Difference Between Big Data and Data Warehouses
Data Warehouses versus Data Lakes
A top-level guide to data lakes
Data Warehouses versus Data Marts
Best Practices for Accessing Your Data Warehouse
What are the Benefits of a Data Warehouse?
Data Warehouse Architecture
What Is a Data Warehouse?
How to Move Data in Data Warehouses
Data Warehouse Best Practices — preparing your data for peak performance
Key Concepts of a Data Warehouse
Data Warehouses versus Databases: What’s the Difference?
Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
How to Create and Use Business Intelligence with a Data Warehouse
How do Data Warehouses Enhance Data Mining?
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Although terms “big data” and “data warehouse” are often compared directly, there is a categorical difference between them. We will cover the difference in this article.
Data warehouses and data lakes occupy different positions in the tradeoff between responsiveness, resource costs, and flexibility. In this article, we explore their differences.
In this article, we’ll cover everything you need to know about data lakes. You’ll learn, what is a data lake? How is a data lake different from a data warehouse? Benefits of data lake.
In the worlds of business intelligence and outcome modeling, the terms data warehouse and data mart are often used interchangeably. The differences are worth knowing, though, so in this article we’ll compare and contrast the two.
In this article, you will learn how data is stored in a data warehouse, how it’s accessed, best practices for writing SQL queries, who the major cloud data warehouse vendors are, and the various types of data storage models available.
A data warehouse is a software construct that pulls data from different sources into a single target for business intelligence analysis and support for strategic decisions. In this article, we examine the benefits of implementing a data warehouse.
Data warehouses need to be architected in a way that maximizes flexibility and maintains speed. In this article, you'll learn about data warehouse architectures, as well as what you should consider when setting up your own data warehouse.
Data warehouses offer a wide range of benefits. In this article, you’ll learn about the benefits and features of a data warehouse and how to effectively implement, use, and maintain a data warehouse.
In this article, we’ll explore the various ways that data enters, moves through, and exits warehouses. You’ll also learn how a data warehouse strategy helps businesses understand their current position and set benchmarks to drive long-term growth.
One of the best modern tools for maximizing the observability and analytic power of your data is the data warehouse. This article will guide you through data warehouse best practices and illustrate how to get the most value from your data warehouse.
This article covers key concepts of the data warehouse, comparing its capabilities with that of relational databases, data marts, and data lakes — all common solutions to the challenges of consuming large, varied types of data.
Understanding the sometimes-subtle differences between types of data infrastructure can be challenging. This article will explain the differences and the best usages of a data warehouse as opposed to a database.
When it comes to choosing a data warehouse for the open-source analytics stack, it is very common to pit data warehouses against each other. Redshift vs BigQuery, Redshift vs Snowflake, etc. are some common examples.
In this article, we cover what business intelligence (BI) is, which roles within the company work with it, and explain BI's relationship to the data warehouse.
Everyone’s saying it more and more, and in increasingly tired metaphors: data is a new currency. This article covers key ways in which data warehouses enhance the data mining process.