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Introduction to Power BI Datamarts

ODSC - Open Data Science

They all agree that a Datamart is a subject-oriented subset of a data warehouse focusing on a particular business unit, department, subject area, or business functionality. The Datamart’s data is usually stored in databases containing a moving frame required for data analysis, not the full history of data.

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Leveraging KNIME and Power BI: Integrating Power BI in KNIME

phData

KNIME and Power BI: The Power of Integration The data analytics process invariably involves a crucial phase: data preparation. This phase demands meticulous customization to optimize data for analysis. Consider a scenario: a data repository residing within a cloud-based data warehouse.

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How Thomson Reuters delivers personalized content subscription plans at scale using Amazon Personalize

AWS Machine Learning Blog

TR has a wealth of data that could be used for personalization that has been collected from customer interactions and stored within a centralized data warehouse. The user interactions data from various sources is persisted in their data warehouse. The following diagram illustrates the ML training pipeline.

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Tackling AI’s data challenges with IBM databases on AWS

IBM Journey to AI blog

Db2 Warehouse fully supports open formats such as Parquet, Avro, ORC and Iceberg table format to share data and extract new insights across teams without duplication or additional extract, transform, load (ETL). This allows you to scale all analytics and AI workloads across the enterprise with trusted data. 

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Exploring the AI and data capabilities of watsonx

IBM Journey to AI blog

Visual modeling: Delivers easy-to-use workflows for data scientists to build data preparation and predictive machine learning pipelines that include text analytics, visualizations and a variety of modeling methods. It is supported by querying, governance, and open data formats to access and share data across the hybrid cloud.

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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning Blog

These connections are used by AWS Glue crawlers, jobs, and development endpoints to access various types of data stores. You can use these connections for both source and target data, and even reuse the same connection across multiple crawlers or extract, transform, and load (ETL) jobs.

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How to Use Exploratory Notebooks [Best Practices]

The MLOps Blog

Placing functions for plotting, data loading, data preparation, and implementations of evaluation metrics in plain Python modules keeps a Jupyter notebook focused on the exploratory analysis | Source: Author Using SQL directly in Jupyter cells There are some cases in which data is not in memory (e.g.,

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