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Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

The analyst can easily pull in the data they need, use natural language to clean up and fill any missing data, and finally build and deploy a machine learning model that can accurately predict the loan status as an output, all without needing to become a machine learning expert to do so. Choose Create stack.

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How IoT Can Be Connected to Business Intelligence

Smart Data Collective

IoT solutions as well as Business Intelligence tools are widely used by companies all over the world to improve their processes. First of all, you need to define what data should be collected from your IoT devices, processed, and visualized. Ensure cloud data storage. But what if we combine these technologies?

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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

Flipboard

The data in Amazon Redshift is transactionally consistent and updates are automatically and continuously propagated. Together with price-performance, Amazon Redshift offers capabilities such as serverless architecture, machine learning integration within your data warehouse and secure data sharing across the organization.

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Object-centric Process Mining on Data Mesh Architectures

Data Science Blog

In addition to Business Intelligence (BI), Process Mining is no longer a new phenomenon, but almost all larger companies are conducting this data-driven process analysis in their organization. For analysis the way of Business Intelligence this normalized data model can already be used. Click to enlarge!

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5 misconceptions about cloud data warehouses

IBM Journey to AI blog

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.

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Was ist ein Data Lakehouse?

Data Science Blog

Data Lakehouse Architecture Eine kurze Geschichte des Data Lakehouse Das Konzept des Data Lakehouse ist relativ neu und entstand Mitte der 2010er Jahre als Reaktion auf die Einschränkungen des traditionellen Data Warehousing und die wachsende Beliebtheit von Data Lakes. So basieren z.

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Beyond data: Cloud analytics mastery for business brilliance

Dataconomy

Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence. Ensure that data is clean, consistent, and up-to-date.

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