Remove 2015 Remove Metadata Remove Metrics
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How HPE Aruba Supply Chain optimized cost and performance by migrating to an AWS modern data architecture

AWS Big Data

Hewlett-Packard acquired Aruba Networks in 2015, making it a wireless networking subsidiary with a wide range of next-generation network access solutions. Each file arrives as a pair with a tail metadata file in CSV format containing the size and name of the file. To achieve this, Aruba used Amazon S3 Event Notifications.

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Illuminating the black box: why CIOs should consider publishing an annual IT report

CIO Business Intelligence

By 2015, the technical executives of at least one conglomerate, Intel, had figured they could enrich the firm’s perception of IT by showcasing how essentially that function contributes to business value. And don’t just rattle off project metadata. Such a report has a legacy already, if only a short one. What pains did it alleviate?

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Natural Language in Python using spaCy: An Introduction

Domino Data Lab

For example, with those open source licenses we can download their text, parse, then compare similarity metrics among them: In [12]: pairs = [?. ["mit", "asl"],?. ["asl", "bsd"],?. ["bsd", "mit"] ?]? ?for metadata=convention_df["speaker"]? ). for a, b in pairs:?.

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Turning Streams Into Data Products

Cloudera

In 2015, Cloudera became one of the first vendors to provide enterprise support for Apache Kafka, which marked the genesis of the Cloudera Stream Processing (CSP) offering. The DevOps/app dev team wants to know how data flows between such entities and understand the key performance metrics (KPMs) of these entities.

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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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Introducing the vector engine for Amazon OpenSearch Serverless, now in preview

AWS Big Data

This enables you to process a user’s query to find the closest vectors and combine them with additional metadata without relying on external data sources or additional application code to integrate the results. To create the vector index, you must define the vector field name, dimensions, and the distance metric.

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Replacing Oracle Discoverer: The Smart Way

Jet Global

Chrome: September 2015. Hubble delivers significant benefits to the team, helping us understand key spend metrics.”. However, fear of the unknown has left many companies afraid to implement a new reporting tool, yet the risk of staying with Discoverer increases day by day: Discoverer extended support ended June 2017. Hubble Equivalent.