Expert Data Engineering Solutions for Your Business

The two previous articles where concerning evaluation of Apache Iceberg and it's capabilities, as well as using PyIceberg to create objects and load data. This post will focus on getting data out, and using Pandas dataframes to create analytics.

In today’s data-driven world, processing and analyzing large datasets efficiently can be a major challenge for software engineers and data scientists. Traditional data processing libraries like Pandas, while user-friendly, may struggle with the vast volumes of data that many organizations face.

In the ever-evolving landscape of NoSQL databases, Couchbase and MongoDB stand out as leading choices for developers. Both systems offer unique features that make them suitable for different applications. I believe it's crucial to dive into their features, performance, scalability, and more, so you can decide which one fits your project's needs best.

When you handle big data, having the right tools makes all the difference. Apache Spark and Dask are two popular frameworks that help with large datasets. They both provide powerful DataFrame abstractions for complex data manipulations, but they come with different strengths and weaknesses. In this post, I'll provide insights to help you decide which framework is best suited for your needs.

In today's fast-paced data-driven world, the choice of database system can significantly influence an application's performance and success. Two leading options, graph databases and relational databases, come with distinct features that cater to different needs. In this post, I will delve into both types of databases, providing specific examples and insights into their strengths, weaknesses, and suitable applications.

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.

Data modeling is a vital part of database design that helps organizations manage and structure their data efficiently. One major concept in data modeling is normalization, which organizes data to minimize redundancy while enhancing data integrity. Normalization can be broken down into several levels, called normal forms. In this post, we will examine the differences between the first, second, third, fourth, and fifth normal forms, helping you understand their unique character