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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Data Warehouses and Data Lakes in a Nutshell. A data warehouse is used as a central storage space for large amounts of structured data coming from various sources. On the other hand, data lakes are flexible storages used to store unstructured, semi-structured, or structured raw data.

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Leveraging user-generated social media content with text-mining examples

IBM Big Data Hub

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

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AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference?

IBM Big Data Hub

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

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Themes and Conferences per Pacoid, Episode 7

Domino Data Lab

Over the past six months, Ben Lorica and I have conducted three surveys about “ABC” (AI, Big Data, Cloud) adoption in enterprise. O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). What’s been the impact of using ML models on culture and organization?

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Breaking down the advantages and disadvantages of artificial intelligence

IBM Big Data Hub

Data: AI systems learn and make decisions based on data, and they require large quantities of data to train effectively, especially in the case of machine learning (ML) models. For optimal performance, AI models should receive data from a diverse datasets (e.g.,

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Building a Beautiful Data Lakehouse

CIO Business Intelligence

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure.

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Take advantage of AI and use it to make your business better

IBM Big Data Hub

To overcome these challenges will require a shift in many of the processes and models that businesses use today: changes in IT architecture, data management and culture. A common phrase you’ll hear around AI is that artificial intelligence is only as good as the data foundation that shapes it.

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