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Ways of Converting Textual Data into Structured Insights with LLMs

Analytics Vidhya

Introduction In the era of big data, organizations are inundated with vast amounts of unstructured textual data. The sheer volume and diversity of information present a significant challenge in extracting insights.

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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Both data warehouses and data lakes are used when storing big data.

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9 Careers You Could Go into With a Data Science Degree

Smart Data Collective

The average data scientist earns over $108,000 a year. The interdisciplinary field of data science involves using processes, algorithms, and systems to extract knowledge and insights from both structured and unstructured data and then applying the knowledge gained from that data across a wide range of applications.

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The DataOps Vendor Landscape, 2021

DataKitchen

Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Genie — Distributed big data orchestration service by Netflix.

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Retailers can tap into generative AI to enhance support for customers and employees

IBM Big Data Hub

Generative AI excels at handling diverse data sources such as emails, images, videos, audio files and social media content. This unstructured data forms the backbone for creating models and the ongoing training of generative AI, so it can stay effective over time.

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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Usually, business or data analysts need to extract insights for reporting purposes, so data warehouses are more suitable for them. On the other hand, a data scientist may require access to unstructured data to detect patterns or build a deep learning model, which means that a data lake is a perfect fit for them.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? Machine learning and deep learning are both subsets of AI.