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How Joins Work in PostgreSQL

Joins are one of the most powerful features in SQL, allowing you to combine data from multiple tables in a single query. PostgreSQL, as a relational database system, provides robust support for different types of joins. Understanding how joins work under the hood helps you write more efficient queries and troubleshoot performance issues. What Is
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How to Improve Query Performance in PostgreSQL

PostgreSQL is a powerful relational database, but even the most robust systems can suffer from slow queries without proper tuning. Optimizing query performance is crucial to ensure scalability, responsiveness, and efficient resource usage. In this post, we’ll explore actionable techniques to speed up your PostgreSQL queries. 1. Use Indexes Effectively Create Indexes on Filter and
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Optimizing Joins in PostgreSQL: Practical Cases

Joins are essential for querying relational databases, but they can significantly impact performance if not optimized correctly. PostgreSQL provides several ways to improve join efficiency, from indexing strategies to query restructuring. In this post, we’ll explore different types of joins, performance considerations, and practical ways to optimize them. Types of Joins in PostgreSQL PostgreSQL supports
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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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Testing Apache Airflow DAGs: A Modular Approach

Introduction Apache Airflow is a powerful workflow automation tool, but testing DAGs can be challenging due to their dependency on the Airflow scheduler and execution environment. In this post, we explore a modular approach that enables easier testing by extracting logic into separate Python functions, which can be tested independently. Methods for Testing Airflow DAGs
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Running PySpark on Google Colab: Do You Still Need findspark?

Introduction For a long time, using Apache Spark in Google Colab required manual setup, including installing Spark and configuring Python to recognize it. This was often done using the findspark library. However, recent changes in Colab have made this process much simpler. In this post, we will explore whether findspark is still necessary and the
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Visualizing EXPLAIN ANALYZE in PostgreSQL

When working with PostgreSQL, understanding how queries execute can greatly improve performance tuning and optimization. PostgreSQL provides the EXPLAIN ANALYZE command to help developers analyze query execution plans. However, reading raw execution plans can be challenging. In this post, we will explore how to interpret EXPLAIN ANALYZE output and visualize it using online tools. Understanding
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