Remove Data Processing Remove Metadata Remove Structured Data
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How ZS built a clinical knowledge repository for semantic search using Amazon OpenSearch Service and Amazon Neptune

AWS Big Data

We use leading-edge analytics, data, and science to help clients make intelligent decisions. We developed and host several applications for our customers on Amazon Web Services (AWS). These embeddings, along with metadata such as the document ID and page number, are stored in OpenSearch Service.

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From Data Silos to Data Fabric with Knowledge Graphs

Ontotext

The Data Fabric paradigm combines design principles and methodologies for building efficient, flexible and reliable data management ecosystems. Knowledge Graphs are the Warp and Weft of a Data Fabric. To implement any Data Fabric approach, it is essential to be able to understand the context of data.

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

Ontotext

LLMs] call into question a fundamental tenet of Data Management: that in order to address non-trivial information needs, the first step is to explicitly structure data in order to lift them from the ambiguous swamp of our human language. Thankfully, lt-innovate.org already did a concise wrap-up.

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Migrate Hive data from CDH to CDP public cloud

Cloudera

Using easy-to-define policies, Replication Manager solves one of the biggest barriers for the customers in their cloud adoption journey by allowing them to move both tables/structured data and files/unstructured data to the CDP cloud of their choice easily. Understanding the data sets to be replicated from the CDH Cluster.

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Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS

AWS Big Data

Spark SQL is an Apache Spark module for structured data processing. FINRA centralizes all its data in Amazon Simple Storage Service (Amazon S3) with a remote Hive metastore on Amazon Relational Database Service (Amazon RDS) to manage their metadata information. or later installed. OutputKey=='HiveSecretName'].OutputValue"

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Design a data mesh on AWS that reflects the envisioned organization

AWS Big Data

They classified the metrics and indicators in the following categories: Data usage – A clear understanding of who is consuming what data source, materialized with a mapping of consumers and producers. In this approach, teams responsible for generating data are referred to as producers.

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Enhance query performance using AWS Glue Data Catalog column-level statistics

AWS Big Data

Data lakes are designed for storing vast amounts of raw, unstructured, or semi-structured data at a low cost, and organizations share those datasets across multiple departments and teams. The queries on these large datasets read vast amounts of data and can perform complex join operations on multiple datasets.