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Expanding data analysis and visualization options: Amazon DataZone now integrates with Tableau, Power BI, and more

AWS Big Data

Amazon DataZone now launched authentication supports through the Amazon Athena JDBC driver, allowing data users to seamlessly query their subscribed data lake assets via popular business intelligence (BI) and analytics tools like Tableau, Power BI, Excel, SQL Workbench, DBeaver, and more. Connect with him on LinkedIn.

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Bridging the gap between mainframe data and hybrid cloud environments

CIO Business Intelligence

A high hurdle many enterprises have yet to overcome is accessing mainframe data via the cloud. Giving the mobile workforce access to this data via the cloud allows them to be productive from anywhere, fosters collaboration, and improves overall strategic decision-making. Four key challenges prevent them from doing so: 1.

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Monitor data pipelines in a serverless data lake

AWS Big Data

The combination of a data lake in a serverless paradigm brings significant cost and performance benefits. By monitoring application logs, you can gain insights into job execution, troubleshoot issues promptly to ensure the overall health and reliability of data pipelines.

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Data transformation takes flight at Atlanta’s Hartsfield-Jackson airport

CIO Business Intelligence

The original proof of concept was to have one data repository ingesting data from 11 sources, including flat files and data stored via APIs on premises and in the cloud, Pruitt says. There are a lot of variables that determine what should go into the data lake and what will probably stay on premise,” Pruitt says.

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Streamline AI-driven analytics with governance: Integrating Tableau with Amazon DataZone

AWS Big Data

With this integration, you can now seamlessly query your governed data lake assets in Amazon DataZone using popular business intelligence (BI) and analytics tools, including partner solutions like Tableau. Joel has led data transformation projects on fraud analytics, claims automation, and Master Data Management.

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

In this article, I am drawing from firsthand experience working with CIOs, CDOs, CTOs and transformation leaders across industries. I aim to outline pragmatic strategies to elevate data quality into an enterprise-wide capability. Inflexible schema, poor for unstructured or real-time data.

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How to modernize data lakes with a data lakehouse architecture

IBM Big Data Hub

Data Lakes have been around for well over a decade now, supporting the analytic operations of some of the largest world corporations. Such data volumes are not easy to move, migrate or modernize. The challenges of a monolithic data lake architecture Data lakes are, at a high level, single repositories of data at scale.