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Last updated: May 20, 2022

Data Warehousing

Just as the word 'warehouse' means a place to store bulk produce or goods in large quantities. Adding the word, "data" means storing a large amount of data. Data Warehousing is the method used for analytical purposes and business reporting. It mainly involves collecting and managing the data from various sources and then using it for business purposes. This data is also used to extract business insights. There are various types of Data warehousing architecture such as single-tier, two-tier, and three-tier architecture.
Data Warehousing
In this article, we will learn about data warehouse, their architecture, design implementation, security, advantages, and disadvantages, and so on.
Characteristics of Data Warehouse MEDIUM
Data Warehouse Characteristics 1. Subject-Oriented 2. Integrated 3. Time-Variant 4. Non-Volatile 5. Designed for Decision Support
How does Data Warehousing Work?
In this article, we will understand the meaning and working of Data Warehousing. At the same time, we saw the features and uses of Data Warehousing.
Components of Data Warehouse EASY
In this article, we will discuss the components of a data warehouse.
Author
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Data Lakes vs Data Warehouses EASY
In this article, we will be covering the difference between a Data lake and vs Data warehouse.
Databases vs Data Warehouses EASY
 In this article, we shall discuss the Databases and Data Warehouses
Data Warehouse Architecture EASY
Data Warehouse Architecture is complex as it's an information system that contains historical and commutative data from multiple sources.
Introduction to Meta Data for Data Warehousing MEDIUM
Metadata is data that describes other data. In data warehousing, Metadata refers to information representing the characteristics and structure of the data present in the warehouse.
How are Data Warehouses Built?
This article discusses How are Data Warehouses Built?
Applications of data warehouse EASY
This article discusses Applications Of Data Warehousing.
How to become a Data Scientist? MEDIUM
In this article, we will discuss who a Data Scientist is, their roles & responsibilities and the various steps to becoming a data scientist.
Author Tisha
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Data Warehousing Tools

Introducing to you, the various tools that are helpful in implementing Data Warehousing. You might find the topic tough initially but slow and steady with the proper understanding you will find it quite easy. And we are here to provide all the material on it and give you a proper outline and in-depth explanation about various tools. Starting with a bit of introduction and common features of data warehousing tools, we would introduce some of them such as Amazon Redshift - a brief introduction and setup details will be covered here, and Microsoft Azure - how it can be helpful as a data warehousing tool, Google Big Query - introduction to what it is and Snowflake- details including what it is and how it can be used as a data warehousing tool. We will also be taking up one data warehousing tool that is oracle Warehouse builder and shows you how you can install and set it on your local machine and what are its feature. We will be comparing The three bigs - AWS, Azure, and google big query, laying out various features and comparing them.
Amazon Redshift EASY
This article will cover a detailed explanation regarding Amazon RedShift.
Microsoft Azure EASY
In this blog, we will learn about Microsoft Azure, the types of Azure Cloud and the various services offered by Azure.
Google BigQuery
In this blog, we will learn about Google BigQuery, features of BigQuery and its usage.
Snowflake
In this blog, we will learn about what the snowflake is, its features, its architecture, along with that how it is used in data warehousing.
MicroFocus Vertica EASY
In this blog, we will be studying the concept of MicroFocus Vertica in Data Mining and Warehousing.
Oracle Warehousing Builder
This blog mainly focuses on the overview of the Oracle Warehousing Builder, its features and its installation process.
AWS vs. Azure and Google Cloud
We will be heading towards the Aws, Azure, and Google cloud platform and their difference.