CIO Business Intelligence

MARCH 9, 2022

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

Data Lake Unstructured Data Data Warehouse Big Data 119

Smart Data Collective

SEPTEMBER 23, 2020

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 Lake 106

Data Lake Data Warehouse Unstructured Data Big Data 106

CIO Business Intelligence

JUNE 14, 2023

How is it possible to manage the data lifecycle, especially for extremely large volumes of unstructured data? Unlike structured data, which is organized into predefined fields and tables, unstructured data does not have a well-defined schema or structure.

Unstructured Data 64

Unstructured Data IT Manufacturing Visualization 64

Analytics Vidhya

AUGUST 26, 2021

This article was published as a part of the Data Science Blogathon The intersection of medicine and data science has always been relevant; perhaps the most obvious example is the implementation of neural networks in deep learning. Nanotechnology, stem cells, […].

Deep Learning 291

Deep Learning Machine Learning Data Science Publishing 291

Analytics Vidhya

JULY 25, 2022

This article was published as a part of the Data Science Blogathon. Machine Learning is the method of teaching computer programs to do a specific task accurately (essentially a prediction) by training a predictive model using various statistical algorithms leveraging data. Source: [link] For […].

Machine Learning 268

Machine Learning Predictive Modeling Statistics Data Science 268

Domino Data Lab

AUGUST 9, 2021

With the right tools, your data science teams can focus on what they do best – testing, developing and deploying new models while driving forward-thinking innovation. In general terms, a model is a series of algorithms that can solve problems when given appropriate data. It’s most helpful in analyzing structured data.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

Analytics Vidhya

APRIL 29, 2022

This article was published as a part of the Data Science Blogathon. Introduction Azure data factory (ADF) is a cloud-based ETL (Extract, Transform, Load) tool and data integration service which allows you to create a data-driven workflow. In this article, I’ll show […].

Data-driven 321

Data-driven Data Transformation Data Science Data Integration 321

CIO Business Intelligence

APRIL 25, 2022

“You can think that the general-purpose version of the Databricks Lakehouse as giving the organization 80% of what it needs to get to the productive use of its data to drive business insights and data science specific to the business. Features focus on media and entertainment firms.

Recreation/Entertainment 119

Recreation/Entertainment Data Lake Data Warehouse Unstructured Data 119

Analytics Vidhya

JUNE 11, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon What is Computer Vision? Computer vision is a field of artificial. The post PAN card fraud detection using computer vision appeared first on Analytics Vidhya.

Data Science 295

Data Science Publishing Analytics Unstructured Data 295

IBM Big Data Hub

JANUARY 10, 2024

Artificial intelligence (AI) refers to the convergent fields of computer and data science focused on building machines with human intelligence to perform tasks that would previously have required a human being. In order to “teach” a program new information, the programmer must manually add new data or adjust processes.

Unstructured Data 75

Unstructured Data Deep Learning Cost-Benefit Machine Learning 75

Cloudera

JUNE 7, 2022

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

IT 112

IT Data Architecture Unstructured Data Big Data 112

Analytics Vidhya

MAY 20, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction We produce a massive amount of data each day, whether. The post What is Big Data? Introduction, Uses, and Applications. appeared first on Analytics Vidhya.

Big Data 192

Big Data Data Science Publishing Analytics 192

CIO Business Intelligence

JUNE 23, 2022

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

IT 97

IT Data Architecture Unstructured Data Big Data 97

bridgei2i

FEBRUARY 28, 2020

With AI, apart from the quantitative data, unstructured data systems can be assessed for risk management. With AI, one can manage entire portfolios by identifying stock price movement trends from both unstructured and structured data sources. Trading was anyway decision making in mere fractions of seconds.

Risk Management 78

Risk Management Unstructured Data Machine Learning Digital Transformation 78

Cloudera

JULY 26, 2021

Once we have identified those capabilities, the second article explores how the Cloudera Data Platform delivers those prerequisite capabilities and has enabled organizations such as IQVIA to innovate in Healthcare with the Human Data Science Cloud. . Business and Technology Forces Shaping Data Product Development.

Strategy 119

Strategy Data Science Unstructured Data Marketing 119

Ontotext

NOVEMBER 2, 2023

The pathway forward doesn’t require ripping everything out but building a semantic “graph” layer across data to connect the dots and restore context. However, it will take effort to formalize a shared semantic model that can be mapped to data assets, and turn unstructured data into a format that can be mined for insight.

IT 69

IT Cost-Benefit Data-driven Technology 69

Cloudera

SEPTEMBER 6, 2018

Support machine learning (ML) algorithms and data science activities, to help with name matching, risk scoring, link analysis, anomaly detection, and transaction monitoring. Provide audit and data lineage information to facilitate regulatory reviews. Spark also enables data science at scale. Cloudera Enterprise.

Machine Learning 40

Machine Learning Big Data Risk Unstructured Data 40

IBM Big Data Hub

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. 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.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

CIO Business Intelligence

JUNE 28, 2022

Philosophers and economists may argue about the quality of the metaphor, but there’s no doubt that organizing and analyzing data is a vital endeavor for any enterprise looking to deliver on the promise of data-driven decision-making. And to do so, a solid data management strategy is key.

Management 138

Management Internet of Things Statistics Data-driven 138

Cloudera

APRIL 1, 2024

Cloudera, a leader in big data analytics, provides a unified Data Platform for data management, AI, and analytics. Our customers run some of the world’s most innovative, largest, and most demanding data science, data engineering, analytics, and AI use cases, including PB-size generative AI workloads.

Unstructured Data 105

Unstructured Data Cost-Benefit Metadata Machine Learning 105

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. There are many types of data pipelines, and all of them include extract, transform, load (ETL) to some extent.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52