Knowledge Hut

JANUARY 30, 2024

In today's world, where data rules the roost, data extraction is the key to unlocking its hidden treasures. As someone deeply immersed in the world of data science, I know that raw data is the lifeblood of innovation, decision-making, and business progress. What is data extraction?

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

Knowledge Hut

DECEMBER 26, 2023

Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. This is one of the major reasons behind the popularity of data science. What is the role of a Data Engineer?

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

ProjectPro

FEBRUARY 16, 2023

Business Intelligence and Artificial Intelligence are popular technologies that help organizations turn raw data into actionable insights. While both BI and AI provide data-driven insights, they differ in how they help businesses gain a competitive edge in the data-driven marketplace.

Business Intelligence 52

Business Intelligence BI Data Mining Raw Data 52

Knowledge Hut

APRIL 25, 2024

One of the industries with the quickest growth rates is big data. It refers to gathering and processing sizable amounts of data to produce insights that may be used by an organization to improve its various facets. What Are the Different Features of Big Data Analytics? The main features of big data analytics are: 1.

Big Data 52

Big Data Data Analytics Manufacturing Retail 52

Ascend.io

DECEMBER 14, 2022

How ETL Became Outdated The ETL process (extract, transform, and load) is a data consolidation technique in which data is extracted from one source, transformed, and then loaded into a target destination. In the 1980s, companies started to amass big amounts of transactional data. This causes two issues.

Data Integration 52

Data Integration Raw Data Data Consolidation 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. Companies need more than definitions.

Data Management 52

Data Management Data Lake Management Data Warehouse 52

Monte Carlo

FEBRUARY 15, 2023

Since the inception of the cloud, there has been a massive push to store any and all data. On the surface, the promise of scaling storage and processing is readily available for databases hosted on AWS RDS, GCP cloud SQL and Azure to handle these new workloads. Cloud data warehouses solve these problems.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 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. Companies need more than definitions.

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. Companies need more than definitions.

Data Management 52

Data Management Data Lake Management Data Warehouse 52

Grouparoo

NOVEMBER 16, 2021

Breaking silos and collating data into a coherent set of information for processing will yield business benefits. This is where data transformation can come to the rescue. Traditional processes for data transformation follow the principle of Extract, Transform, and Load (ETL). To validate data and improve quality.

Data Mining 52

Data Mining Raw Data ETL Tools Unstructured Data 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

Cloudera

SEPTEMBER 15, 2022

Mark: While most discussions of modern data platforms focus on comparing the key components, it is important to understand how they all fit together. The collection of source data shown on your left is composed of both structured and unstructured data from the organization’s internal and external sources.

Data Lake 81

Data Lake Analytics Application Cloud Storage Architecture 81

Meltano

OCTOBER 5, 2022

Data is becoming the world’s most valuable resource, according to an article in The Economist dating back to 2017. Since then, the way we compile, process, and store data has evolved significantly, and it continues to do so at incredible speed. The goal of DataOps is to speed up the process of deriving value from data.

Engineering 52

Engineering Raw Data Data Pipeline ETL Tools 52

Knowledge Hut

JUNE 20, 2023

Factors Data Engineer Machine Learning Definition Data engineers create, maintain, and optimize data infrastructure for data. In addition, they are responsible for developing pipelines that turn raw data into formats that data consumers can use easily.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

ProjectPro

FEBRUARY 8, 2023

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses.

AWS 98

AWS Scala Metadata Data Lake 98

Edureka

JANUARY 18, 2024

For those looking to start learning in 2024, here is a data science roadmap to follow. What is Data Science? Data science is the study of data to extract knowledge and insights from structured and unstructured data using scientific methods, processes, and algorithms.

Data Science 40

Data Science Deep Learning NoSQL Machine Learning 40

ProjectPro

JUNE 9, 2021

As we proceed further into the blog, you will find some statistics on data engineering vs. data science jobs and data engineering vs. data science salary, along with an in-depth comparison between the two roles- data engineer vs. data scientist. vs. What does a Data Engineer do?

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

U-Next

AUGUST 3, 2022

Up until 2010, it was extremely difficult for companies to store data. Now that well-known technologies like Hadoop and others have resolved the storage issue, the emphasis is on information processing. And Data Science has a significant impact here. The people who have inquiries about data are known as Data Scientists.

Retail 52

Retail Data Science Computer Science Entertainment 52

AltexSoft

MAY 12, 2022

Our brains are constantly processing sounds to give us important information about our environment. Particularly, we’ll explain how to obtain audio data, prepare it for analysis, and choose the right ML model to achieve the highest prediction accuracy. Audio data file formats. Audio data transformation basics to know.

Machine Learning 122

Machine Learning Building Datasets Healthcare 122

Monte Carlo

AUGUST 31, 2023

A NASA space probe, the Mars Climate Orbiter crashed as a result of a data entry error that produced outputs in non-SI (International System) units versus SI units, bringing it too close to the planet. Like spacecraft, analytic pipelines can be extremely vulnerable to the most innocent changes at any stage of the process.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

AltexSoft

MARCH 30, 2023

Shell, Adobe, Burberry, Columbia, Bayer — you definitely know the names. The answer is simple: They use the same technology to make the most of data. Along with thousands of other data-driven organizations from different industries, the above-mentioned leaders opted for Databrick to guide strategic business decisions.

Scala 64

Scala Data Lake BI Google Cloud 64

AltexSoft

JULY 29, 2022

Apache Hadoop is an open-source Java-based framework that relies on parallel processing and distributed storage for analyzing massive datasets. Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics. What is Hadoop? But you can configure the block size.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 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

MAY 31, 2021

Before starting any big data project, it is essential to become familiar with the fundamental processes and steps involved, from gathering raw data to creating a machine learning model to its effective implementation. Access Solution to Data Warehouse Design for an E-com Site 4.

Big Data 52

Big Data Coding Project Hadoop 52

ProjectPro

JULY 3, 2015

solutions with in-memory technology store data in the working memory instead of the hard drive making it easier for processing, evaluation and use. SAP is all set to ensure that big data market knows its hip to the trend with its new announcement at a conference in San Francisco that it will embrace Hadoop. .

Hadoop 40

Hadoop Big Data Data Solutions Unstructured Data 40

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Learn How to Build a Data Warehouse for an E-commerce Business. What is a Data lake?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

ProjectPro

OCTOBER 18, 2021

To build such ML projects, you must know different approaches to cleaning raw data. Statistical modeling: Here, the student has to prepare a real-world model of the mathematical representation and the statistics uncertainties within the analyzing and prediction process. Text Processing b. Text Sequencing c.

Machine Learning 52

Machine Learning Project Datasets Algorithm 52

ProjectPro

AUGUST 16, 2021

Machine Learning Projects on Natural Language Processing (NLP) 5. Quite similar to classification is clustering but with the minor difference of working with unlabelled data. Clustering defines the process of grouping together identical objects into individual clusters. Machine Learning Projects on Classification 2.

Machine Learning 40

Machine Learning Coding Project Deep Learning 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.

Hadoop 40

Hadoop Big Data Metadata Java 40