Databand.ai

JULY 19, 2023

Data can come from many different sources, and in many different formats—from structured databases to unstructured documents. These sources might include external data like social media feeds, internal data like logs or reports, or even real-time data feeds from IoT (Internet of Things) devices.

Data Ingestion 52

Data Ingestion Process Data Cleanse Data Governance 52

Databand.ai

JULY 19, 2023

In this phase, data is collected from various sources. These sources could be databases, CRM systems, social media platforms, or any other place where data is stored. The extracted data is often raw and unstructured and may come in various formats such as text, images, audio, or video.

Data Cleanse 52

Data Cleanse Raw Data Data Storage Data Warehouse 52

Knowledge Hut

APRIL 19, 2023

The process of gathering and compiling data from various sources is known as data Aggregation. Businesses and groups gather enormous amounts of data from a variety of sources, including social media, customer databases, transactional systems, and many more. This can be done manually or with a data cleansing tool.

Process 59

Process Data Mining Aggregated Data Portfolio 59

AltexSoft

MAY 14, 2021

To understand Big Data, you need to get acquainted with its attributes known as the four V’s: Volume is what hides in the “big” part of Big Data. This relates to terabytes to petabytes of information coming from a range of sources such as IoT devices, social media, text files, business transactions, etc. Data cleansing.

Big Data 78

Big Data Data Analytics IT NoSQL 78

Edureka

APRIL 17, 2023

Social Media Sentiment Analysis: Social media sentiment analysis involves analyzing social media data to determine the sentiment or opinion of users about a particular product or service. For this project, you can start with a messy dataset and use tools like Excel, Python, or OpenRefine to clean and pre-process the data.

Portfolio 40

Portfolio Data Analytics Project Data Mining 40

Knowledge Hut

JANUARY 18, 2024

Picture this: every day, we generate a mind-boggling amount of data. From social media posts and online transactions to sensor readings and healthcare records, data is the fuel that powers modern businesses and organizations.

Data Analytics 52

Data Analytics Healthcare Machine Learning Algorithm 52

U-Next

AUGUST 11, 2022

The first step is capturing data, extracting it periodically, and adding it to the pipeline. The next step includes several activities: database management, data processing, data cleansing, database staging, and database architecture. Consequently, data processing is a fundamental part of any Data Science project.

Data Science 40

Data Science Data Cleanse Unstructured Data Machine Learning 40

Edureka

FEBRUARY 8, 2024

3D Rendering and Media Processing- High-performance computing is crucial for rendering graphics and processing media files. Batch Processing- C-Series instances excel in scenarios that involve batch processing, where large amounts of data need to be processed in parallel.

AWS 52

AWS NoSQL Deep Learning Datasets 52

U-Next

SEPTEMBER 13, 2022

Data Science is an interdisciplinary field that consists of numerous scientific methods, tools, algorithms, and Machine Learning approaches that attempt to identify patterns in the provided raw input data and derive practical insights from it. . The first step is to compile the pertinent data and business requirements.

Machine Learning 40

Machine Learning Data Science Deep Learning Algorithm 40

Knowledge Hut

OCTOBER 13, 2023

Data cleaning involves removing all the unwanted data from the data set and keeping only the data that is relevant to your analysis. Remove duplicate data to avoid misrepresentation of the analysis Eliminate irrelevant data columns or rows Fix structural errors like inconsistent data formats, data types, etc.

BI 52

BI Business Analyst Datasets Raw Data 52

AltexSoft

AUGUST 29, 2023

Unlike traditional DWs, cloud data warehouses like Snowflake, BigQuery, and Redshift come pre-equipped with advanced features; learn more about the differences in our dedicated article. Unlike data warehouses, data lakes allow a schema-on-read approach, enabling greater flexibility in data storage.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

ProjectPro

AUGUST 24, 2021

This project is an opportunity for data enthusiasts to engage in the information produced and used by the New York City government. Generally, Twitter sentiment is analyzed in most big data projects using parsing. Finally, this data is used to create KPIs and visualize them using Tableau.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Rockset

SEPTEMBER 6, 2019

I could be describing either the VR space or Data Engineering, but in fact this post is about the intersection of both. Virtual Reality – The Next Frontier in Media I work as a Data Engineer at a leading company in the VR space, with a mission to capture and transmit reality in perfect fidelity.

Metadata 40

Metadata Kafka Data Cleanse SQL 40

ProjectPro

OCTOBER 20, 2021

This is used in social media to better gauge sentiments towards an event or a product. Data Volumes and Veracity Data volume and quality decide how fast the AI System is ready to scale. The larger the set of predictions and usage, the larger is the implications of Data in the workflow.

Machine Learning 52

Machine Learning Algorithm Government Data Science 52