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Data Warehouse for the Beginners!

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction The concept of data warehousing dates to the 1980s. DHW, short for Data Warehouse, was presented first by great IBM researchers Barry Devlin and Paul […].

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Data Exchange with Modern Data Warehouse – Snowflake

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Snowflake has long been a Cloud Data Warehouse. The post Data Exchange with Modern Data Warehouse – Snowflake appeared first on Analytics Vidhya.

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Data Warehouse in Azure SQL

Analytics Vidhya

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.

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An Introduction to Data Warehouse

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction The following is an in-depth article explaining what data warehousing is as well as its types, characteristics, benefits, and disadvantages. What is a data warehouse?

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The Next-Generation Cloud Data Lake: An Open, No-Copy Data Architecture

A next-gen cloud data lake architecture has emerged that brings together the best attributes of the data warehouse and the data lake. This new open data architecture is built to maximize data access with minimal data movement and no data copies.

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Data Warehouses: Basic Concepts for data enthusiasts

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction The purpose of a data warehouse is to combine multiple sources to generate different insights that help companies make better decisions and forecasting.

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Building a Data Warehouse using Google BigQuery

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Like many marketers, I’m sure your marketing data comes from multiple platforms, and you must juggle a mix of classified data to analyze and understand your customer journey.

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The Need for Data Warehouse and Its Alternatives

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Data from different sources are brought to a single location and then converted into a format that the data warehouse can process and store.

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What are Schemas in Data Warehouse Modeling?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Do you think you can derive insights from raw data? Wouldn’t the process be much easier if the raw data were more organized and clean? Here’s when Data […].

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Data Warehouses, Data Marts and Data Lakes

Analytics Vidhya

Introduction All data mining repositories have a similar purpose: to onboard data for reporting intents, analysis purposes, and delivering insights. By their definition, the types of data it stores and how it can be accessible to users differ.

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Data Lake or Data Warehouse- Which is Better?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Data is defined as information that has been organized in a meaningful way. Data collection is critical for businesses to make informed decisions, understand customers’ […].

Data Lake 233
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TCO Considerations of Using a Cloud Data Warehouse for BI and Analytics

Enterprises poured $73 billion into data management software in 2020 – but are seeing very little return on their data investments. 22% of data leaders surveyed have fully realized ROI in the past two years, with 56% having no consistent way of measuring it.

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Snowflake Architecture & Key Concepts for Data Warehouse

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. 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.

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Understanding Key Concepts on Data Warehouses

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. 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.

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Do you Know About Data Warehouse?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. A data warehouse (DW) is a key component of business intelligence that brings together data from a variety of sources into a single data repository for advanced analytics and decision support.

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Data Modelling Techniques in Modern Data Warehouse

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Hello, data-enthusiast! 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.

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Building Data Warehouse Using Google Big Query

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. The post Building Data Warehouse Using Google Big Query appeared first on Analytics Vidhya. Big data Cloud Computing Data Warehouse Intermediate big query blogathon Google Cloud

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Checklist Report: Preparing for the Next-Generation Cloud Data Architecture

Data architectures have evolved dramatically. It is time to reconsider the fundamental ways that information is accumulated, managed, and then provisioned to the different downstream data consumers.

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AWS Redshift: Cloud Data Warehouse Service

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Amazon’s Redshift Database is a cloud-based large data warehousing solution. The post AWS Redshift: Cloud Data Warehouse Service appeared first on Analytics Vidhya.

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Data Warehouse vs Data Lake: Differences Explained

DataFloq

We experience the great impact of data both on our lives and business. But those great amounts of data must be stored and analyzed in an effective way. It is a crucial part of an organization as the data stored is a valuable asset. Big Data

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A Brief Introduction to the Concept of Data Warehouse

Analytics Vidhya

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 Lakes Meet Data Warehouses

David Menninger's Analyst Perspectives

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.

