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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. Apache Iceberg addresses customer needs by capturing rich metadata information about the dataset at the time the individual data files are created.

Data Lake 114
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Do Large Language Models Dream of Knowledge Graphs – Impressions from Day 2 At SEMANTiCS 2023

Ontotext

I learned that fact from a comment in the audience on the second day of SEMANTICS 2023 – the European conference series focused on semantic technologies ever since 2005. Both speakers talked about common metadata standards and adequate language resources as key enablers of efficient interoperable, multilingual projects.

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The Very Group adopts a data catalog to better organize and leverage its online retail capabilities

CIO Business Intelligence

The group’s move online began in the 1990s with its first steps into e-commerce, followed by the closure of its physical stores in 2005. It took about nine weeks to set up the infrastructure, make the connection to the database, and index and understand the metadata.

IT 98
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Gain insights from historical location data using Amazon Location Service and AWS analytics services

AWS Big Data

The Data Catalog provides metadata that allows analytics applications using Athena to find, read, and process the location data stored in Amazon S3. The crawlers will automatically classify the data into JSON format, group the records into tables and partitions, and commit associated metadata to the AWS Glue Data Catalog. Choose Run.

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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

The second streaming data source constitutes metadata information about the call center organization and agents that gets refreshed throughout the day. This data contains metadata information like organization names for their respective organization IDs, agent names, and more. client("s3") S3_BUCKET = ' ' kinesis_client = boto3.client("kinesis")

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Modernize Using The BI & Analytics Magic Quadrant

Rita Sallam

Or when Tableau and Qlik’s serious entry into the market circa 2004-2005 set in motion a seismic market shift from IT to the business user creating the wave of what was to become the modern BI disruption. After five minutes of seeing these products back then, I just knew they would change everything!

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GraphDB Users Ask: Is RDF-Star The Best Choice For Reification?

Ontotext

As an abstract knowledge representation model, it does not differentiate between data and metadata. Therefore, if you want to model quadruples or more complex relationships, which store both the data (triple) and its metadata as a single datapoint, you have to normalize the connection somehow. standard. . #