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Perform time series forecasting using Amazon Redshift ML and Amazon Forecast

AWS Big Data

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future.

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Manage your data warehouse cost allocations with Amazon Redshift Serverless tagging

AWS Big Data

Amazon Redshift Serverless makes it simple to run and scale analytics without having to manage your data warehouse infrastructure. In Cost Explorer, you can visualize daily, monthly, and forecasted spend by combining an array of available filters. Tags allows you to assign metadata to your AWS resources.

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Create, train, and deploy Amazon Redshift ML model integrating features from Amazon SageMaker Feature Store

AWS Big Data

Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. Amazon Redshift ML makes it easy for SQL users to create, train, and deploy ML models using SQL commands familiar to many roles such as executives, business analysts, and data analysts.

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What are decision support systems? Sifting data for better business decisions

CIO Business Intelligence

A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. The data sources used by a DSS could include relational data sources, cubes, data warehouses, electronic health records (EHRs), revenue projections, sales projections, and more.

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Swiss energy services company uses machine learning to see the future

CIO Business Intelligence

As the company conceptualized the best measuring solution, planners understood the importance of integrating existing data from diverse sources. The new platform would alleviate this dilemma by using machine learning (ML) algorithms, along with source data accessed by SAP’s Data Warehouse Cloud.

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How a data fabric overcomes data sprawls to reduce time to insights

IBM Big Data Hub

Problem : Traditionally, developing a solid backorder forecast model that takes every factor into consideration would take anywhere from weeks to months as sales data, inventory or lead-time data and supplier data would all reside in disparate data warehouses.

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4 ways to ensure CEO support for your digital strategy

CIO Business Intelligence

But we also have our own internal data that objectively measures needs and results, and helps us communicate with top management.” In fact, CNR has had a data warehouse for 15 years, which gathers information from internal management systems to perform analyses and guide strategies. C-suite support for investments is essential.

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