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Data Warehouses: Basic Concepts for data enthusiasts

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction The purpose of a data warehouse is to combine multiple sources to generate different insights that help companies make better decisions and forecasting. It consists of historical and commutative data from single or multiple sources.

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Biggest Trends in Data Visualization Taking Shape in 2022

Smart Data Collective

Data virtualization is ideal in any situation where the is necessary: Information coming from diverse data sources. Multi-channel publishing of data services. How does Data Virtualization complement Data Warehousing and SOA Architectures? In forecasting future events. Real-time information.

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How HPE Aruba Supply Chain optimized cost and performance by migrating to an AWS modern data architecture

AWS Big Data

This also includes building an industry standard integrated data repository as a single source of truth, operational reporting through real time metrics, data quality monitoring, 24/7 helpdesk, and revenue forecasting through financial projections and supply availability projections.

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Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics, Part 3: Visualization and trend analysis using Amazon QuickSight

AWS Big Data

Publish the QuickSight dashboard When the analysis is ready, complete the following steps to publish the dashboard: Choose PUBLISH. Select Publish new dashboard as , and enter GlueObservabilityDashboard. Select Publish new dashboard as , and enter GlueObservabilityDashboard. Choose Publish dashboard.

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How HelloFresh is Disrupting the Grocery Industry Using Deep Customer Insights.

Cloudera

We’ve just published our most recent customer success story ! This story gives a look at how HelloFresh is becoming a more data centric organization to better serve its customers. For HelloFresh, data is key to understanding customer preferences, including what recipes, ingredients, and meals each household likes.

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IBM to help businesses scale AI workloads, for all data, anywhere

IBM Big Data Hub

Watsonx.data will allow users to access their data through a single point of entry and run multiple fit-for-purpose query engines across IT environments. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. [1]

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Upcoming Power BI Workshop in Brisbane – 27 Mar 2015

Ms SQL Girl

Data Modelling Patterns 101 using Power Pivot. Tips and Tricks on Charts and Data Models. Publishing and Administering Dashboards and Reports in Power BI for the Organisation. Her recent projects include delivering a SQL Server 2012 Data Warehouse and BI solutions for a number of high profile clients in the US and Australia.