Remove Metadata Remove Publishing Remove Structured Data
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Do I Need a Data Catalog?

erwin

The data catalog is a searchable asset that enables all data – including even formerly siloed tribal knowledge – to be cataloged and more quickly exposed to users for analysis. Three Types of Metadata in a Data Catalog. Technical Metadata. Operational Metadata. for analysis and integration purposes).

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Implement a custom subscription workflow for unmanaged Amazon S3 assets published with Amazon DataZone

AWS Big Data

Amazon DataZone , a data management service, helps you catalog, discover, share, and govern data stored across AWS, on-premises systems, and third-party sources. This solution enhances governance and simplifies access to unstructured data assets across the organization. This is the data that will be published to Amazon DataZone.

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Top 10 Key Features of BI Tools in 2020

FineReport

Metadata management. Users can centrally manage metadata, including searching, extracting, processing, storing, sharing metadata, and publishing metadata externally. The metadata here is focused on the dimensions, indicators, hierarchies, measures and other data required for business analysis.

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The Semantic Web: 20 Years And a Handful of Enterprise Knowledge Graphs Later

Ontotext

The second one is the Linked Open Data (LOD): a cloud of interlinked structured datasets published without centralized control across thousands of servers. There are more than 80 million pages with semantic, machine interpretable metadata , according to the Schema.org standard. Take this restaurant, for example.

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From charred scrolls to customer sentiment: How AI helps you monetize your unstructured data

CIO Business Intelligence

Unlike structured data, which fits neatly into databases and tables, etc. I also doubt that all the data your organization owns that’s been strategically stored or piling up is accurate and trustworthy–-nor that you need to invest in making it so if it’s irrelevant and you don’t plan to use it.

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Non-JSON ingestion using Amazon Kinesis Data Streams, Amazon MSK, and Amazon Redshift Streaming Ingestion

AWS Big Data

JSON data in Amazon Redshift Amazon Redshift enables storage, processing, and analytics on JSON data through the SUPER data type, PartiQL language, materialized views, and data lake queries. The function JSON_PARSE allows you to extract the binary data in the stream and convert it into the SUPER data type.

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Seamless integration of data lake and data warehouse using Amazon Redshift Spectrum and Amazon DataZone

AWS Big Data

Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data. Business units can simply share data and collaborate by publishing and subscribing to the data assets.

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