Remove Data Processing Remove Metadata Remove Optimization Remove Unstructured Data
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KGF 2023: Bikes To The Moon, Datastrophies, Abstract Art And A Knowledge Graph Forum To Embrace Them All

Ontotext

Atanas Kiryakov presenting at KGF 2023 about Where Shall and Enterprise Start their Knowledge Graph Journey Only data integration through semantic metadata can drive business efficiency as “it’s the glue that turns knowledge graphs into hubs of metadata and content”.

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Petabyte-scale log analytics with Amazon S3, Amazon OpenSearch Service, and Amazon OpenSearch Ingestion

AWS Big Data

Organizations often need to manage a high volume of data that is growing at an extraordinary rate. At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. Cold storage is optimized to store infrequently accessed or historical data.

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Ontotext Invents the Universe So You Don’t Need To

Ontotext

Businesses wanted a way to make pie and not an in-depth understanding of forward-chaining, inferential explosion or SPARQL optimizations. Content Enrichment and Metadata Management. The value of metadata for content providers is well-established.

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Discover and Explore Data Faster with the CDP DDE Template

Cloudera

DDE also makes it much easier for application developers or data workers to self-service and get started with building insight applications or exploration services based on text or other unstructured data (i.e. data best served through Apache Solr). Coordinates distribution of data and metadata, also known as shards.

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. The long history and pervasiveness of SQL has helped make data-driven work much more accessible to a wider audience.

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The new challenges of scale: What it takes to go from PB to EB data scale

CIO Business Intelligence

To accomplish this, we will need additional data center space, more storage disks and nodes, the ability for the software to scale to 1000+PB of data, and increased support through additional compute nodes and networking bandwidth. Focus on scalability.