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How Amazon Devices scaled and optimized real-time demand and supply forecasts using serverless analytics

AWS Big Data

To further optimize and improve the developer velocity for our data consumers, we added Amazon DynamoDB as a metadata store for different data sources landing in the data lake. We used the same AWS Glue jobs to further transform and load the data into the required S3 bucket and a portion of extracted metadata into DynamoDB.

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Achieve your AI goals with an open data lakehouse approach

IBM Big Data Hub

Consider this, a forecast by IDC shows that global spending on AI will surpass $300 billion in 2026, resulting in a compound annual growth rate (CAGR) of 26.5% Also, a lakehouse can introduce definitional metadata to ensure clarity and consistency, which enables more trustworthy, governed data. from 2022 to 2026.

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BMW Cloud Efficiency Analytics powered by Amazon QuickSight and Amazon Athena

AWS Big Data

It seamlessly consolidates data from various data sources within AWS, including AWS Cost Explorer (and forecasting with Cost Explorer ), AWS Trusted Advisor , and AWS Compute Optimizer. These sources encompass the AWS Cost and Usage Reports, Cost Explorer (and forecasting with Cost Explorer), Trusted Advisor, and Compute Optimizer.

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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

QuickSight makes it straightforward for business users to visualize data in interactive dashboards and reports. An AWS Glue crawler scans data on the S3 bucket and populates table metadata on the AWS Glue Data Catalog. All of the resources are defined in a sample AWS Cloud Development Kit (AWS CDK) template.

Metrics 107
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Top 10 Key Features of BI Tools in 2020

FineReport

They prefer self-service development, interactive dashboards, and self-service data exploration. Metadata management. Users can centrally manage metadata, including searching, extracting, processing, storing, sharing metadata, and publishing metadata externally. Interactive visual exploration.

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Minimizing Supply Chain Disruptions with Advanced Analytics

Cloudera

Accurate demand forecasting can’t rely upon last year’s data based upon dated consumer preferences, lifestyle and demand patterns that just don’t exist today – the world has changed. The last eighteen months is causing supply chain forecasters to rethink the definition and incorporate risk into the planning process. .

Analytics 109
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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

Customer 360 (C360) provides a complete and unified view of a customer’s interactions and behavior across all touchpoints and channels. Profile aggregation – When you’ve uniquely identified a customer, you can build applications in Managed Service for Apache Flink to consolidate all their metadata, from name to interaction history.