Data lakes and data warehouses serve distinct purposes in data storage and management. Amazon Web Services (AWS) offers Amazon S3 for data lakes, ideal for varied data types and big data scenarios. For structured data and optimized querying, AWS provides Amazon Redshift for data warehouses. Choosing the right solution depends on specific needs, offering flexibility... Continue reading →

This is a continuation of my posts on relational databases, started here. Previous posts on this theme, “Data Modeling“, “Let’s Talk About Database Schema“, “The Exasperating Topic of Database Indexes“, “The Keys & Relationships of Relational Databases“, and “Relational Database Query Optimization“. Consider you’re a die-hard fan of progressive death metal, with a particular affinity... Continue reading →

Over the next few months while I read through these books I hope to provide summaries and related notes on the material. Who knows, maybe you’ll want to dive into the material yourself! Until then happy thrashing code and may you have high retention and comprehension in reading! Continue reading →

I dig through a lot of internet results and blog entries that show CRUD data modeling all the time. A lot of these blog entries and documentation are pretty solid. Unfortunately, rarely do we end up with data that is accurately or precisely modeled the way it ought to be or the way we would... Continue reading →

Recently I put together some quick code to give some timings on the various data generation libraries available for Go. For each library there were a few key pieces of data generation I wanted to time: First Name – basically a first name of some sort, like Adam, Nancy, or Frank. Full Name – something... Continue reading →