Data Lake 232
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The Unexpected Cost of Data Copies

This paper will discuss why organizations frequently end up with multiple data copies and how a secure "no-copy" data strategy enabled by the Dremio data lake service can help reduce complexity, boost efficiency, and dramatically reduce costs.

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How to Build a Data Warehouse Using PostgreSQL in Python?

Analytics Vidhya

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?

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Beginners Guide to Data Warehouse Using Hive Query Language

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Have you ever wondered how big IT giants store and process huge amounts of data? storing the data […].

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What are the differences between Data Lake and Data Warehouse?

Analytics Vidhya

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?

Data Lake 232
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The Data Warehouse is Dead, Long Live the Data Warehouse, Part I

Data Virtualization

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.

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Top Considerations for Building an Open Cloud Data Lake

In this paper, we explore the top considerations for building a cloud data lake including architectural principles, when to use cloud data lake engines and how to empower non-technical users.

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The Definitive Guide to Data Warehouse vs. Data Lake vs. Data Lakehouse

DataFloq

Struggling to harness data sprawl, CIOs across industries are facing tough challenges. One of them is where to store all of their enterprise’s data to deliver robust data analytics. There have traditionally been two storage solutions for data: data warehouses and data lakes.

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Should The Data Warehouse Be Immutable?

KDnuggets

Is the data warehouse broken? Is the "immutable data warehouse" the right path for your data team? KDnuggets Originals Data ScienceLearn more here.

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Why Organizations Need Data Warehouses

KDnuggets

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

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Cloud Data Warehouse Migration 101: Expert Tips

Alation

There was a time when most CIOs would never consider putting their crown jewels — AKA customer data and associated analytics — into the cloud. But today, there is a magic quadrant for cloud databases and warehouses comprising more than 20 vendors. Data quality /wrangling.

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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Data lakes and data warehouses are probably the two most widely used structures for storing data. Data Warehouses and Data Lakes in a Nutshell. The stored data is unprocessed, and the structure is usually applied when it is retrieved.

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HIVE – A DATA WAREHOUSE IN HADOOP FRAMEWORK

Analytics Vidhya

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.

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Differences Between Data Lake and Data Warehouses

The Data Administration Newsletter

Data lake is a newer IT term created for a new category of data store. But just what is a data lake? According to IBM, “a data lake is a storage repository that holds an enormous amount of raw or refined data in native format until it is accessed.”

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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

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.

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The Data Warehouse is Dead, Long Live the Data Warehouse, Part II

Data Virtualization

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).

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The Seven Best ELT Tools for Data Warehouses

KDnuggets

ELT helps to streamline the process of modern data warehousing and managing a business’ data. In this post, we’ll discuss some of the best ELT tools to help you clean and transfer important data to your data warehouse.

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Data Warehouse Migration: How to Make This Strategic Move

Octopai

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. It’s time to commence a data warehouse migration project.

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Seven Common Challenges Fueling Data Warehouse Modernisation

Cloudera

Enterprise data warehouse platform owners face a number of common challenges. In this article, we look at seven challenges, explore the impacts to platform and business owners and highlight how a modern data warehouse can address them. Data Types & Access Patterns.

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Snowflake: 6 Compelling Reasons to Modernize Your Data Warehouse

Corinium

Are you extracting maximum insights from your data? Data is the same. 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. Focusing on data-driven decision-making instead of on administration and maintenance. You know crude oil is more valuable when it’s processed.

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Unlocking Data Storage: The Traditional Data Warehouse vs. Cloud Data Warehouse

Sisense

Data warehouse vs. databases Traditional vs. Cloud Explained Cloud data warehouses in your data stack A data-driven future powered by the cloud. The datasphere is expanding at an exponential rate, and companies of all sizes are sitting on immense data stores.

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Metadata-Driven Data Warehouses are Ideal

The Data Administration Newsletter

A metadata-driven data warehouse (MDW) offers a modern approach that is designed to make EDW development much more simplified and faster. It makes use of metadata (data about your data) as its foundation and combines data modeling and ETL functionalities to build data warehouses.