ProjectPro

JUNE 6, 2025

Let us compare traditional data warehousing and Hadoop-based BI solutions to better understand how using BI on Hadoop proves more effective than traditional data warehousing- Point Of Comparison Traditional Data Warehousing BI On Hadoop Solutions Data Storage Structured data in relational databases.

Hadoop

ProjectPro

JUNE 6, 2025

He suggests one should start by understanding the crucial distinction between structured and unstructured data—it's the cornerstone. For those venturing into data engineering, structured data is your launchpad. Consider this advice as your compass through the diverse roles in data science.

Data Science

ProjectPro

JUNE 6, 2025

In the big data industry, Hadoop has emerged as a popular framework for processing and analyzing large datasets, with its ability to handle massive amounts of structured and unstructured data. With Hadoop and Pig platform one can achieve next-level extraction and interpretation of such complex unstructured data.

Hadoop

ProjectPro

JUNE 6, 2025

Instead, you can concentrate on using GoogleSQL to discover insightful data while taking advantage of BigQuery's flexible pricing models, including on-demand and flat-rate options. BigQuery has native integrations to streaming products like Dataflow and the ability to ingest streaming data and make it instantly accessible for querying.

ETL Tools

Edureka

MARCH 26, 2025

Finance and Banking: Fraud Detection: Neural networks detect anomalies in transactions to identify potential fraud in real time. Algorithmic Trading: Predicting stock trends using historical data for automated trading strategies. Data Preprocessing: Tools for cleaning, normalizing, and augmenting data to ensure accuracy and relevance.

Raw Data

ProjectPro

JUNE 6, 2025

Here are a few examples of government databases and open data initiatives- The World Bank Open Data One of the largest sources of development data is the World Bank Open Data website, which spans a wide range of topics like infrastructure, poverty, education, and health.

Datasets

ProjectPro

JUNE 6, 2025

The banking sector is at the forefront of Generative AI(Gen AI) advancements, poised to revolutionize operations and address challenges like high costs and legacy technology. Large legacy organizations, especially in the banking sector, often face data silos, making it difficult to integrate information from disparate systems.

Banking

Scott Logic

JULY 23, 2025

The API promised to: Capture and archive user interactions with Copilot across Microsoft 365 apps Enable compliance, monitoring, and auditing of AI usage Export structured data including timestamps, user prompts, and AI responses This sounded perfect - exactly what I needed for my analysis. A proper, official solution.

Metadata

ProjectPro

JUNE 6, 2025

Topic modelling finds applications in organization of large blocks of textual data, information retrieval from unstructured data and for data clustering. For e-commerce websites, data scientists often use topic modelling to group customer reviews and identify common issues faced by consumers. value_counts().plot(kind='bar',

Data Analytics

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

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

Knowledge Hut

MAY 31, 2023

Loan Eligibility Prediction Language: Python Data set: CSV file Source code: Loan_Status_Prediction The goal of loan eligibility prediction using AI is to forecast the likelihood of loan approval for new applicants by analysing historical data on borrowers and their loan applications.

Project

ProjectPro

JULY 13, 2015

Large commercial banks like JPMorgan have millions of customers but can now operate effectively-thanks to big data analytics leveraged on increasing number of unstructured and structured data sets using the open source framework - Hadoop. JP Morgan has massive amounts of data on what its customers spend and earn.

Hadoop

Knowledge Hut

NOVEMBER 3, 2023

MongoDB is a NoSQL database that’s been making rounds in the data science community. MongoDB’s unique architecture and features have secured it a place uniquely in data scientists’ toolboxes globally. Let us see where MongoDB for Data Science can help you.

MongoDB

ThoughtSpot

SEPTEMBER 26, 2024

We are fortunate to have an enviable customer base that’s growing rapidly; among our 1000+ customers, we count three of the Fortune 5 , six of the top ten healthcare and life sciences companies, six of the top ten technology companies, and three of the top five banks as our customers.

Electronics

Wayne Yaddow

MARCH 14, 2025

In the case of a transformation step failure or the generation of unexpected results, dbt Core offers comprehensive logging and failure reports, averting the dissemination of erroneous data throughout the pipeline. The following categories of transformations pose significant limitations for dbt Cloud and dbtCore : 1.

