Edureka

MARCH 26, 2025

Drug Discovery: Generative models simulate molecular structures to identify potential drugs. Predictive Analytics: Forecasting patient outcomes and optimizing treatment plans using patient data. Manufacturing: Predictive Maintenance: Neural networks monitor machine performance to predict and prevent breakdowns.

Raw Data

ProjectPro

JUNE 6, 2025

An approach to performing customer market basket analysis can be done using Apriori and Fp Growth data mining algorithms. Source Code: Market Basket Analysis using Apriori and Fp Growth Algorithms 2) Reducing Manufacturing Failures Product-based companies have the task of ensuring that their products are top of the notch.

Data Analytics

Cloudera

NOVEMBER 15, 2021

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.

Unstructured Data

AltexSoft

MAY 12, 2023

In today’s data-driven world, organizations amass vast amounts of information that can unlock significant insights and inform decision-making. A staggering 80 percent of this digital treasure trove is unstructured data, which lacks a pre-defined format or organization. What is unstructured data?

Unstructured Data

RandomTrees

OCTOBER 9, 2024

Today’s manufacturing landscape is truly on a whole new level, and getting perfection has never been more intense. This is where Generative Artificial Intelligence, simply known as GenAI, comes in and is currently being used to transform quality assurance in manufacturing processes.

Manufacturing

Cloudera

JUNE 11, 2024

By leveraging an organization’s proprietary data, GenAI models can produce highly relevant and customized outputs that align with the business’s specific needs and objectives. Structured data is highly organized and formatted in a way that makes it easily searchable in databases and data warehouses.

Unstructured Data

Snowflake

JULY 10, 2023

“California Air Resources Board has been exploring processing atmospheric data delivered from four different remote locations via instruments that produce netCDF files. Previously, working with these large and complex files would require a unique set of tools, creating data silos. ” U.S.

Unstructured Data

Edureka

JUNE 7, 2024

paintings, songs, code) Historical data relevant to the prediction task (e.g., Here’s a detailed breakdown of the core algorithms that power predictive AI: Machine Learning Algorithms These algorithms help identify patterns and make predictions based on structured data. stock market trends).

Deep Learning

Cloudera

OCTOBER 28, 2021

There is a wealth of data now available to make this possible. For example, the types of data sourced from other industries that we can use in the underwriting process include: Manufacturing – sensors (for quality, safety and maintenance-related). This results in enhancements in finance reporting or compliance.

Insurance

Cloudera

DECEMBER 11, 2020

Without meeting GxP compliance, the Merck KGaA team could not run the enterprise data lake needed to store, curate, or process the data required to inform business decisions. It established a data governance framework within its enterprise data lake. Underpinning everything with security and governance.

Data Lake

Cloudera

SEPTEMBER 15, 2022

Today’s platform owners, business owners, data developers, analysts, and engineers create new apps on the Cloudera Data Platform and they must decide where and how to store that data. Structured data (such as name, date, ID, and so on) will be stored in regular SQL databases like Hive or Impala databases.

Systems

Monte Carlo

JANUARY 16, 2024

Data issues identified and resolved faster A bright and rapidly evolving future 1. Data lake and data warehouse convergence The data lake vs data warehouse question is constantly evolving. The maxim that data warehouses hold structured data while data lakes hold unstructured data is quickly breaking down.

Data Lake

Knowledge Hut

APRIL 23, 2024

Data storing and processing is nothing new; organizations have been doing it for a few decades to reap valuable insights. Compared to that, Big Data is a much more recently derived term. So, what exactly is the difference between Traditional Data and Big Data? However, it also severely limits the scope of the data.

Big Data

Knowledge Hut

DECEMBER 26, 2023

Analyzing and organizing raw data Raw data is unstructured data consisting of texts, images, audio, and videos such as PDFs and voice transcripts. The job of a data engineer is to develop models using machine learning to scan, label and organize this unstructured data.

Data Engineering

Knowledge Hut

APRIL 25, 2024

Variety: Unstructured data, semi-structured data, and raw data are only a few examples of the variety of data kinds that exist. Based on data about which product is being searched for/sold most frequently, manufacturing and collection rates for that product are fixed.

