Knowledge Hut

DECEMBER 27, 2023

Throughout the 20th century, volumes of data kept growing at an unexpected speed and machines started storing information magnetically and in other ways. Accessing and storing huge data volumes for analytics was going on for a long time. What is Big Data? Types of Big Data 1. Then computers started doing the same.

Big Data Tools 59

Big Data Tools Big Data Hadoop Database-centric 59

Knowledge Hut

SEPTEMBER 6, 2023

Over the past decade, the IT world transformed with a data revolution. Back when I studied Computer Science in the early 2000s, databases like MS Access and Oracle ruled. The rise of big data and NoSQL changed the game. Systems evolved from simple to complex, and we had to split how we find data from where we store it.

Database 52

Database Certification MongoDB MySQL 52

ProjectPro

JULY 27, 2021

Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language). SQL works on data arranged in a predefined schema. Non-relational databases support dynamic schema for unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

JULY 3, 2015

. “SAP systems hold vast amounts of valuable business data -- and there is a need to enrich this, bring context to it, using the kinds of data that is being stored in Hadoop. Hadoop supports huge volumes of unstructured data such as data generated from sensors, Facebook updates, Twitter Feeds, etc.

Hadoop 40

Hadoop Big Data Data Solutions Unstructured Data 40

ProjectPro

DECEMBER 10, 2021

Azure Cloud Services are hosted on virtual machines, and they let one have a higher degree of control over the VMs by allowing software installation on the VMs and also making them remotely accessible. A user only pays for the resources he needs from a cloud service provider and accesses them over a secure Internet connection.

BI 52

BI Cloud Computing SQL Database 52

ProjectPro

AUGUST 11, 2021

The data warehouse layer consists of the relational database management system (RDBMS) that contains the cleaned data and the metadata, which is data about the data. The RDBMS can either be directly accessed from the data warehouse layer or stored in data marts designed for specific enterprise departments.

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

ProjectPro

OCTOBER 28, 2015

Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc., and Flume in Hadoop is used to sources data which is stored in various sources like and deals mostly with unstructured data. The complexity of the big data system increases with each data source.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

ProjectPro

JUNE 4, 2015

HDFS in Hadoop architecture provides high throughput access to application data and Hadoop MapReduce provides YARN based parallel processing of large data sets. The basic principle of working behind Apache Hadoop is to break up unstructured data and distribute it into many parts for concurrent data analysis.

Hadoop 52

Hadoop Architecture IT Java 52

ProjectPro

JUNE 2, 2015

We have to find the right correlation patterns for all our forward memories and incoming data to predict upcoming malfunctions and their consequences."- Big data is unusable without structure and companies might take years to comprehend the data, and yet might not be able to yield useful insights.

Big Data 40

Big Data Hadoop Banking Retail 40

ProjectPro

MARCH 16, 2015

Thus, the computing technology and infrastructure must be able to render a cost efficient implementation of: Parallel Data Processing that is unconstrained. Provide storage for billions and trillions of unstructured data sets. The upswing for big data in healthcare industry is due to the falling cost of storage.

Healthcare 40

Healthcare Big Data Project Hospitality 40

AltexSoft

JUNE 26, 2023

Commonly, the entire flow is fully automated and consists of three main steps — data extraction, transformation, and loading ( ETL or ELT , for short, depending on the order of the operations.) Dive deeper into the subject by reading our article Data Integration: Approaches, Techniques, Tools, and Best Practices for Implementation.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

ProjectPro

MAY 23, 2015

With more than 245 million customers visiting 10,900 stores and with 10 active websites across the globe, Walmart is definitely a name to reckon with in the retail sector. petabytes of unstructured data from 1 million customers every hour. We want to know who every person in the world is.

Big Data 52

Big Data Data Analysis Hadoop Retail 52

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 Java 40

AltexSoft

JULY 29, 2022

A master node called NameNode maintains metadata with critical information, controls user access to the data blocks, makes decisions on replications, and manages slaves. As a result, today we have a huge ecosystem of interoperable instruments addressing various challenges of Big Data. How data engineering works under the hood.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

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 59

Unstructured Data NoSQL Hadoop Data Lake 59

AltexSoft

NOVEMBER 23, 2021

Not to mention that additional sources are constantly being added through new initiatives like big data analytics , cloud-first, and legacy app modernization. To break data silos and speed up access to all enterprise information, organizations can opt for an advanced data integration technique known as data virtualization.

Process 69

Process Data Lake Metadata Data Warehouse 69