Blog
Essays, breakdowns, and field notes on simplifying data systems, avoiding overengineering, and building tools that actually get used.
Key Takeaways 1. Streamlit vs Dash comes down to execution model, not features Streamlit uses a full script rerun model, while Dash uses a callback-based dependency
Key Takeaways * WhatsApp works well for order management in small distribution businesses because it removes friction for both customers and operators. * The problem is not WhatsApp
Key Takeaways * Many organizations adopt complex data tools too early. Platforms like Spark clusters, streaming systems, and lakehouses are often introduced before the data problems actually
Key Takeaways * Many order management systems are designed for mid sized or enterprise distributors, making them difficult to justify economically for smaller operations. * Vendor support models
Key Takeaways * Streamlit is best for fast internal data applications and prototypes. * Dash provides more structure for internal analytical applications built in Python. * React is the
Key Takeaways * Most wholesale distributors with fewer than 50 employees already have the tools needed to understand business performance. The primary challenge is consistent use of
A new year brings new challenges that will inevitably surface on your data engineering roadmap. The beginning of the year is a great moment to embrace
Key Takeaways * Most companies with fewer than one hundred employees do not need a distributed cloud data warehouse. A well configured Postgres database can support the
Key Takeaways * Table formats such as Apache Iceberg, Apache Hudi, and Delta Lake solve real problems in large data platforms, but those problems usually appear only
Key Takeaways * Many data teams default to distributed frameworks like Apache Spark for workloads that could run efficiently on a single machine, which often leads to
Data is the black gold of the 21st century, yet most organizations are still digging in the wrong place. They pour resources into massive platforms built