Big Data

Knowledge Hut

MARCH 7, 2024

Factors Considered for Selecting the Best Big Data Analytics Tools There are a few factors to consider when selecting the best big data analytics tool for your organization. The first is the type of data you have, which will determine the tool you need.

Big Data

AltexSoft

AUGUST 29, 2023

Data sources can be broadly classified into three categories. Structured data sources. These are the most organized forms of data, often originating from relational databases and tables where the structure is clearly defined. Semi-structured data sources. Unstructured data sources.

Data Lake

Knowledge Hut

DECEMBER 29, 2023

A Data Engineer's primary responsibility is the construction and upkeep of a data warehouse. In this role, they would help the Analytics team become ready to leverage both structured and unstructured data in their model creation processes. They construct pipelines to collect and transform data from many sources.

Data Science

Knowledge Hut

JUNE 16, 2023

Data preparation: Because of flaws, redundancy, missing numbers, and other issues, data gathered from numerous sources is always in a raw format. After the data has been extracted, data analysts must transform the unstructured data into structured data by fixing data errors, removing unnecessary data, and identifying potential data.

Programming Language

Striim

AUGUST 22, 2024

For instance, say you work in a manufacturing plant and are looking to use real-time analytics to find faults in your machinery. You can use machine sensors to collect data and analyze it in real time to deduct if there are any signs of failure. You’ll also need to have an efficient way of collecting it.

Data Warehouse

ProjectPro

SEPTEMBER 26, 2014

The toughest challenges in business intelligence today can be addressed by Hadoop through multi-structured data and advanced big data analytics. Big data technologies like Hadoop have become a complement to various conventional BI products and services. Big data, multi-structured data, and advanced analytics.

Business Intelligence

Knowledge Hut

MARCH 27, 2024

Factors Considered for Selecting the Best Big Data Analytics Tools There are a few factors to consider when selecting the best big data analytics tool for your organization. The first is the type of data you have, which will determine the tool you need.

Big Data

ProjectPro

MARCH 15, 2016

Such large commercial banks can leverage big data analytics more effectively by using frameworks like Hadoop on massive volumes of structured and unstructured data. Hadoop allows us to store data that we never stored before. Another example of Big Data management in the telecom industry comes from Nokia.

Hadoop

ProjectPro

NOVEMBER 14, 2015

As we step into the latter half of the present decade, we can’t help but notice the way Big Data has entered all crucial technology-powered domains such as banking and financial services, telecom, manufacturing, information technology, operations, and logistics.

Hadoop

AltexSoft

NOVEMBER 1, 2023

For others, however, this will be a car manufacturer with the same name. NER for structuring unstructured data NER plays a pivotal role in converting unstructured text into structured data. Let’s take the name Lincoln , for example. A simple word, it has different meanings.

Deep Learning

ProjectPro

MARCH 14, 2014

As the big data boom spreads globally, we at ProjectPro describe on how big data helps business across different industries and the companies using big data that stand to gain the most from implementing big data initiatives. Hadoop is used at eBay for Search Optimization and Research.

Hadoop

U-Next

OCTOBER 27, 2022

Cloud computing is becoming increasingly popular in the healthcare, BFSI, and manufacturing sectors in the region as the use of cloud computing has grown rapidly. You don’t have to worry about patching, taking a backup, or upgrading data. The company provides structured data management services exclusively.

AWS

ProjectPro

OCTOBER 20, 2021

Six Sigma, factory-like approach to manufacturing and managing algorithm Considering algorithms as part of the entire flow instead of the whole process means that we can focus more on manufacturing algorithms and reducing errors. Auto-scaling and scheduling Portability and virtualization 2. Discuss a few use cases.

Machine Learning

Knowledge Hut

DECEMBER 26, 2023

After carefully exploring what we mean when we say "big data," the book explores each phase of the big data lifecycle. With Tableau, which focuses on big data visualization , you can create scatter plots, histograms, bar, line, and pie charts.

Big Data

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop

Snowflake

APRIL 14, 2025

Manufacturing operations are seeing improved demand forecasting and maintenance scheduling, with 79% of users reporting substantial benefits. The challenge of unstructured data is particularly pressing. Organizations struggle with time-consuming data management (55%), quality issues (52%) and data sensitivity concerns (50%).

Unstructured Data