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Amazon Q data integration adds DataFrame support and in-prompt context-aware job creation

AWS Big Data

Amazon Q data integration , introduced in January 2024, allows you to use natural language to author extract, transform, load (ETL) jobs and operations in AWS Glue specific data abstraction DynamicFrame. In this post, we discuss how Amazon Q data integration transforms ETL workflow development.

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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

With the growing emphasis on data, organizations are constantly seeking more efficient and agile ways to integrate their data, especially from a wide variety of applications. We take care of the ETL for you by automating the creation and management of data replication. Glue ETL offers customer-managed data ingestion.

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How To Use Airbyte, dbt-teradata, Dagster, and Teradata Vantage™ for Seamless Data Integration

Teradata

Register now Home Insights Data platform Article How To Use Airbyte, dbt-teradata, Dagster, and Teradata Vantage™ for Seamless Data Integration Build and orchestrate a data pipeline in Teradata Vantage using Airbyte, Dagster, and dbt. Register now Join us at Possible 2025.

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Bridging the AI Execution Gap: Why Strong Data Foundations Make or Break Enterprise AI

Jen Stirrup

Fragmented Systems and Data Silos Enterprise data typically resides across dozens—sometimes hundreds—of disparate systems: legacy databases, modern cloud platforms, departmental applications, and third-party services. When these systems don't communicate effectively, AI initiatives cannot access the comprehensive data they need.

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Data & Analytics Maturity Model Workshop Series

Speaker: Dave Mariani, Co-founder & Chief Technology Officer, AtScale; Bob Kelly, Director of Education and Enablement, AtScale

Workshop video modules include: Breaking down data silos. Integrating data from third-party sources. Developing a data-sharing culture. Combining data integration styles. Translating DevOps principles into your data engineering process. Using data models to create a single source of truth.

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Build ETL Pipelines for Data Science Workflows in About 30 Lines of Python

KDnuggets

More On This Topic Developing Robust ETL Pipelines for Data Science Projects Data Science ETL Pipelines with DuckDB Build a Data Cleaning & Validation Pipeline in Under 50 Lines of Python Automatically Build AI Workflows with Magical AI Multi-modal deep learning in less than 15 lines of code SQL and Data Integration: ETL and ELT Our Top 5 Free Course (..)

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

While real-time data is processed by other applications, this setup maintains high-performance analytics without the expense of continuous processing. This agility accelerates EUROGATEs insight generation, keeping decision-making aligned with current data.

IoT
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Building Best-in-Class Enterprise Analytics

Speaker: Anthony Roach, Director of Product Management at Tableau Software, and Jeremiah Morrow, Partner Solution Marketing Director at Dremio

Tableau works with Strategic Partners like Dremio to build data integrations that bring the two technologies together, creating a seamless and efficient customer experience. Through co-development and Co-Ownership, partners like Dremio ensure their unique capabilities are exposed and can be leveraged from within Tableau.