The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Data Lake Management Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Data Lake Management Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. 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 113

IT Unstructured Data Data Architecture Government 113

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.

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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 57

Data Lake Data Warehouse Hadoop Raw Data 57

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. . This led them to fall behind.

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Unstructured Data Data Warehouse Pharmaceutical MySQL 83

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. Data Analyst Scientist.

Data Science 97

Data Science BI Business Intelligence Data Mining 97

AltexSoft

AUGUST 29, 2023

It is a versatile platform for exploring, refining, and analyzing petabytes of information that continually flow in from various data sources. Who needs a data lake? If the intricacies of big data are becoming too much for your existing systems to handle, a data lake might be the solution you’re seeking.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Data Engineering Weekly

JUNE 4, 2023

[link] Uber: Spark Analysers: Catching Anti-Patterns In Spark Apps One of the challenges in commoditizing data processing engines like Spark is that it requires an expert user to understand and operate this system. Many of the real-world data, all the way from medical images to astro monitoring, are unstructured data.

Data Engineering 52

Data Engineering Data Engineer Engineering Medical 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.

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Deep Learning Machine Learning Unstructured Data Algorithm 52

AltexSoft

OCTOBER 25, 2021

NLP-powered systems can derive meaning from what’s said or written, with all the complexities and nuances of natural narrative text. This allows machines to extract value even from unstructured data. Healthcare organizations generate a lot of text data. Unstructured data is unavoidable, yet extremely valuable.

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Medical Healthcare Process Hospitality 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. Driving innovation with secure and governed data .

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Data Lake Government Data Security Unstructured Data 98

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. What is a Data Lake? Want to learn more about data governance?

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Data Lake Process Metadata Data Warehouse 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? Take the infamous Mars Climate Orbiter crash in 1999.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Ascend.io

NOVEMBER 21, 2023

ELT (Extract, Load, Transform) is a data integration technique that collects raw data from multiple sources and directly loads it into the target system, typically a cloud data warehouse. Extract The initial stage of the ELT process is the extraction of data from various source systems.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

ProjectPro

SEPTEMBER 9, 2021

The datasets are usually present in Hadoop Distributed File Systems and other databases integrated with the platform. Hive is built on top of Hadoop and provides the measures to read, write, and manage the data. Spark SQL, for instance, enables structured data processing with SQL.

Hadoop 52

Hadoop Big Data Tools Java SQL 52

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

ProjectPro

FEBRUARY 16, 2023

AI can help BI tools provide clear, actionable insights from the study data. An AI-powered system can explain the significance of each data point at the granular level and help human operators understand how that data may be translated into real-world business decisions. PREVIOUS NEXT <

Business Intelligence 52

Business Intelligence BI Data Mining Raw Data 52

Silectis

JANUARY 3, 2021

.” Artificial Intelligence AI is a broad term used to describe engineered systems that have been taught to do a task that typically requires human intelligence. BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

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

ProjectPro

DECEMBER 7, 2021

Data Pipeline Tools AWS Data Pipeline Azure Data Pipeline Airflow Data Pipeline Learn to Create a Data Pipeline FAQs on Data Pipeline What is a Data Pipeline? In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 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

Ascend.io

AUGUST 31, 2023

Modern Cloud Data Platforms The native capabilities of the cloud providers have been joined by third-party services to offload that data into separate less costly systems that are optimized for analysis of that data. Transform before Load: The ETL process conforms data to the static, predefined warehouse schema.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

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. Paving the way for creating intelligent systems.

Data Science 52

Data Science Machine Learning Datasets Algorithm 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

ProjectPro

MARCH 17, 2021

Basically, traditional machine learning requires you to manually select features from the data and train the model to recognize patterns in data and make predictions on the new data that arrives within the machine learning system. Now, what if you add the images of bananas into your data? What is Deep Learning?

Deep Learning 52

Deep Learning Machine Learning Algorithm Datasets 52

ProjectPro

JULY 22, 2016

The desire to save every bit and byte of data for future use, to make data-driven decisions is the key to staying ahead in the competitive world of business operations. All this is possible due to the low cost storage systems like Hadoop and Amazon S3.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

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

SEPTEMBER 30, 2014

That laid the foundation for an entirely new domain of ETL (an acronym for Extract Transform Load) – a field that continues to dominate data warehousing to this date. The modern technological ecosystem is run and managed by interconnected systems that can read, copy, aggregate, transform and re–load data from one another.

Hadoop 52

Hadoop Unstructured Data ETL Tools ETL System 52

ProjectPro

NOVEMBER 17, 2021

Consistent criteria: A centralized sentiment analysis system can improve accuracy and deliver better insights since tagging text by sentiment is highly subjective, influenced by personal experiences, thoughts, and beliefs. Access the Sentiment Analysis Project on Movie Reviews with Source Code 3.

Coding 52

Coding Project Entertainment Datasets 52

ProjectPro

OCTOBER 18, 2021

Recommender System Projects Have you ever seen movies or web series on online streaming platforms? They have a well-researched collection of data such as ratings, reviews, timestamps, price, category information, customer likes, and dislikes. It contains all the attributes you need to build your stock price prediction system.

Machine Learning 52

Machine Learning Project Datasets Algorithm 52

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 52

Hadoop Big Data Data Analytics Banking 52

ProjectPro

JUNE 6, 2015

It also detects any problems that might be there in the data – so there is some data quality layer. There is Master Function Data and Meta Function Data. Then there is the Access layer to bring the reports to the end users. In the data transformation we saw lot of limitation with this kind of BI architecture.

Business Intelligence 40

Business Intelligence Big Data BI Hadoop 40

ProjectPro

SEPTEMBER 14, 2016

Concisely, a hadoop developer plays with the data, transforms it, decodes it and ensure that it is not destroyed. Most of the hadoop developers receive unstructured data through flume or structured data through RDBMS and perform data cleaning using various tools in the hadoop ecosystem.

Hadoop 40

Hadoop Unstructured Data Java Big Data 40

ProjectPro

SEPTEMBER 26, 2014

The toughest challenges in business intelligence today can be addressed by Hadoop through multi-structured data and advanced big data analytics. Big data technologies like Hadoop have become a complement to various conventional BI products and services. Big data, multi-structured data, and advanced analytics.

Business Intelligence 40

Business Intelligence Hadoop BI Relational Database 40

ProjectPro

AUGUST 6, 2015

Let’s take a look at how Amazon uses Big Data- Amazon has approximately 1 million hadoop clusters to support their risk management, affiliate network, website updates, machine learning systems and more. Sports brands like ESPN have also got on to the big data bandwagon. ” Interesting?

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Big Data Hadoop Insurance Media 52