Difference Between Big Data and Data Warehouses

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 versus Data Lakes

Data warehouses and data lakes occupy different positions in the tradeoff between responsiveness, resource costs, and flexibility. In this article, we explore their differences.

A top-level guide to data lakes

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.

Data Warehouses versus Data Marts

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.

Best Practices for Accessing Your Data Warehouse

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.

What are the Benefits of a Data Warehouse?

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 Warehouse Architecture

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.

What Is a 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.

How to Move Data in Data Warehouses

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.

Data Warehouse Best Practices — preparing your data for peak performance

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.

What is a Data Warehouse Layer?

A tiered data warehouse architecture forms the backbone of your enterprise's data flow, systematically managing and orchestrating every piece of customer and operational data. What is a Data Warehouse Layer?

Key Concepts of a 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.

Data Warehouses versus Databases: What’s the Difference?

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.

How to Create and Use Business Intelligence with a Data Warehouse

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.

How do Data Warehouses Enhance Data Mining?

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.