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The importance of data ingestion and integration for enterprise AI

IBM Journey to AI blog

In the generative AI or traditional AI development cycle, data ingestion serves as the entry point. Here, raw data that is tailored to a company’s requirements can be gathered, preprocessed, masked and transformed into a format suitable for LLMs or other models. A popular method is extract, load, transform (ELT).

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Re-evaluating data management in the generative AI age

IBM Journey to AI blog

Enterprise data is often complex, diverse and scattered across various repositories, making it difficult to integrate into gen AI solutions. This complexity is compounded by the need to ensure regulatory compliance, mitigate risk, and address skill gaps in data integration and retrieval-augmented generation (RAG) patterns.

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What is Data Ingestion? Understanding the Basics

Pickl AI

Summary: Data ingestion is the process of collecting, importing, and processing data from diverse sources into a centralised system for analysis. This crucial step enhances data quality, enables real-time insights, and supports informed decision-making. This is where data ingestion comes in.

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A Simple Guide to Real-Time Data Ingestion

Pickl AI

What is Real-Time Data Ingestion? Real-time data ingestion is the practise of gathering and analysing information as it is produced, without little to no lag between the emergence of the data and its accessibility for analysis. Traders need up-to-the-second information to make informed decisions.

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Improving air quality with generative AI

AWS Machine Learning Blog

This post presents a solution that uses a generative artificial intelligence (AI) to standardize air quality data from low-cost sensors in Africa, specifically addressing the air quality data integration problem of low-cost sensors. A human-in-the-loop mechanism safeguards data ingestion.

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A Comprehensive Overview of Data Engineering Pipeline Tools

Marktechpost

ELT Pipelines: Typically used for big data, these pipelines extract data, load it into data warehouses or lakes, and then transform it. Data Integration, Ingestion, and Transformation Pipelines: These pipelines handle the organization of data from multiple sources, ensuring that it is properly integrated and transformed for use.

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A Beginner’s Guide to Data Warehousing

Unite.AI

These can include structured databases, log files, CSV files, transaction tables, third-party business tools, sensor data, etc. The pipeline ensures correct, complete, and consistent data. The data ecosystem is connected to company-defined data sources that can ingest historical data after a specified period.

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