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Tag: Data Warehouse

From OLTP to OLAP: How Data Moves from 3NF to a Dimensional Data Warehouse

Modern data architectures typically separate operational systems from analytical systems. This separation is not accidental—it reflects fundamentally different workloads, data models, and optimization strategies. This article explains the conceptual transition: Operational Systems (OLTP) and 3rd Normal Form Transactional systems—CRM platforms, payment processors, ERPs, application databases—are designed for: These systems are usually modeled in Third Normal
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Benchmarking OLTP vs. OLAP: Measuring Performance Effectively

Understanding the performance differences between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) is crucial for designing efficient database systems. This post outlines a structured approach to benchmarking these two architectures and measuring their efficiency based on real-world scenarios. Key Metrics for Benchmarking To compare OLTP and OLAP performance, we focus on the following
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OLTP vs. OLAP: How JOINs and Efficiency Shape Their Differences

Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) are two distinct database architectures, each designed for different purposes. One key factor that differentiates them is how they handle JOIN operations and the impact these have on query performance. In this post, we’ll explore these differences and why OLAP tends to be more efficient for
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The Origins of OLTP and OLAP: A Brief History

Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) are fundamental concepts in database management, each serving distinct purposes. But when did these terms first appear, and how did they evolve? Let’s explore their origins and how they became the cornerstone of modern data systems. The Emergence of OLTP The concept of Online Transaction Processing
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Comparison Between Star Schema and Snowflake Schema in PostgreSQL

Comparison Between Star Schema and Snowflake Schema in PostgreSQL When designing a database for analytical workloads, choosing the right schema can significantly impact performance and query efficiency. The two most common data warehouse schema models are Star Schema and Snowflake Schema. In this post, we’ll explore the differences between these schemas, their advantages and disadvantages,
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Handling Schema Changes in a Data Warehouse

When building and maintaining a Data Warehouse (DWH), handling schema changes without breaking existing processes is a crucial challenge for data engineers. As new requirements emerge, we often need to add new fields, modify existing structures, or adjust data models while ensuring smooth operation for reporting and analytics. This blog post explores best practices and
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Why OLTP Systems Don’t Retain Historical Changes

Online Transaction Processing (OLTP) systems are designed for high-speed transactions and efficient data management. However, one of their characteristics is that they do not retain historical changes by default. In this post, we will explore why this happens and provide an example to illustrate the concept. OLTP Systems: Focused on Current Data OLTP databases are
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Understanding the Evolution of Data Warehousing: From Codd’s Relational Model to Modern Data Warehouses

Data management has undergone significant transformations since the advent of the relational model by Edgar F. Codd. Today, data warehouses stand as a cornerstone of modern data analytics. This blog post explores the differences between Codd’s relational model and data warehouses, highlighting their unique roles and applications in data management. The Relational Model: A Brief
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