Category: AWS Glue DataBrew

As the industry grows with more data volume, big data analytics is becoming a common requirement in data analytics and machine learning (ML) use cases. Data comes from many different sources in structured, semi-structured, and unstructured formats. For semi-structured data, one of the most common lightweight file formats is JSON. However, due to the complex […]

AWS Glue DataBrew offers over 350 pre-built transformations to automate data preparation tasks (such as filtering anomalies, standardizing formats, and correcting invalid values) that would otherwise require days or weeks writing hand-coded transformations. You can now choose single or multiple output files instead of autogenerated files for your DataBrew recipe jobs. You can generate a […]

Do you want to reduce stockouts at stores? Do you want to improve order delivery timelines? Do you want to provide your customers with accurate product availability, down to the millisecond? A retail operational data lake can help you transform the customer experience by providing deeper insights into a variety of operational aspects of your […]

Organizations today have continuous incoming data, and analyzing this data in a timely fashion is becoming a common requirement for data analytics and machine learning (ML) use cases. As part of this, you need clean data in order to gain insights that can enable enterprises to get the most out of their data for business […]

AWS Glue DataBrew offers over 250 pre-built transformations to automate data preparation tasks (such as filtering anomalies, standardizing formats, and correcting invalid values) that would otherwise require days or weeks writing hand-coded transformations. You can now write cleaned and normalized data directly into JDBC-supported databases and data warehouses without having to move large amounts of […]

Personally identifiable information (PII) data handling is a common requirement when operating a data lake at scale. Businesses often need to mitigate the risk of exposing PII data to the data science team while not hindering the productivity of the team to get to the data they need in order to generate valuable data insights. […]

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 […]

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 your data in from multiple data sources via the AWS Glue Data Catalog. However, this requires you to have […]