Business Intelligence System Architecture – Effective business decision-making processes depend on high-quality information. This is a fact of life in today’s competitive business environment, which requires flexible access to data storage organized in a way that improves business efficiency and provides fast, accurate and up-to-date insights into data. To meet these requirements, a business intelligence architecture has emerged in which the data warehouse is at the core of these processes.
In this post, we will explain the definition, relationship, and differences between data warehousing and business intelligence, and provide a BI architecture diagram that visually explains the relationship of these terms and the structure in which they operate. But first, let’s start with some basic definitions.
Business Intelligence System Architecture
What is BI architecture? Business intelligence architecture is a term used to describe the standards and policies for organizing data through computer methods and technologies that create business intelligence systems used for online data visualization, reporting, and analysis. One of the components of the BI architecture is data warehousing tools. Organizing, storing, cleaning, and retrieving data must be done through a central repository system, namely the data warehouse, which is considered a fundamental component of business intelligence. But how exactly are they connected? Before answering this question, let’s first define in more detail what data warehouse models are. What is a data warehouse? A data warehouse is a central repository for businesses to store and analyze massive amounts of data from multiple sources. Data warehousing is considered a key element of the business intelligence process, providing organizations with the tools to make informed decisions. In other words, a storage system is a data management system in which organizations store current and historical information about sales, marketing, finance, customer service, etc. It simplifies business intelligence processes by providing organizations with the means to generate queries and respond to the most current ones. analytical questions. This allows companies to optimize their performance and build strategies based on accurate information rather than pure intuition. When trying to understand storage and its value in a business environment, it is important to distinguish it from a database. Although they are similar and can be considered valuable for storing and managing data, they are different. Below we’ll discuss some obvious differences that will help you put the value of storage into perspective. Database and Data Warehouse The first and most important difference between the two is that databases record data and transactions, usually in a tabular format that users can access, manipulate and retrieve data at will. The ultimate goal of a database is to provide users with a secure and organized way to store and access information. On the other hand, warehouses store huge amounts of data from many disparate sources and store it for analytical purposes. Giving businesses the environment they need to create queries and communicate the strategies that matter most. The second difference, which is also one of the most significant, is the way the data is processed. On the one hand, databases use online transaction processing (OLTP) to perform a series of simple transactions such as insert, replace, and update, among others. In addition, OLTP responds instantly to user requests, allowing data to be processed in real time. On the other hand, data warehouses use online analytical processing (OLAP) to quickly analyze huge volumes of big data. The main difference between the two is that while OLTP can collect data that happened just a few seconds ago, OLAP can process and analyze data a thousand times faster. However, the third and final difference between the two is that databases are usually limited to one use case, such as storing real-time data about every product sold on your website. It can handle a huge number of simple and detailed queries in a short time. Conversely, a data warehouse is “domain-specific” and can extract aggregated data for complex queries that are later used for analysis and reporting. These are just three of the various differences between the two. We won’t delve into them because that would stray from the real purpose of this blog. However, you can check them out in more detail in this article. Types of data warehouses. Now that you understand the basic concepts of data warehouses, let’s look at some of the key types you need to know. Types: Enterprise Data Warehouse (EDW): As the name suggests. suggests that EDW provides businesses with a centralized system for storing and managing information from a wide range of sources. It helps make decisions from a tactical and strategic perspective. Operational Data Store (ODS): ODS complements the EDW we just described above. This is a central database that is updated in real time and is used for operational reporting when the EDW does not meet the reporting requirements of the business. Data Mart: This is a subset of a data warehouse designed specifically for a specific area of business or team. , such as sales, HR or marketing. It is topic-focused, which means users can find the information they need very quickly. Without further ado, let’s look at how BI and DWH are related. What is Data Warehousing and Business Intelligence? Data warehousing and business intelligence are terms used to describe the process of storing all a company’s data in internal or external databases from various sources, with a focus on analyzing and generating actionable insights using online BI tools. There is a lot of discussion around the topic of BI and DW. Some say that the data warehouse concept has been “rebranded” as business intelligence; therefore they mean the same thing. Others say they are completely different and can be considered two separate categories of software. Others will tell you that a data warehouse is one of many tools that support the BI process. For the purposes of this article, we will consider the last statement to be true. Rather, you consider them to be separate or interchangeable concepts; one without the other will not work. So, to get rid of all this confusion, here we will explain the premises that surround their structure using a BI architecture diagram to understand how a data warehouse completely improves BI processes. BI Architecture Structure In modern business, there are various components and layers that make up a business intelligence architecture. Each of these components has its own purpose, which we will discuss in more detail, focusing on data warehouses. But first, let’s first understand what exactly these components are made of. A robust BI architecture structure consists of: Data collection. The first step involves collecting relevant data from various external and internal sources, which could be databases, ERP or CRM systems, flat files or APIs, to name a few. few. Data Integration: At this stage, the collected data is integrated into a centralized system, often through ETL processes. Here the data is also cleaned and prepared for analysis. Data storage: This is where storage comes into play. A warehouse is a place where structured data is stored. This makes it accessible for querying and analysis. Data Analysis: Once the information is processed, stored and cleaned, it is ready for analysis. With the help of the right tool, data is visualized and used to make strategic decisions. Data distribution: Data, now in the form of graphs and charts, is distributed in different formats. This could be online reporting, a dashboard, or implementing solutions. Knowledge based response. The final stage of the architecture is to extract useful information from the data and use it to make better decisions that ensure the company’s growth. **click to enlarge** In the diagram above we can see how the process goes through different levels and we will now focus in detail on the BI architecture and its components.1. Data Collection The first step in creating a stable architecture begins with collecting data from various data sources such as CRM, ERP, databases, files or APIs, depending on the company’s requirements and resources. Modern BI software offers many different, fast and simple data connectors that make the process smooth and easy by using intelligent ETL engines in the background. They provide connectivity between disparate departments and systems that would otherwise remain siled. From a business perspective, it is a critical element in creating a successful data-driven decision-making culture that can eliminate errors, improve productivity, and streamline operations. You need to collect data to be able to manipulate it.2. Data Integration Once data is collected across disparate systems, the next step is to extract it and load it into the BI data warehouse architecture. This is called ETL (Extract-Transform-Load). With the growing volume of data being generated today and the overload of IT departments and professionals, ETL as a Service is becoming the natural answer to solving complex data queries across a variety of industries. The process is simple; Data is retrieved from external sources (starting with step 1), while ensuring that these sources are not negatively impacted by performance or other issues. Secondly, the data met the required standard. In other words, this (transformation) step ensures that the data will be
Data Warehouse Architecture: Traditional Vs. Cloud Models
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