Today, I had a call with three senior data leaders, and we discussed my preferred data warehouse solutions. I believe modern data warehouses are incredibly powerful and can address almost all use cases. However, they can also become the most expensive data assets within an organization. Managing your own data warehouse cluster is feasible, but finding the right talent to handle large data clusters is challenging, not to mention the hiring budget constraints. A logical option is to adopt a hybrid approach. You can start with a single-node stack using @duckdb and @ApacheArrow , leveraging it as much as possible. Many data use cases can be efficiently handled with reasonable vertical scaling. It's essential to optimize your data and understand why your team needs to process trillions of rows. When the need arises to process large volumes of data, you can then utilize a managed data warehouse. This approach is not difficult to implement. By using @IbisData , switching between systems becomes a seamless process.

Today, I met with five senior managers from enterprise data teams. Here are the key points from our discussion: All of them are using data warehouses, but none have tried single-node data stacks like @duckdb, Arrow, or @IbisData. They argued that single-node technology cannot handle serious workloads, expressing disbelief in its capabilities. The majority were unaware of technologies such as Apache Arrow, Ibis, or Data Fusion. Most were focused on dashboard metrics or AI training, considering data warehouses as the default option without exploring alternatives. Then I brought the topic of cost and suddenly everything changed 🤑