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?

Big Data 52

Big Data Relational Database Data Datasets 52

Knowledge Hut

DECEMBER 21, 2023

In the present-day world, almost all industries are generating humongous amounts of data, which are highly crucial for the future decisions that an organization has to make. This massive amount of data is referred to as “big data,” which comprises large amounts of data, including structured and unstructured data that has to be processed.

Hadoop 52

Hadoop Big Data NoSQL Unstructured Data 52

Knowledge Hut

JANUARY 30, 2024

Goal To extract and transform data from its raw form into a structured format for analysis. To uncover hidden knowledge and meaningful patterns in data for decision-making. Data Source Typically starts with unprocessed or poorly structured data sources. Analyzing and deriving valuable insights from data.

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

U-Next

MARCH 1, 2023

They are also responsible for ensuring that the data is clean and organized, as well as making sure that it’s easily accessible to other departments within the company. They often work closely with database administrators to ensure they have access to all of the tools and resources needed to meet their goals.

Data Engineering 75

Data Engineering Data Engineer Non-relational Database Engineering 75

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 52

MongoDB Data Science NoSQL ETL Tools 52

Precisely

MARCH 9, 2023

We will also address some of the key distinctions between platforms like Hadoop and Snowflake, which have emerged as valuable tools in the quest to process and analyze ever larger volumes of structured, semi-structured, and unstructured data. They may want to look at those numbers on a daily or weekly basis.

Data Lake 59

Data Lake Data Warehouse Hadoop Raw Data 59

The Modern Data Company

MAY 10, 2023

To choose the most suitable data management solution for your organization, consider the following factors: Data types and formats: Do you primarily work with structured, unstructured, or semi-structured data? Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Data Lake Management Data Warehouse 52

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 109

IT Unstructured Data Data Architecture Government 109

The Modern Data Company

MAY 10, 2023

To choose the most suitable data management solution for your organization, consider the following factors: Data types and formats: Do you primarily work with structured, unstructured, or semi-structured data? Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

To choose the most suitable data management solution for your organization, consider the following factors: Data types and formats: Do you primarily work with structured, unstructured, or semi-structured data? Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

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 52

Data Lake Architecture IT Amazon Web Services 52

Cloudera

SEPTEMBER 17, 2020

By enabling their event analysts to monitor and analyze events in real time, as well as directly in their data visualization tool, and also rate and give feedback to the system interactively, they increased their data to insight productivity by a factor of 10. . Our solution: Cloudera Data Visualization.

Unstructured Data 81

Unstructured Data Data Warehouse Pharmaceutical MySQL 81

Data Engineering Weekly

JUNE 4, 2023

Our latest report highlights the impact of bad data on your bottom line (did you know that poor data quality impacts 31% of revenue?!) Access the Report Kaushik Muniandi: Text-Based Search - From Elastic Search to Vector Search Last month or so, I experimented with vector search with embedding.

Data Engineering 52

Data Engineering Data Engineer Engineering Medical 52

Cloudera

DECEMBER 11, 2020

As mentioned in my previous blog on the topic , the recent shift to remote working has seen an increase in conversations around how data is managed. Toolsets and strategies have had to shift to ensure controlled access to data. It established a data governance framework within its enterprise data lake.

Data Lake 98

Data Lake Government Data Security Unstructured Data 98

Cloudera

OCTOBER 28, 2021

Commercial Lines insurers generally do not analyze the full range of their data, nor do they always have the right processes and technology in place to enable the collection, storage, and analysis to operationalize the data. In the last few years, Commercial Insurers have been making great strides in expanding the use of their data.

Insurance 95

Insurance Transportation Unstructured Data Manufacturing 95

Grouparoo

JANUARY 11, 2022

When it comes to storing large volumes of data, a simple database will be impractical due to the processing and throughput inefficiencies that emerge when managing and accessing big data. This article looks at the options available for storing and processing big data, which is too large for conventional databases to handle.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Knowledge Hut

JULY 28, 2023

Data Types and Dimensionality ML algorithms work well with structured and tabular data, where the number of features is relatively small. DL models excel at handling unstructured data such as images, audio, and text, where the data has a large number of features or high dimensionality.

Deep Learning 52

Deep Learning Machine Learning Unstructured Data Algorithm 52

Knowledge Hut

DECEMBER 26, 2023

According to the Cybercrime Magazine, the global data storage is projected to be 200+ zettabytes (1 zettabyte = 10 12 gigabytes) by 2025, including the data stored on the cloud, personal devices, and public and private IT infrastructures. They clean, cumulate, connect and structure data for analysis-based applications.

Data Science 97

Data Science BI Business Intelligence Data Mining 97

AltexSoft

OCTOBER 25, 2021

This allows machines to extract value even from unstructured data. Healthcare organizations generate a lot of text data. Some of it is structured , or organized into specific fields of an EHR. Unstructured data is unavoidable, yet extremely valuable. The many healthcare factors hidden in unstructured data.

