Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. 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

Knowledge Hut

APRIL 25, 2024

Information Technology is a field that manages and processes information for large-scale organizations or companies. Information technology is now synonymous with any form of digital communications and technologies. Even analyzing consumer data or live streaming social media plays a vital role in Information Technology.

Technology 52

Technology Recruitment Media Cloud Computing 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

Monte Carlo

AUGUST 31, 2023

Data is a priority for your CEO, as it often is for digital-first companies, and she is fluent in the latest and greatest business intelligence tools. Your CTO is excited about migrating to the cloud, and constantly sends your team articles highlighting performance measurements against some of the latest technologies.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Data Lake Management Data Governance 52

LinkedIn Engineering

JULY 19, 2023

Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore). The framework itself is extensible to run custom jobs.

Big Data 78

Big Data Data Management Management Metadata 78

ProjectPro

NOVEMBER 14, 2015

Big data has taken over many aspects of our lives and as it continues to grow and expand, big data is creating the need for better and faster data storage and analysis. These Apache Hadoop projects are mostly into migration, integration, scalability, data analytics, and streaming analysis. Data Integration 3.Scalability

Hadoop 52

Hadoop Project Big Data Healthcare 52

DareData

JANUARY 30, 2023

In this post, we'll discuss some key data engineering concepts that data scientists should be familiar with, in order to be more effective in their roles. These concepts include concepts like data pipelines, data storage and retrieval, data orchestrators or infrastructure-as-code.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Edureka

FEBRUARY 9, 2023

The role of Azure Data Engineer is in high demand in the field of data management and analytics. As an Azure Data Engineer, you will be in charge of designing, building, deploying, and maintaining data-driven solutions that meet your organization’s business needs. According to the 2020 U.S.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Mining 52

Monte Carlo

AUGUST 25, 2023

That’s why it’s essential for teams to choose the right architecture for the storage layer of their data stack. But, the options for data storage are evolving quickly. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

ProjectPro

JANUARY 19, 2022

More people, organizations, corporations, and other entities use data daily. Earlier, people focused more on meaningful insights and analysis but realized that data management is just as important. As a result, the role of data engineer has become increasingly important in the technology industry.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Knowledge Hut

JULY 24, 2023

Get ready to discover fascinating insights, uncover mind-boggling facts, and explore the transformative potential of cutting-edge technologies like blockchain, cloud computing, and artificial intelligence. To kick-start your career in database management, you can take the best database courses.

Database 52

Database NoSQL Management Relational Database 52

Edureka

JANUARY 18, 2024

This guide provides a comprehensive understanding of the essential skills and knowledge required to become a successful data scientist, covering data manipulation, programming, mathematics, big data, deep learning, and machine learning technologies. Table of Contents Introduction to Data Science What is Data Science?

Data Science 40

Data Science Deep Learning NoSQL Machine Learning 40

Towards Data Science

FEBRUARY 15, 2023

Concepts, theory, and functionalities of this modern data storage framework Photo by Nick Fewings on Unsplash Introduction I think it’s now perfectly clear to everybody the value data can have. To use a hyped example, models like ChatGPT could only be built on a huge mountain of data, produced and collected over years.

Data Lake 80

Data Lake Data Warehouse Hadoop Data Architecture 80

Edureka

AUGUST 2, 2023

Their efforts make ensuring that data is accurate, dependable, and consistent, laying the groundwork for data analysis and decision-making. What does a Data Processing Analysts do ? A data processing analyst’s job description includes a variety of duties that are essential to efficient data management.

Data Process 52

Data Process Process Data Cleanse Data Mining 52

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Data integration , on the other hand, happens later in the data management flow.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

ProjectPro

AUGUST 11, 2021

This means that a data warehouse is a collection of technologies and components that are used to store data for some strategic use. Data is collected and stored in data warehouses from multiple sources to provide insights into business data.

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Grouparoo

DECEMBER 26, 2021

For data storage, the database is one of the fundamental building blocks. Homogeneous Distributed Database A homogeneous distributed database is one where the underlying database technology is identical for all distributed database elements. There are many kinds of databases available, each with its strengths and weaknesses.

Database 52

Database NoSQL Relational Database Data Storage 52

AltexSoft

MARCH 2, 2023

Data modeling involves creating a conceptual representation of data objects and their relationships to each other, as well as the rules governing those relationships. To design an effective data governance program, it’s crucial to choose an operational model that fits your business size and structure.

