Remove Data Architecture Remove Data Processing Remove Modeling Remove Structured Data
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Large Language Models and Data Management

Ontotext

I did some research because I wanted to create a basic framework on the intersection between large language models (LLM) and data management. But there are also a host of other issues (and cautions) to take into consideration. LLM is by its very design a language model. The technology is very new and not well understood.

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Migrate a petabyte-scale data warehouse from Actian Vectorwise to Amazon Redshift

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. Data store – The data store used a custom data model that had been highly optimized to meet low-latency query response requirements.

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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. All other teams can be data producers or data consumers. default encryption for S3 buckets).”

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

Unified customer profile Graph databases excel in modeling customer interactions and relationships, offering a comprehensive view of the customer journey. Strategize based on how your teams explore data, run analyses, wrangle data for downstream requirements, and visualize data at different levels.

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How Cloudera Data Flow Enables Successful Data Mesh Architectures

Cloudera

The need for a decentralized data mesh architecture stems from the challenges organizations faced when implementing more centralized data management architectures – challenges that can attributed to both technology (e.g., difficulty to achieve cross-organizational governance model). Components of a Data Mesh.

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How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

Overview of solution As a data-driven company, smava relies on the AWS Cloud to power their analytics use cases. smava ingests data from various external and internal data sources into a landing stage on the data lake based on Amazon Simple Storage Service (Amazon S3).