In the fast-paced world of data warehousing and business intelligence, effectively managing changes in data is a game changer. A key concept in this field is slowly changing dimensions (SCDs). These dimensions help maintain accurate historical records in reporting and analytics. This blog post will explore the different types of slowly changing dimensions, backed by practical examples to highlight their real-world applications.

When it comes to database design, the concepts of super types and sub-types are vital for creating structured and efficient data. These ideas help in modeling how different entities relate to each other in real-world scenarios. In addition, intersection and association tables play essential roles in managing complex relationships. In this post, we will break down these concepts, using clear explanations and specific examples to enhance your understanding.

In the fast-evolving world of data analytics and cloud computing, businesses face the challenge of effectively processing and analyzing vast amounts of data. With many solutions available, two standout platforms often come up in conversation: Databricks and Snowflake. Both tools offer advanced capabilities driven by different architectural designs, making them suitable for varied data needs. This article will break down the key architectural differences between Databricks and

In today's dynamic business landscape, having the right tools for data analysis can make all the difference. With the vast amount of data available, businesses need efficient ways to process and analyze it for better decision-making. Two powerful platforms that stand out in this area are Apache Kylin and Trino, also known as Presto. While both serve important functions in analytics, understanding how they differ is key for data professionals looking to leverage these technolo

Navigating the landscape of data processing and management tools can be a daunting task for software engineers. With so many options available, it is crucial to identify which solution aligns best with your specific workflow needs. In this post, we will compare three popular tools: Apache Hive, AWS Glue, and Google Data Catalog.

In a previous post, I did a short summary of what distinguishing criteria constituted a datalake and lakehouse. Data management and organization was the key take-away as what made a lakehouse, and the lack of points toward a datalake, in addition to higher velocity of data inputs.