Data Governance 52

Data Governance Government Healthcare Programming 52

Snowflake

MARCH 30, 2023

3EJHjvm Once a business need is defined and a minimal viable product ( MVP ) is scoped, the data management phase begins with: Data ingestion: Data is acquired, cleansed, and curated before it is transformed. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

Engineering 82

Engineering Raw Data Data Science Scala 82

Knowledge Hut

DECEMBER 29, 2023

The highlight feature of this platform is its potential to integrate semi-structured and structured data without using any third-party tools. Snowflake also has an automatic query optimization system, so you wouldn’t have to worry about managing its settings.

Data Engineering 40

Data Engineering Data Engineer Engineering BI 40

AltexSoft

JULY 18, 2023

These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. Spark SQL brings native support for SQL to Spark and streamlines the process of querying semistructured and structured data.

Big Data 52

Big Data Data Process Process Hadoop 52

AltexSoft

JULY 29, 2022

The biggest star of the Big Data world, Hadoop was named after a yellow stuffed elephant that belonged to the 2-year son of computer scientist Doug Cutting. The toy became the official logo of the technology, used by the major Internet players — such as Twitter, LinkedIn, eBay, and Amazon. How data engineering works under the hood.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

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. These data have been accessible to us because of the advanced and latest technologies which are used in the collection of data.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

Rockset

DECEMBER 31, 2022

Storage of inconsistent schema items If your data objects are required to be stored in inconsistent schemas, DynamoDB can manage that. Automatic data management DynamoDB constantly creates a backup of your data for safety purposes which allows owners to have data saved on the cloud.

Non-relational Database 52

Non-relational Database Healthcare Amazon Web Services NoSQL 52

ProjectPro

JANUARY 31, 2022

Extensive Network of Partners Data scientists leverage various tools, and the machine learning (ML) sector is continually expanding, with the release of new data science tools every year. By doing so, Snowflake has overcome one of the most significant constraints associated with traditional database technology.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

AltexSoft

MAY 14, 2021

A growing number of companies now use this data to uncover meaningful insights and improve their decision-making, but they can’t store and process it by the means of traditional data storage and processing units. Key Big Data characteristics. And most of this data has to be handled in real-time or near real-time.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

JANUARY 31, 2023

The Big data market was worth USD 162.6 Big data enables businesses to get valuable insights into their products or services. Almost every company employs data models and big data technologies to improve its techniques and marketing campaigns. Define Big Data and Explain the Seven Vs of Big Data.

Big Data 52

Big Data Hadoop AWS Relational Database 52

AltexSoft

NOVEMBER 10, 2021

Well, there’s a new phenomenon in data management that received the name of a data lakehouse. The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. Data warehouse.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

JUNE 7, 2021

Master Nodes control and coordinate two key functions of Hadoop: data storage and parallel processing of data. Worker or Slave Nodes are the majority of nodes used to store data and run computations according to instructions from a master node. Data storage options. Data access options.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

AltexSoft

SEPTEMBER 10, 2021

These types define the underlying principles of integrating data. Data consolidation. Data consolidation is the classic data integration process leveraging ETL technology. The delivery style to consolidate data is common data storage we’re covering below. How data consolidation works.

Data Integration 52

Data Integration Data Consolidation Data Warehouse BI 52

AltexSoft

SEPTEMBER 23, 2021

We’ll also analyze some of the data hub products existing in the market to explore how this technology works and what it actually can do. What is Data Hub? A data hub is a central mediation point between various data sources and data consumers. A data hub serves as a gateway to dispense the required data.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

ProjectPro

NOVEMBER 15, 2021

With SQL, machine learning, real-time data streaming, graph processing, and other features, this leads to incredibly rapid big data processing. DataFrames are used by Spark SQL to accommodate structured and semi-structured data. Trino is a distributed query tool for effectively querying large volumes of data.

Big Data 52

Big Data Project Metadata Programming Language 52

ProjectPro

MARCH 15, 2016

Hadoop is beginning to live up to its promise of being the backbone technology for Big Data storage and analytics. Companies across the globe have started to migrate their data into Hadoop to join the stalwarts who already adopted Hadoop a while ago.

Hadoop 40

Hadoop Retail Healthcare Banking 40

Cloudera

OCTOBER 3, 2018

Google built an innovative scale-out platform for data storage and analysis in the late 1990s and early 2000s, and published research papers about their work. They, too, saw the enormous potential for data at scale in the enterprise. They had proven their ability to build and deliver the technology at Yahoo.

Hadoop 75

Hadoop Cloud Data Storage Big Data 75