Knowledge Hut

DECEMBER 26, 2023

Businesses benefit at large with these data collection and analysis as they allow organizations to make predictions and give insights about products so that they can make informed decisions, backed by inferences from existing data, which, in turn, helps in huge profit returns to such businesses.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

Knowledge Hut

MARCH 27, 2024

A company can make the right decision by organizing a massive amount of raw data with the right data analytic tool and a professional data analyst. You can check out the Big Data Certification Online to have an in-depth idea about big data tools and technologies to prepare for a job in the domain.

Big Data 52

Big Data Data Analytics MongoDB Big Data Tools 52

Edureka

JANUARY 18, 2024

Introduction of R as an optional language in data science, highlighting its strengths in statistics and visualization. Data Manipulation Examine the most important data manipulation libraries like explore Pandas for structured data manipulation and Numpy for numerical operations in Python.

Data Science 40

Data Science Deep Learning NoSQL Machine Learning 40

Knowledge Hut

OCTOBER 24, 2023

Critical Thinking Because they must match the client's expectations with their analysis of the data, business analysts need to be able to think critically. Business analysts must therefore put business requirements and data analysis in order of importance, considering several factors.

Business Analyst 98

Business Analyst Business Intelligence SQL Programming Language 98

AltexSoft

JUNE 26, 2023

Data collection revolves around gathering raw data from various sources, with the objective of using it for analysis and decision-making. It includes manual data entries, online surveys, extracting information from documents and databases, capturing signals from sensors, and more.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

ProjectPro

AUGUST 11, 2021

Data Lake vs Data Warehouse - The Differences Before we closely analyse some of the key differences between a data lake and a data warehouse, it is important to have an in depth understanding of what a data warehouse and data lake is. Data Lake vs Data Warehouse - The Introduction What is a Data warehouse?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

AltexSoft

MAY 14, 2021

A single car connected to the Internet with a telematics device plugged in generates and transmits 25 gigabytes of data hourly at a near-constant velocity. And most of this data has to be handled in real-time or near real-time. Variety is the vector showing the diversity of Big Data. Data analysis. NoSQL databases.

Big Data 78

Big Data Data Analytics IT NoSQL 78

AltexSoft

NOVEMBER 10, 2021

A data warehouse (DW) is a centralized repository for data accumulated from an array of corporate sources like CRMs, relational databases , flat files, etc. Typically used for data analysis and reporting, data warehouses rely on ETL mechanisms to extract , transform , and load data into a destination.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

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 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

JANUARY 31, 2023

Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data. Big data enables businesses to gain a deeper understanding of their industry and helps them extract valuable information from the unstructured and raw data that is regularly collected.

Big Data 52

Big Data Hadoop AWS Relational Database 52

AltexSoft

MAY 12, 2023

This article delves into the realm of unstructured data, highlighting its importance, and providing practical guidance on extracting valuable insights from this often-overlooked resource. We will discuss the different data types, storage and management options, and various techniques and tools for unstructured data analysis.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

ProjectPro

JULY 27, 2021

Data Engineer Interview Questions on Big Data Any organization that relies on data must perform big data engineering to stand out from the crowd. But data collection, storage, and large-scale data processing are only the first steps in the complex process of big data analysis.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

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 40

Hadoop Big Data Metadata Unstructured Data 40

AltexSoft

MARCH 14, 2023

This promotes data literacy and allows more individuals to make data-driven decisions. It also eliminates the bottleneck of having only a few individuals with expertise in data analysis and encourages a more collaborative and inclusive culture around data within the organization.

IT 59

IT Data Warehouse Data Governance Data Lake 59