Tag: Data Warehouse

Top 10 Data Warehouse Tools

Data warehousing is one of the most important factors in reporting and data analysis; it’s considered the core component of business intelligence. This article will discuss essential data warehouse tools. Building a data warehouse was historically a complex process, requiring lots of infrastructure for data storage. But Cloud computing technology has amazingly reduced the effort and cost of data warehousing. Due to this reason, data warehousing is moving forward with fast, efficient, and scalable Cloud-based tools.

What Are Facts and Dimensions in a Data Warehouse?

Facts and dimensions are the fundamental elements that define a data warehouse. They record relevant events of a subject or functional area (facts) and the characteristics that define them (dimensions). Data warehouses are data storage and retrieval systems (i.e., databases) specifically designed to support business intelligence (BI) and OLAP (online analytical processing) activities. They are different from databases designed to support transactional systems – e.g., e-commerce sites – whose function is primarily OLTP (online transactional processing).

What Is a Data Warehouse?

We help you understand the data warehouse and its concepts. Data and analytics have become indispensable for businesses to remain competitive. Business users rely on reports, dashboards, and analytics to extract insights from data, monitor business performance, and support decision-making. Data warehouses are used to feed these reports, dashboards, and analysis tools. It does so by efficiently storing data to minimize input and output (I/O) and deliver query results quickly to hundreds and thousands of users simultaneously.

OLAP, OLTP, ETL and More: What You Need to Know About Data Warehouses

A data warehouse is a system that uses different technologies – including relational databases – to enable analytical reporting, which aids in tactical and strategic decision-making. Learn about all the key concepts of data warehousing in this article. Understanding how a data warehouse (DWH) works means more than just understanding the database designs for that DWH. You also have to understand online analytical processing (OLAP) and online transaction processing (OLTP) systems and extract-transform-load (ETL) procedures.

What is Amazon Redshift?

Amazon Redshift is one of the most popular cloud databases. Could it be your data-warehouse solution? Read on to find out if Amazon Redshift meets your needs. In 2012, Amazon announced its new cloud database system called Redshift. Basically, it is a data-warehouse solution intended for analytical systems, which can handle huge volumes of data—up to 1 petabyte (1024 TB). Amazon Redshift is available as a service (Database as a Service) and is a part of a bigger cloud ecosystem called Amazon Web Services (AWS).

A Subscription Business Data Model

In the previous two parts, we’ve presented the live database model for a subscription-based business and a data warehouse (DWH) we could use for reporting. While it’s obvious that they should work together, there was no connection between these two models. Today, we’ll take that next step and write the code to transfer data from the live database into our DWH. The Data Models Before we dive into the code, let’s remind ourselves of the two models we’ll work with.

A Subscription Business Data Model

Can you design an OLAP database model from an OLTP model? In this article, we’ll show you how! This is the second article of our data warehouse (DWH) series. You can find the first one here. The idea behind the series is to start with the OLTP (Online Transaction Processing) database model, present a possible solution for the reporting/OLAP (Online Analytical Processing) data model, and then finally consider the code we’ll use to perform the ETL process.

A Subscription Business Data Model

Welcome to a new series that shows you the practical side of the data warehouse (DWH)! In the first article, we’ll tackle a data model for a subscription business. In previous data warehouse articles (The Star Schema, The Snowflake Schema, Star Schema vs. Snowflake Schema) we focused more on the theory. In this series, we’ll show you how you could create a data warehouse for a real-life application, such as a database model.

Dimensions of Dimensions: A Look at Data Warehousing’s Most Common Dimensional Table Types

When we start a data warehousing project, the first thing we do is define the dimensional tables. Dimensional tables are the interesting bits, the framework around which we build our measurements. They come in many shapes and sizes. In this article, we are going to take a closer look at each type of dimensional table. Dimensional tables provide context to the business processes we wish to measure. They tell us all we need to know about an event.

Facts about Facts: Organizing Fact Tables in Data Warehouse Systems

The process of defining your data warehousing system (DWH) has started. You’ve outlined the relevant dimension tables, which tie to the business requirements. These tables define what we weigh, observe and scale. Now we need to define how we measure. Fact tables are where we store these measurements. They hold business data that can be aggregated across dimension combinations. But the fact is that fact tables are not so easily described – they have flavors of their own.

Improve Your Financial Reporting With Data Warehousing

Financial institutions, especially banks, usually have really large datasets. To use that data, it must be stored in such a way that it is easily available for generating reports. The trend now is to use a data warehouse to store all your relevant data, and to use smaller data marts (subsets of the warehouse) to keep specific data sets in a convenient place. But where to start? In this article, we’ll look at one possible solution, similar to a project I worked on in the past.

Star Schema vs. Snowflake Schema

In the previous two articles, we considered the two most common data warehouse models: the star schema and the snowflake schema. Today, we’ll examine the differences between these two schemas and we’ll explain when it’s better to use one or the other. The star schema and the snowflake schema are ways to organize data marts or entire data warehouses using relational databases. Both of them use dimension tables to describe data aggregated in a fact table.

The Snowflake Schema

In a previous article we discussed the star schema model. The snowflake schema is next to the star schema in terms of its importance in data warehouse modeling. It was developed out of the star schema, and it offers some advantages over its predecessor. But these advantages come at a cost. In this article, we’ll discuss when and how to use the snowflake schema. The Snowflake Schema if (typeof VertabeloEmbededObject === 'undefined') {var VertabeloEmbededObject = "

The Star Schema

Today, reports and analytics are almost as important as core business. Reports can be built out of your live data; often this approach will do the trick for small- and medium-sized companies without lots of data. But when things get bigger – or the amount of data starts increasing dramatically – it’s time to think about separating your operational and reporting systems. Before we tackle basic data modeling, we need some background on the systems involved.

13 Blog Articles on Database Design Best Practices and Tips

There’s a lot to keep in mind when you’re designing a database, and very few of us can remember every valuable tip and trick we’ve learned. So, let’s take a look at some online resources that feature database design tips and best practices. As we go, I’ll share my own opinions on the ideas presented, based on my experience in database design. Obviously, this article is not an exhaustive list, but I’ve tried to review and comment on a cross section of sources.

Data Vault 2.0 Modeling Basics

In my last post, we looked at the need for an Agile Data Engineering solution, issues with some of the current data warehouse modeling approaches, the history of data modeling in general, and Data Vault specifically. This time we get into the technical details of what the Data Vault Model looks like and how you build one. For my examples I will be using a simply Human Resources (HR) type model that most people should relate to (even if you have never worked with an HR model).

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