Analytics Vidhya

SEPTEMBER 13, 2022

Introduction The purpose of a data warehouse is to combine multiple sources to generate different insights that help companies make better decisions and forecasting. It consists of historical and commutative data from single or multiple sources. Most data scientists, big data analysts, and business […].

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Data Warehouse Forecasting Data Science Big Data 351

Analytics Vidhya

JANUARY 7, 2022

By their definition, the types of data it stores and how it can be accessible to users differ. This article will discuss some of the features and applications of data warehouses, data marts, and data […]. The post Data Warehouses, Data Marts and Data Lakes appeared first on Analytics Vidhya.

Data Warehouse 376

Data Warehouse Data Lake Data mining Reporting 376

Analytics Vidhya

SEPTEMBER 28, 2022

This article was published as a part of the Data Science Blogathon. Introduction to Data Warehouse SQL Data Warehouse is also a cloud-based data warehouse that uses Massively Parallel Processing (MPP) to run complex queries across petabytes of data rapidly. Import big […].

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Data Warehouse Big Data Data Science Publishing 259

Analytics Vidhya

AUGUST 3, 2022

Data and analytics have become critical for firms to remain competitive. The post Most Frequently Asked Data Warehouse Interview Questions appeared first on Analytics Vidhya. Reports, dashboards, and analytics tools are used by business users to derive insights […].

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Data Warehouse Dashboards Data Collection Data Science 371

Analytics Vidhya

OCTOBER 28, 2022

Data collection is critical for businesses to make informed decisions, understand customers’ […]. The post Data Lake or Data Warehouse- Which is Better? We can use it to represent facts, figures, and other information that we can use to make decisions. appeared first on Analytics Vidhya.

Data Lake 359

Data Lake Data Warehouse Data Collection Data Science 359

Analytics Vidhya

MAY 3, 2022

Introduction on Data Warehouses During one of the technical webinars, it was highlighted where the transactional database was rendered no-operational bringing day to day operations to a standstill. The post Understanding Key Concepts on Data Warehouses appeared first on Analytics Vidhya.

Data Warehouse 290

Data Warehouse Data Science Publishing Analytics 290

Analytics Vidhya

JUNE 6, 2022

Wouldn’t the process be much easier if the raw data were more organized and clean? Here’s when Data […]. The post What are Schemas in Data Warehouse Modeling? It’s possible, of course, but it can be tiresome and not be as accurate as it should be. appeared first on Analytics Vidhya.

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Data Warehouse Modeling Data Science Publishing 349

Analytics Vidhya

AUGUST 5, 2022

Introduction to Data Warehouse In today’s data-driven age, a large amount of data gets generated daily from various sources such as emails, e-commerce websites, healthcare, supply chain and logistics, transaction processing systems, etc. It is difficult to store, maintain and keep track of […].

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Data Warehouse Data-driven Data Science Publishing 304

Analytics Vidhya

JUNE 11, 2022

Introduction on Snowflake Architecture This article helps to focus on an in-depth understanding of Snowflake architecture, how it stores and manages data, as well as its conceptual fragmentation concepts. The post Snowflake Architecture & Key Concepts for Data Warehouse appeared first on Analytics Vidhya.

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Data Warehouse Data Science Publishing Management 320

Analytics Vidhya

APRIL 25, 2022

Companies may store petabytes of data in easy-to-access “clusters” that can be searched in parallel using the platform’s storage system. The post AWS Redshift: Cloud Data Warehouse Service appeared first on Analytics Vidhya. The datasets range in size from a few 100 megabytes to a petabyte. […].

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Data Warehouse Data Science Publishing Analytics 286

Analytics Vidhya

JUNE 20, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Data warehouse generalizes and mingles data in multidimensional space. The post How to Build a Data Warehouse Using PostgreSQL in Python? appeared first on Analytics Vidhya.

Data Warehouse 364

Data Warehouse Data Science Publishing Analytics 364

Analytics Vidhya

FEBRUARY 21, 2023

This is where data warehousing is a critical component of any business, allowing companies to store and manage vast amounts of data. It provides the necessary foundation for businesses to […] The post Understanding the Basics of Data Warehouse and its Structure appeared first on Analytics Vidhya.

Data Warehouse 275

Data Warehouse IT Data Collection Reporting 275

Analytics Vidhya

JULY 6, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction A Data Warehouse is Built by combining data from multiple. The post A Brief Introduction to the Concept of Data Warehouse appeared first on Analytics Vidhya.

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Data Warehouse Data Science Publishing Analytics 319

Analytics Vidhya

JULY 10, 2022

In this article let’s discuss “Data Modelling” right from the traditional and classical ways and aligning to today’s digital way, especially for analytics and advanced analytics. The post Data Modelling Techniques in Modern Data Warehouse appeared first on Analytics Vidhya.

Data Warehouse 266

Data Warehouse Modeling Data Science Publishing 266

Analytics Vidhya

FEBRUARY 2, 2023

Now, businesses are looking for different types of data storage to store and manage their data effectively. Organizations can collect millions of data, but if they’re lacking in storing that data, those efforts […] The post A Comprehensive Guide to Data Lake vs. Data Warehouse appeared first on Analytics Vidhya.

