The hottest Data Stack Substack posts right now

The Modern Data Stack evolved but faced challenges of cost, complexity, and sprawl.
MDS led to more focus on product analytics and consolidation of data systems.
There is still a need for innovation in data modeling to address complexity and drive value.

The Modern Data Stack (MDS) community has grown significantly over the years with various meetups and events.
Using tools like Snowflake, dbt, and Looker in the Modern Data Stack improves data capabilities and productivity.
Although some criticize the Modern Data Stack and its imperfections, it has greatly enhanced data handling and analytics for many organizations.

Data teams are facing challenges with tool abandonment in the current economy.
Databases remain crucial in the data stack, with less need for new, specialized tools.
Building trust and bridging gaps between data and engineering teams is vital for successful data applications.

The modern data stack is evolving with new tools and options for data architecture.
Key trends include the focus on data ingestion and telemetry, improved orchestration tools, and advancements in compute engines.
Data consumption is being enhanced through self-serve AI capabilities, BI tools, and free-form analyst tools, all sitting on a semantic layer.

Understanding technology pricing models and unit costs in embedded analytics can help predict ROI and optimize expenses. Choose tools with fair pricing like MotherDuck for cost-effectiveness.
Adding value to customers and business in embedded analytics is achievable with tools like MotherDuck for speed, Cube for data curation, and React for front-end performance and flexibility.
Choosing a developer-friendly stack like MDCuRe (MotherDuck, Cube, React) enhances productivity by enabling efficient team collaboration, tool integration, and continuous delivery workflows.

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New advancements in vendor APIs can render your product irrelevant overnight.
Products heavily reliant on a single API backend are not sustainable in the long term.
To avoid becoming obsolete, build products that combine multiple unique data sources.