Remove Deep Learning Remove Optimization Remove Structured Data Remove Unstructured Data
article thumbnail

The Rise of Unstructured Data

Cloudera

Here we mostly focus on structured vs unstructured data. In terms of representation, data can be broadly classified into two types: structured and unstructured. Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else.

article thumbnail

Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

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. Many people are confused about these two, but the only similarity between them is the high-level principle of data storing. Data Warehouse.

Data Lake 106
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

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

IBM Big Data Hub

Data extraction Once you’ve assigned numerical values, you will apply one or more text-mining techniques to the structured data to extract insights from social media data. A targeted approach will optimize the user experience and enhance an organization’s ROI. positive, negative or neutral).

article thumbnail

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?

article thumbnail

Building a Beautiful Data Lakehouse

CIO Business Intelligence

As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio.

Data Lake 119
article thumbnail

Breaking down the advantages and disadvantages of artificial intelligence

IBM Big Data Hub

Data is often divided into three categories: training data (helps the model learn), validation data (tunes the model) and test data (assesses the model’s performance). For optimal performance, AI models should receive data from a diverse datasets (e.g.,