Analyze daily trading activity using transaction data from Amazon Redshift in Amazon FinSpace

Financial services organizations use data from various sources to discover new insights and improve trading decisions. Finding the right dataset and getting access to the data can frequently be a time-consuming process. For example, to analyze daily trading activity, analysts need to find a list of available databases and tables, identify its owner’s contact information, get access, understand the table schema, and load the data.

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How Rapid7 built multi-tenant analytics with Amazon Redshift using near-real-time datasets

This is a guest post co-written by Rahul Monga, Principal Software Engineer at Rapid7. Rapid7 InsightVM is a vulnerability assessment and management product that provides visibility into the risks present across an organization. It equips you with the reporting, automation, and integrations needed to prioritize and fix those vulnerabilities in a fast and efficient manner.

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DXのためのデータ活用を成功させるには「データ活用」だけ考えていてはいけない

データは企業運営に不可欠な資源だ。企業が生成するデータ量が増えるほど、企業の内外に散財するデータの中から適切なデータを見つけて活用することが難しくなる。だからこそデータ活用には戦略が必要だ。中編「 自動化技術が期待外れだった企業こそ『プロセスマイニング』を試すべき理由 」に続く本稿は、デジタルトランスフォーメーション(DX)を支えるデータ活用戦略に組み込むべき4つの要素のうち、3つ目と4つ目を解説する。 

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Field Notes: How to Enable Cross-Account Access for Amazon Kinesis Data Streams using Kinesis Client Library 2.x

Businesses today are dealing with vast amounts of real-time data they need to process and analyze to generate insights. Real-time delivery of data and insights enable businesses to quickly make decisions in response to sensor data from devices, clickstream events, user engagement, and infrastructure events, among many others.

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Klare Sicht auf die Cloud

Die Cloud hat in den letzten zehn Jahren enorm an Akzeptanz und Beliebtheit gewonnen. Unternehmen wie auch öffentliche Einrichtungen setzen auf Cloud-Lösungen, da sie kostengünstig, flexibel, einfach zu bedienen, sicher und schnell zu implementieren sind.

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Field Notes: Tracking Overall Equipment Effectiveness with AWS IoT Analytics and Amazon QuickSight

This post was co-written with Michael Brown, Senior Solutions Architect, Manufacturing at AWS. Overall equipment effectiveness (OEE) is a measure of how well a manufacturing operation is utilized (facilities, time and material) compared to its full potential, during the periods when it is scheduled to run.

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AI/機械学習とデータ分析の関係を知る(2) シチズン・データ・サイエンティストの役割

前回 は、AI/機械学習がもたらすデータ分析業務の効率化の結果としてもたらされる、データ・サイエンティスト組織の少数精鋭化とシチズン・データ・サイエンティストの登場について解説しました。今回は、シチズン・データ・サイエンティストの役割と、これからのデータ分析の組織・体制について解説します

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