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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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Artificial intelligence and machine learning adoption in European enterprise

O'Reilly on Data

How companies in Europe are preparing for and adopting AI and ML technologies. In a recent survey , we explored how companies were adjusting to the growing importance of machine learning and analytics, while also preparing for the explosion in the number of data sources. Data Platforms. Data Integration and Data Pipelines.

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The DataOps Vendor Landscape, 2021

DataKitchen

Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Genie — Distributed big data orchestration service by Netflix.

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Scaling RISE with SAP data and AWS Glue

AWS Big Data

The SAP OData connector supports both on-premises and cloud-hosted (native and SAP RISE) deployments. By using the AWS Glue OData connector for SAP, you can work seamlessly with your data on AWS Glue and Apache Spark in a distributed fashion for efficient processing.

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AI Technology is Invaluable for Cybersecurity

Smart Data Collective

AI technology is helping with cybersecurity in a myriad of ways. The proliferation of cybersecurity firms reflects the increasing sophistication of cyber threats in today’s technology-driven society. They also uphold relevant regulations and protect systems, data, and communications.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

With the addition of these technologies alongside existing systems like terminal operating systems (TOS) and SAP, the number of data producers has grown substantially. However, much of this data remains siloed and making it accessible for different purposes and other departments remains complex. She can reached via LinkedIn.

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The success of GenAI models lies in your data management strategy

CIO Business Intelligence

However, this enthusiasm may be tempered by a host of challenges and risks stemming from scaling GenAI. As the technology subsists on data, customer trust and their confidential information are at stake—and enterprises cannot afford to overlook its pitfalls. An example is Dell Technologies Enterprise Data Management.

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