Unstructured Data

ProjectPro

AUGUST 6, 2015

Companies like Electronic Arts, Riot Games are using big data for keeping a track of game play which helps predict performance of the play by analysing 4TB of operational logs and 500GB of structured data. Sports brands like ESPN have also got on to the big data bandwagon.

Big Data

ProjectPro

OCTOBER 28, 2015

Hadoop Sqoop and Hadoop Flume are the two tools in Hadoop which is used to gather data from different sources and load them into HDFS. Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc., The complexity of the big data system increases with each data source.

ETL Tools

ProjectPro

MARCH 15, 2016

One of the primary use cases for Hadoop, was in risk modelling, to solve the question for banks to evaluate customers and markets better than legacy systems. These days we notice that many banks compile separate data warehouses into a single repository backed by Hadoop for quick and easy analysis.

Hadoop

Knowledge Hut

JULY 24, 2023

Disruptive database technologies include, for instance: Blockchain Blockchain is a distributed ledger technology that enables data to be safely stored across several nodes in a decentralized (or sometimes in a centralized) manner. In-memory Databases For applications that demand real-time data processing, in-memory databases are created.

Database

Knowledge Hut

APRIL 23, 2024

Example of Data Velocity An instance of data velocity within the four Vs of big data is the real-time monitoring and analysis of stock market data. Example of Data Variety An instance of data variety within the four Vs of big data is exemplified by customer data in the retail industry.

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

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

Cloudera

SEPTEMBER 6, 2018

It supports a variety of storage engines that can handle raw files, structured data (tables), and unstructured data. It also supports a number of frameworks that can process data in parallel, in batch or in streams, in a variety of languages. Cloudera Enterprise.

Banking

ProjectPro

JANUARY 31, 2023

Big data enables businesses to get valuable insights into their products or services. Almost every company employs data models and big data technologies to improve its techniques and marketing campaigns. Most leading companies use big data analytical tools to enhance business decisions and increase revenues.

Big Data

ProjectPro

NOVEMBER 14, 2015

These projects are proof of how far Apache Hadoop and Apache Spark have come and how they are making big data analysis a profitable enterprise. With Big Data came a need for programming languages and platforms that could provide fast computing and processing capabilities.

Hadoop

Monte Carlo

JUNE 28, 2023

These are the world of data and the data warehouse that is focused on using structured data to answer questions about the past and the world of AI that needs more unstructured data to train models to predict the future.

Data Warehouse

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

SEPTEMBER 11, 2015

Learning Hadoop will ensure that you can build a secure career in Big Data. Big Data is not going to go away. There will always be a place for RDBMS, ETL, EDW and BI for structured data. But at the pace and nature at which big data is growing, technologies like Hadoop will be very necessary to tackle this data.

Hadoop

ProjectPro

JUNE 6, 2025

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.

Hadoop

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.

Hadoop

Edureka

DECEMBER 21, 2023

Content Focus : Bing primarily focuses on web searches and structured data. Unlike some AI counterparts, Gemini thrives on structured and unstructured data, wielding knowledge from scientific articles to code repositories. Pros: Knowledge Bank: Got a question? Bard’s got an answer!

Education

ProjectPro

MARCH 14, 2014

Get More Practice, More Big Data and Analytics Projects , and More guidance. Fast-Track Your Career Transition with ProjectPro Big data is changing the manner in which sellers, buyers, real-estate professionals and as well banks think about different transactions related to property.

Hadoop

Knowledge Hut

JANUARY 11, 2024

It uses complex machine learning algorithms to build meaningful and structured data. Data Science can be described as a domain that applies advanced analytics, statistics and scientific principle for extracting valuable information and deriving valuable conclusions from structured or unstructured data.

Finance

ProjectPro

MAY 31, 2021

The three primary types of big data are: Structured Data: Structured data refers to the data that can be analyzed, retrieved, and stored in a fixed format. Machines and humans are both sources of structured data. Semi-structured Data: It is a combination of structured and unstructured data.

Big Data

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. Learn how big data transform banking, law, hospitality, fashion, and science.

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