Big Data and AI

Data Warehouses and Business Intelligence: How They Power Modern Decision-Making Introduction Data is the fuel of modern business. It helps companies understand their customers, optimize operations, and make smarter decisions. But raw data alone isn’t enough. To unlock its full potential, data must be collected, stored, processed, and analyzed efficiently. That’s where data warehouses and business intelligence (BI) come in. What Is a Data Warehouse? A data warehouse is a centralized system that stores large volumes of data from multiple business sources—sales, marketing, finance, inventory, customer service. It’s designed for online analytical processing (OLAP) , enabling fast, complex queries and multidimensional analysis. Unlike a transactional OLTP database or a data lake , a data warehouse focuses on structured, cleaned, and integrated data for analytics and reporting. How Is It Different from Other Data Systems? Database (OLTP): Stores structured data in tables....

Understanding Database Frameworks and Schema Design in Business Intelligence Introduction As we delve deeper into data modeling and schema design, it becomes clear that businesses must evaluate multiple aspects of their database architecture to enhance Business Intelligence (BI). The database framework—including organization, storage, and processing—directly influences how data is used to generate insights. Let’s explore this through a practical example: a grocery store’s database system. Real-World Example: Grocery Store Database In a grocery store, the database system supports daily operations, customer behavior analysis, and promotional effectiveness. It must manage transactions while enabling BI professionals to extract insights about preferences, trends, and marketing performance. Types of Databases in BI OLTP vs OLAP OLTP (Online Transaction Processing) databases are optimized for fast, consistent transaction handling. For example, in an online bookstore, an OLTP syste...

Introduction In the world of Business Intelligence (BI), professionals rely on different schema designs to organize, structure, and analyze data efficiently. These schemas play a crucial role in database functionality and data modeling. In this post, we explore two of the most common BI schemas— star schemas and snowflake schemas —and how they help BI teams extract meaningful insights from data. The Importance of Schemas in BI A schema defines the logical structure of data elements, their physical characteristics, and the relationships between tables in a database. It acts as a blueprint that describes how data is shaped, connected, and stored. Every entry in a database is an instance of a schema, containing all the properties defined within it. Understanding schemas allows BI professionals to organize data effectively, improve query performance, and support accurate decision‑making. For a deeper look at how schemas fit into BI architecture, see: Data Warehouses and Business I...

Dimensional Modeling in Business Intelligence: Simplifying Data Analysis In the field of Business Intelligence (BI) , relational databases and their modeling techniques play a crucial role in managing and analyzing data. One specific modeling technique used in BI is dimensional modeling , which is optimized for quick data retrieval from a data warehouse . From Relational Databases to Dimensional Modeling To begin, let's review relational databases . They consist of tables connected through primary keys and foreign keys , which establish relationships between the tables. For example, in a car dealership database, the Branch ID serves as the primary key in the car dealerships table, while acting as a foreign key in the product details table. This establishes a direct connection between these two tables. Additionally, the VIN acts as the primary key in the product details table and as a foreign key in the repair parts table. These connections create...

Data is the fuel of the digital economy, but not all data is equally useful or accessible. To make data-driven decisions, you need to store, organize and analyze your data in a way that suits your business needs and goals. One way to do that is to use a data mart . A data mart is a subset of a data warehouse that focuses on a specific business area, department or topic. It provides targeted data to defined users, enabling fast access to critical insights. In this post, we’ll explain what a data mart is, how it differs from a data warehouse and a data lake, and the benefits and challenges of using a data mart. What Is a Data Warehouse? A data warehouse is a centralized repository that stores historical and current data from across an organization. It supports business intelligence (BI) and analytics applications, enabling complex queries, reporting, and advanced analytics. It follows the ETL (extract-transform-load) process and stores structured data fro...

If you have ever used an online banking app, bo oked a flight ticket, or placed an order on an e-commerce website, you have interacted with an OLTP database. An OLTP database is a type of database that handles online transaction processing (OLTP), which is a data processing category that deals with numerous transactions performed by many users. In this post, we will explain what an OLTP database is, how it differs from an OLAP database, and what are the features and benefits of using an OLTP database. What is Online Transaction Processing (OLTP)? Online transaction processing (OLTP) is a data processing category that deals with numerous transactions performed by many users. The OLTP system is an online database system that processes day-to-day queries that usually involve inserting, updating, and deleting data. A transaction is a logical unit of work that consists of one or more operations on the database. For example, when you transfer money from your checking a...

Data is the new oil, as the saying goes. But how do you store, manage and analyze all the data that your organization generates or collects? How do you turn data into insights that can drive your business forward? One possible solution is to use a data lake . A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics and machine learning. In this post, we explain what a data lake is, how it differs from a data warehouse, and the benefits and challenges of using a data lake. Data Lake vs Data Warehouse – Two Different Approaches Depending on your requirements, a typical organization will need both a data warehouse and a data lake, as they serve different needs and use cases. Data warehouse : optimized for analyzing relational data from transactional system...

Introduction Data modeling , design patterns, and schemas form the backbone of efficient and organized data management. Whether you're an experienced database professional or a beginner, understanding these concepts is crucial for leveraging the full potential of your data. In this article, we'll delve into the world of data modeling, explore different design patterns, and shed light on the significance of schemas in organizing and optimizing databases. Section 1: Data Modeling – An Essential Tool for Organizing Data Data modeling is the art of organizing and structuring data elements and their relationships. It provides a conceptual map that helps maintain data consistency and enables efficient navigation through complex database systems. Just like a map guides you through a train system, a data model guides you through the database, allowing you to understand the relationships between different data elements. Section 2: Unlocking the Power of Design Patterns ...

Section 1: Understanding Data Modeling Data modeling is the process of creating a visual representation of an entire information system or its components, establishing connections between data points and structures. Its goal is to illustrate the types of data used and stored within the system, their relationships, grouping and organization methods, as well as their formats and attributes. By modeling data, businesses can gain valuable insights and facilitate effective decision-making processes. Section 2: Types of Data Systems Data systems consist of source systems, where data is imported and exposed, and target databases, where data is acted upon. Source systems include: Data lakes : store large amounts of raw data in its original format. OLTP databases : optimized for fast data processing and transactions. Target systems include: Data marts : subject‑oriented subsets of data warehouses. OLAP databases : designed for anal...

Experiential learning is a form of education that involves learning by doing. It is based on the idea that people learn best when they are actively engaged in a meaningful task that reflects real-world situations and challenges. Experiential learning can take many forms, such as internships, projects, simulations, games, field trips, service-learning, and more. One of the benefits of experiential learning is that it can help you develop transferable skills. These are skills that can be applied from one job to another, regardless of the industry or sector. Transferable skills are highly valued by employers because they demonstrate your ability to adapt, learn, and solve problems in different contexts. Some examples of transferable skills are: • Communication : The ability to express yourself clearly and effectively in oral and written forms, as well as listen and respond to others. • Collaboration : The ability to work well with others, respect diverse perspecti...