Amazon RDS) for MySQL. Initially, we’re creating a raw data lake of all modified records in the database in near real time using Amazon MSK and writing to Amazon S3 as raw data. Later, we use an AWS Glue exchange, transform, and load (ETL) job for batch processing of CDC data from the S3 raw data lake.

In the era of big data, data lakes have emerged as a cornerstone for storing vast amounts of raw data in its native format. They support structured, semi-structured, and unstructured data, offering a flexible and scalable environment for data ingestion from multiple sources. Data lakes provide a unified repository for organizations to store and use […]