Remove Big Data Remove Data Analytics Remove Deep Learning Remove Unstructured Data
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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.

Data Lake 106
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Perplexing Impacts of AI on The Future Insurance Claims

Smart Data Collective

We previously talked about the benefits of data analytics in the insurance industry. One report found that big data vendors will generate over $2.4 Key benefits of AI include recognizing speech, identifying objects in an image, and analyzing natural or unstructured data forms. Are we close to AI reliance?

Insurance 135
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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.

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The most valuable AI use cases for business

IBM Big Data Hub

By infusing AI into IT operations , companies can harness the considerable power of NLP, big data, and ML models to automate and streamline operational workflows, and monitor event correlation and causality determination. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions.

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Building a Beautiful Data Lakehouse

CIO Business Intelligence

Applying artificial intelligence (AI) to data analytics for deeper, better insights and automation is a growing enterprise IT priority. 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.

Data Lake 111
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The DataOps Vendor Landscape, 2021

DataKitchen

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Testing and Data Observability.

Testing 307
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Biggest Trends in Data Visualization Taking Shape in 2022

Smart Data Collective

There are countless examples of big data transforming many different industries. There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. We would like to talk about data visualization and its role in the big data movement.