Category: AWS Glue DataBrew

Businesses collect more and more data every day to drive processes like decision-making, reporting, and machine learning (ML). Before cleaning and transforming your data, you need to determine whether it’s fit for use. Incorrect, missing, or malformed data can have large impacts on downstream analytics and ML processes. Performing data quality checks helps identify issues […]

Before you can create visuals and dashboards that convey useful information, you need to transform and prepare the underlying data. With AWS Glue DataBrew, you can now easily transform and prepare datasets from Amazon Simple Storage Service (Amazon S3), an Amazon Redshift data warehouse, Amazon Aurora, and other Amazon Relational Database Service (Amazon RDS) databases […]

Data analytics remains a constantly hot topic. More and more businesses are beginning to understand the potential their data has to allow them to serve customers more effectively and give them a competitive advantage. However, for many small to medium businesses, gaining insight from their data can be challenging because they often lack in-house data […]

May 2024: Connecting to Snowflake as a data source is now supported natively. To learn more, visit our documentation. Historically, inserting and retrieving data from a given database platform has been easier compared to a multi-platform architecture for the same operations. To simplify bringing data in from a multi-database platform, AWS Glue DataBrew supports bringing […]

GIGO (garbage in, garbage out) is a concept common to computer science and mathematics: the quality of the output is determined by the quality of the input. In modern data architecture, you bring data from different data sources, which creates challenges around volume, velocity, and veracity. You might write unit tests for applications, but it’s […]

AWS Glue DataBrew, a visual data preparation tool, now allows users to identify and handle sensitive data by applying advanced transformations like redaction, replacement, encryption, and decryption on their personally identifiable information (PII) data, and other types of data they deem sensitive. With exponential growth of data, companies are handling huge volumes and a wide […]

Marketing teams often rely on data engineers to provide a consumer dataset that they can use to launch marketing campaigns. This can sometimes cause delays in launching campaigns and consume data engineers’ bandwidth. The campaigns are often launched using complex solutions that are either code heavy or using licensed tools. The processes of both extract, […]

As organizations embark on their data modernization journey, big data analytics and machine learning (ML) use cases are becoming even more integral parts of business. The ease for data preparation and seamless integration with third-party data sources is of paramount importance in order to gain insights quickly and make critical business decisions faster. AWS Glue […]

Data volumes in organizations are increasing at an unprecedented rate, exploding from terabytes to petabytes and in some cases exabytes. As data volume increases, it attracts more and more users and applications to use the data in many different ways—sometime referred to as data gravity. As data gravity increases, we need to find tools and […]

One of the key phases of a machine learning (ML) workflow is data preprocessing, which involves cleaning, exploring, and transforming the data. AWS Glue DataBrew, announced in AWS re:Invent 2020, is a visual data preparation tool that enables you to develop common data preparation steps without having to write any code or installation. In this […]