Remove Data Analytics Remove Data Integration Remove Structured Data Remove Unstructured Data
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8 data strategy mistakes to avoid

CIO Business Intelligence

Building a successful data strategy at scale goes beyond collecting and analyzing data,” says Ryan Swann, chief data analytics officer at financial services firm Vanguard. Overlooking these data resources is a big mistake. What are the goals for leveraging unstructured data?”

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Data governance in the age of generative AI

AWS Big Data

However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications. As part of the transformation, the objects need to be treated to ensure data privacy (for example, PII redaction).

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New Software Development Initiatives Lead To Second Stage Of Big Data

Smart Data Collective

For example, you can organize an employee table in a database in a structured manner to capture the employee’s details, job positions, salary, etc. Unstructured. Unstructured data lacks a specific format or structure. As a result, processing and analyzing unstructured data is super-difficult and time-consuming.

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The Superpowers of Ontotext’s Relation and Event Detector

Ontotext

From a technological perspective, RED combines a sophisticated knowledge graph with large language models (LLM) for improved natural language processing (NLP), data integration, search and information discovery, built on top of the metaphactory platform. Let’s have a quick look under the bonnet.

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How Ruparupa gained updated insights with an Amazon S3 data lake, AWS Glue, Apache Hudi, and Amazon QuickSight

AWS Big Data

Data analytic challenges As an ecommerce company, Ruparupa produces a lot of data from their ecommerce website, their inventory systems, and distribution and finance applications. The data can be structured data from existing systems, and can also be unstructured or semi-structured data from their customer interactions.

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What is a Data Pipeline?

Jet Global

A data pipeline is a series of processes that move raw data from one or more sources to one or more destinations, often transforming and processing the data along the way. Data pipelines support data science and business intelligence projects by providing data engineers with high-quality, consistent, and easily accessible data.