Remove Data Integration Remove Data Processing Remove Measurement
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The DataOps Vendor Landscape, 2021

DataKitchen

RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Azure DevOps. AWS Code Deploy.

Testing 304
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Demystify data sharing and collaboration patterns on AWS: Choosing the right tool for the job

AWS Big Data

Let’s briefly describe the capabilities of the AWS services we referred above: AWS Glue is a fully managed, serverless, and scalable extract, transform, and load (ETL) service that simplifies the process of discovering, preparing, and loading data for analytics. To incorporate this third-party data, AWS Data Exchange is the logical choice.

Sales 115
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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

The applications are hosted in dedicated AWS accounts and require a BI dashboard and reporting services based on Tableau. While real-time data is processed by other applications, this setup maintains high-performance analytics without the expense of continuous processing.

IoT 111
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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. That’s why many enterprises are adopting a two-pronged approach to GenAI.

Strategy 143
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Data confidence begins at the edge

CIO Business Intelligence

Data doubt compounds tough edge challenges The variety of operational challenges at the edge are compounded by the difficulties of sourcing trustworthy data sets from heterogeneous IT/OT estates. Consequently, implementing continuous monitoring systems in these conditions is often not practical or effective.

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Why you should care about debugging machine learning models

O'Reilly on Data

Security vulnerabilities : adversarial actors can compromise the confidentiality, integrity, or availability of an ML model or the data associated with the model, creating a host of undesirable outcomes. 8] , [12] Again, traditional model assessment measures don’t tell us much about whether a model is secure.

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CDOs: Your AI is smart, but your ESG is dumb. Here’s how to fix it

CIO Business Intelligence

However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive. Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams.

IT 59