Medical 52

Medical Healthcare Process Hospitality 52

Monte Carlo

APRIL 24, 2023

Amazon S3 and/or Lake Formation Amazon S3 is a popular storage platform to build and store data lakes thanks to its high availability and low latency access. S3 has seamless integration with the AWS ecosystem including AWS Glue for data cataloging, Amazon Athena for ad-hoc querying, and Amazon Redshift for data warehousing.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Monte Carlo

AUGUST 31, 2023

Your downstream data consumers including product analysts, marketing leaders, and sales teams rely on data-driven tools like CRMs, CXPs, CMSs, and any other acronym under the sun to do their jobs quickly and effectively. But what happens when the data is wrong?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

phData: Data Engineering

SEPTEMBER 19, 2023

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. Want to learn more about data governance?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

ProjectPro

SEPTEMBER 9, 2021

Spark SQL, for instance, enables structured data processing with SQL. Hive uses HQL, while Spark uses SQL as the language for querying the data. Access rights is another difference between the two tools with Hive offering access rights and grouping the users as per their roles.

Hadoop 52

Hadoop Big Data Tools Java SQL 52

ProjectPro

FEBRUARY 16, 2023

Focus Historical data analysis, reporting, and visualization. Input Data Structured data from various sources, such as databases, spreadsheets, and ERP systems. Structured, semi-structured, and unstructured data from multiple sources, such as social media, IoT devices, and sensors.

Business Intelligence 52

Business Intelligence BI Data Mining Raw Data 52

Cloudera

JUNE 25, 2021

Using easy-to-define policies, Replication Manager solves one of the biggest barriers for the customers in their cloud adoption journey by allowing them to move both tables/structured data and files/unstructured data to the CDP cloud of their choice easily. Specification of access conditions for specific users and groups.

Cloud 69

Cloud Data Lake Cloud Storage Metadata 69

Ascend.io

NOVEMBER 21, 2023

Extract The initial stage of the ELT process is the extraction of data from various source systems. This phase involves collecting raw data from the sources, which can range from structured data in SQL or NoSQL servers, CRM and ERP systems, to unstructured data from text files, emails, and web pages.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Silectis

JANUARY 3, 2021

BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions. Big Data Large volumes of structured or unstructured data. Data Visualization Graphic representation of a set or sets of data. Database A collection of structured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Knowledge Hut

JANUARY 19, 2024

With businesses relying heavily on data, the demand for skilled data scientists has skyrocketed. In data science, we use various tools, processes, and algorithms to extract insights from structured and unstructured data. Importance: Efficient organization and retrieval of data.

Data Science 52

Data Science Machine Learning Datasets Algorithm 52

Knowledge Hut

JULY 26, 2023

These datasets typically involve high volume, velocity, variety, and veracity, which are often referred to as the 4 v's of Big Data: Volume: Volume refers to the vast amount of data generated and collected from various sources. Managing and analyzing such large volumes of data requires specialized tools and technologies.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

ProjectPro

DECEMBER 7, 2021

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. What is a Big Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Rockset

JULY 29, 2022

This consistency promotes data integrity, so you can trust the insights to make informed decisions. Additionally, data warehouses are great at offering historical intelligence. However, organizations today are moving beyond just batch analytics on historical data. So, it’s not real-time data. Pricing starts at $0.25

Data Analytics 52

Data Analytics Data Warehouse Datasets Google Cloud 52

Knowledge Hut

MAY 31, 2023

Companies utilize different approaches to deal with data in order to extract information from structured, semi-structured, or unstructured data sets. Business Intelligence is one such approach that helps professionals to extract valuable information from structured data.

Business Intelligence 52

Business Intelligence BI Business Analyst Consulting 52

Ascend.io

AUGUST 31, 2023

For example, unlike traditional platforms with set schemas, data lakes adapt to frequently changing data structures at points where the data is loaded , accessed, and used. This is leading many enterprises to consolidate their data operations onto the newest generation of cloud-based data warehouses.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

ProjectPro

OCTOBER 15, 2014

Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization Hadoop technology is the buzz word these days but most of the IT professionals still are not aware of the key components that comprise the Hadoop Ecosystem. Pig is SQL like but varies to a great extent.

Hadoop 52

Hadoop Unstructured Data Java SQL 52

ProjectPro

JULY 22, 2016

Data warehouses do a good job for what they are meant to do, but with disparate data sources and different data types like transaction logs, social media data, tweets, user reviews, and clickstream data –Data Lakes fulfil a critical need. Data Warehouses do not retain all data whereas Data Lakes do.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

ProjectPro

NOVEMBER 17, 2021

Access the Sentiment Analysis Project on Product Reviews with Source Code Get FREE Access to Machine Learning Example Codes for Data Cleaning, Data Munging, and Data Visualization 2. Access the Sentiment Analysis Project on Movie Reviews with Source Code 3.

Coding 52

Coding Project Entertainment Datasets 52

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 98

Data Science Data Mining Deep Learning Programming Language 98