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What Is a Metadata Catalog? (And How it Can Dramatically Improve Your Data Accuracy)

Octopai

If you’re a mystery lover, I’m sure you’ve read that classic tale: Sherlock Holmes and the Case of the Deceptive Data, and you know how a metadata catalog was a key plot element. Maybe they have different definitions of conversions, which would certainly lead to metrics that don’t match up. Enter the metadata catalog.

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Build an analytics pipeline that is resilient to schema changes using Amazon Redshift Spectrum

AWS Big Data

You can ingest and integrate data from multiple Internet of Things (IoT) sensors to get insights. However, you may have to integrate data from multiple IoT sensor devices to derive analytics like equipment health information from all the sensors based on common data elements.

IoT 97
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Three Emerging Analytics Products Derived from Value-driven Data Innovation and Insights Discovery in the Enterprise

Rocket-Powered Data Science

Accompanying the massive growth in sensor data (from ubiquitous IoT devices, including location-based and time-based streaming data), there have emerged some special analytics products that are growing in significance, especially in the context of innovation and insights discovery from on-prem enterprise data sources.

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Use Amazon OpenSearch Ingestion to migrate to Amazon OpenSearch Serverless

AWS Big Data

OSI is a fully managed, serverless data collector that delivers real-time log, metric, and trace data to OpenSearch Service domains and OpenSearch Serverless collections. Migration of metadata such as security roles and dashboard objects will be covered in another subsequent post.

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

Sources Data can be loaded from multiple sources, such as systems of record, data generated from applications, operational data stores, enterprise-wide reference data and metadata, data from vendors and partners, machine-generated data, social sources, and web sources. Let’s look at the components of the architecture in more detail.

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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

In the subsequent post in our series, we will explore the architectural patterns in building streaming pipelines for real-time BI dashboards, contact center agent, ledger data, personalized real-time recommendation, log analytics, IoT data, Change Data Capture, and real-time marketing data.

Analytics 115
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How to Manage Risk with Modern Data Architectures

Cloudera

Incorporate data from novel sources — social media feeds, alternative credit histories (utility and rental payments), geo-spatial systems, and IoT streams — into liquidity risk models. Use predictive analytics and ML to formalize key intraday liquidity metrics and monitor liquidity positions in real time.