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Build and manage your modern data stack using dbt and AWS Glue through dbt-glue, the new “trusted” dbt adapter

AWS Big Data

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

Data Lake 103
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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

“Digitizing was our first stake at the table in our data journey,” he says. That step, primarily undertaken by developers and data architects, established data governance and data integration. That step, primarily undertaken by developers and data architects, established data governance and data integration.

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Connect your data for faster decisions with AWS

AWS Big Data

Second, organizations still need transformations like cleansing, deduplication, and combining datasets for analysis and machine learning (ML). For these, AWS Glue provides fast, scalable data transformation. Prior to his current role, he was VP of Analytics at AWS, where he worked across the entire AWS database portfolio.

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Reference guide to build inventory management and forecasting solutions on AWS

AWS Big Data

By collecting data from store sensors using AWS IoT Core , ingesting it using AWS Lambda to Amazon Aurora Serverless , and transforming it using AWS Glue from a database to an Amazon Simple Storage Service (Amazon S3) data lake, retailers can gain deep insights into their inventory and customer behavior.

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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

It does this by helping teams handle the T in ETL (extract, transform, and load) processes. It allows users to write data transformation code, run it, and test the output, all within the framework it provides. This process has been scheduled to run daily, ensuring a consistent batch of fresh data for analysis.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

In other words, instead of training numerous models on labeled, task-specific data, it’s now possible to pre-train one big model built on a transformer and then, with additional fine-tuning, reuse it as needed. They offer an enterprise-ready dataset with trusted data that’s undergone negative and positive curation.

Risk 70
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How Tricentis unlocks insights across the software development lifecycle at speed and scale using Amazon Redshift

AWS Big Data

Tricentis is the global leader in continuous testing for DevOps, cloud, and enterprise applications. From detailed design to a beta release, Tricentis had customers expecting to consume data from a data lake specific to only their data, and all of the data that had been generated for over a decade.