Tag: Data Warehouse
All the articles with the tag "Data Warehouse".
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Batch Means Two Different Things: Why the Term Became Confusing in Data Engineering
In data systems, some of the most common words are also the most overloaded. Few terms illustrate this better than batch . Historically, batch processing described a very specific operating model:
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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
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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
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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
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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
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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
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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
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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