Category: AWS Glue

With AWS Glue DataBrew, data analysts and data scientists can easily access and visually explore any amount of data across their organization directly from their Amazon Simple Storage Service (Amazon S3) data lake, Amazon Redshift data warehouse, or Amazon Aurora and Amazon Relational Database Service (Amazon RDS) databases. You can choose from over 250 built-in […]

AWS Lake Formation provides a machine learning (ML) capability (FindMatches transform) to identify duplicate or matching records in your dataset, even when the records don’t have a common unique identifier and no fields match exactly. Customers across many industries have come to rely on this feature for linking datasets like patient records, customer databases, and […]

You can build data lakes with millions of objects on Amazon Simple Storage Service (Amazon S3) and use AWS native analytics and machine learning (ML) services to process, analyze, and extract business insights. You can use a combination of our purpose-built databases and analytics services like Amazon EMR, Amazon OpenSearch Service, and Amazon Redshift as […]

  Organizations of all sizes have recognized that data is one of the key enablers to increase and sustain innovation, and drive value for their customers and business units. They are eagerly modernizing traditional data platforms with cloud-native technologies that are highly scalable, feature-rich, and cost-effective. As you look to make business decisions driven by […]

In this post, we briefly walk through the most common design patterns adapted by enterprises to build lake house solutions to support their business agility in a multi-tenant model using the AWS Lake Formation cross-account feature to enable a multi-account strategy for line of business (LOB) accounts to produce and consume data from your data […]

Organizations today want to make data-driven decisions. The data could lie in multiple source systems, such as line of business applications, log files, connected devices, social media, and many more. As organizations adopt software as a service (SaaS) applications, data becomes increasingly fragmented and trapped in different “data islands.” To make decision-making easier, organizations are […]

While creating data lakes on the cloud, the data catalog is crucial to centralize metadata and make the data visible, searchable, and queryable for users. With the recent exponential growth of data volume, it becomes much more important to optimize data layout and maintain the metadata on cloud storage to keep the value of data […]

Data quality plays an important role while building an extract, transform, and load (ETL) pipeline for sending data to downstream analytical applications and machine learning (ML) models. The analogy “garbage in, garbage out” is apt at describing why it’s important to filter out bad data before further processing. Continuously monitoring data quality and comparing it […]

When data analysts and data scientists prepare data for analysis, they often rely on periodically generated data produced by upstream services, such as labeling datasets from Amazon SageMaker Ground Truth or Cost and Usage Reports from AWS Billing and Cost Management. Alternatively, they can regularly upload such data to Amazon Simple Storage Service (Amazon S3) […]

An integral part of DevOps is adopting the culture of continuous integration and continuous delivery (CI/CD). This enables teams to securely store and version code, maintain parity between development and production environments, and achieve end-to-end automation of the release cycle, including building, testing, and deploying to production. In essence, development teams follow CI/CD processes to […]