Data Lake 272

Data Lake Data Warehouse Management Analytics 272

Analytics Vidhya

OCTOBER 21, 2020

Overview Understand the meaning of data lake and data warehouse We will see what are the key differences between Data Warehouse and Data Lake. The post What are the differences between Data Lake and Data Warehouse? appeared first on Analytics Vidhya.

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Data Lake Data Warehouse Analytics 336

David Menninger's Analyst Perspectives

MAY 7, 2020

In this analyst perspective, Dave Menninger takes a look at data lakes. He explains the term “data lake,” describes common use cases and shares his views on some of the latest market trends. He explores the relationship between data warehouses and data lakes and share some of Ventana Research’s findings on the subject.

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Data Lake Data Warehouse Risk Marketing 287

Analytics Vidhya

MAY 30, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Different components in the Hadoop Framework Introduction Hadoop is. The post HIVE – A DATA WAREHOUSE IN HADOOP FRAMEWORK appeared first on Analytics Vidhya.

Data Warehouse 274

Data Warehouse Data Science Publishing Analytics 274

Analytics Vidhya

APRIL 29, 2022

Different organizations make use of different databases like an oracle database storing transactional data, MySQL for storing product data, and many others for different tasks. storing the data […]. The post Beginners Guide to Data Warehouse Using Hive Query Language appeared first on Analytics Vidhya.

Data Warehouse 241

Data Warehouse Data Science Publishing Analytics 241

Data Virtualization

OCTOBER 18, 2022

The post The Data Warehouse is Dead, Long Live the Data Warehouse, Part I appeared first on Data Virtualization blog - Data Integration and Modern Data Management Articles, Analysis and Information. In times of potentially troublesome change, the apparent paradox and inner poetry of these.

Data Warehouse 98

Data Warehouse ROI Data Integration Internet of Things 98

AWS Big Data

AUGUST 15, 2024

Unifying these necessitates additional data processing, requiring each business unit to provision and maintain a separate data warehouse. This burdens business units focused solely on consuming the curated data for analysis and not concerned with data management tasks, cleansing, or comprehensive data processing.

Data Lake 102

Data Lake Data Warehouse Data Governance Publishing 102

Smart Data Collective

AUGUST 28, 2021

Data lakes and data warehouses are probably the two most widely used structures for storing data. Data Warehouses and Data Lakes in a Nutshell. A data warehouse is used as a central storage space for large amounts of structured data coming from various sources. Key Differences.

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Data Lake Data Warehouse Unstructured Data Structured Data 140

ScienceSoft

JULY 8, 2024

What is a data warehouse? Definition and purpose| DWH vs big data warehouse vs a data lake | DWH trends to consider for your business | DWH pricing

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Data Warehouse Data Lake Big Data 52

KDnuggets

OCTOBER 30, 2023

A comparative overview of data warehouses, data lakes, and data marts to help you make informed decisions on data storage solutions for your data architecture.

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Data Lake Data Warehouse Data Architecture 133

KDnuggets

MAY 17, 2022

Is the data warehouse broken? Is the "immutable data warehouse" the right path for your data team? Learn more here.

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Data Warehouse Data Science 112

AWS Big Data

OCTOBER 19, 2023

Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure. Delta Lake doesn’t have a specific concept for incremental queries.

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Data Lake Data Warehouse Visualization Snapshot 115

AWS Big Data

MAY 30, 2024

Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data. Solution overview Amazon Redshift is an industry-leading cloud data warehouse.

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Data Warehouse Data Lake Cost-Benefit Structured Data 92

Octopai

DECEMBER 26, 2022

Migrating a data fulfillment center (i.e. warehouse). Your data warehouse is not too different from an Amazon fulfillment center. Your old data warehouse has become deprecated. Or you predict significant cost and efficiency benefits from transferring to a different data warehousing platform.

Data Warehouse 98

Data Warehouse Cost-Benefit Recreation/Entertainment Reporting 98

Corinium

APRIL 29, 2019

Conventional data warehouses can’t handle the volume, complexity, and variety of today’s data, and they can’t empower all your teams to access and analyze that data in real time. It’s much more valuable when you can use it to drive your business forward.

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Data Warehouse Data Quality Data-driven IT 150

Smart Data Collective

SEPTEMBER 23, 2020

The market for data warehouses is booming. While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Data Warehouse.

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Data Lake Data Warehouse Unstructured Data Big Data 106

KDnuggets

SEPTEMBER 14, 2022

So where can you store, harness and collect findings in your data - in one place? Data Warehouses. What is the right tool for this?

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Data Warehouse Data Science 91

IBM Big Data Hub

FEBRUARY 2, 2023

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. The rise of cloud has allowed data warehouses to provide new capabilities such as cost-effective data storage at petabyte scale, highly scalable compute and storage, pay-as-you-go pricing and fully managed service delivery.

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Data Warehouse Cost-Benefit Unstructured Data Data Architecture 56

Data Virtualization

NOVEMBER 24, 2022

Reading Time: 4 minutes My previous post explained that, in my mind, the data lakehouse differs hardly at all from the traditional data warehouse architectural design pattern (ADP). It consists largely of the application of new cloud-based technology to the same requirements and constraints.

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Data Warehouse Data Integration Technology Management 52