MPP vs. Single-Node Engines Small workloads? Use @DuckDb or @Polars for faster in-memory performance. Massive datasets? MPP systems like @Spark or @Snowflake scale dynamically. Experiment: @DuckDB outperformed Spark at <100GB. 💡 Don't drive groceries shopping with a tank!

The Future of Distributed Systems Object storage like S3 has become databases — scalable & efficient for transactional & analytical workloads. Emerging programming models: 1️⃣ Distributed DBs: On files 2️⃣ Serverless: Focus on code 3️⃣ Wasm: Portable execution Challenge: "one-way-door” innovation

The Iceberg Effect Modern data is evolving: → Iceberg now leads open table formats (Snowflake & Databricks adoption confirms it). → Cloud-native storage is a must (legacy systems won’t keep up). → AI thrives on scalable, open architectures. More innovation. Less vendor lock-in. Ready to shift?

What Do Data Warehouses Really Do? → $300K/year on Snowflake, and 90% is spent on queries. → Most queries are tiny (median: 100MB, 99.9% <300GB). → Most workloads = ingestion + transformation (not analytics). 💡 Small Data > Massive Complexity. Are we overpaying for simplicity?

Think Small. Make Big Impact. More Data ≠ Better Results. → Recent data is the most valuable. → Smaller AI models deliver bigger impact. → Local-first development works. Stop relying on distributed complexity when single machines get the job done. The #SmallData Movement is here. Are you in?

BigData isn’t the problem—it never was. Most enterprises have <100GB in active data but overpay for tools designed for massive scale (#Snowflake, #Databricks, etc.). Focus on #SmallData: → Easier to analyze → Cheaper to manage → Faster insights Time to rethink your data strategy. #SmallData