Delta vs. Iceberg vs. Hudi—what should I pick?

If you’re on Databricks and want simplicity, Delta is great. If you need multi-engine interoperability (Trino, Athena, Spark, Snowflake external tables), Iceberg is the safe bet. If your workload is heavy on CDC upserts and incremental merges, Hudi shines. Pick one and standardize.

When do I need streaming instead of batch?

If business value depends on freshness under ~30 minutes (fraud scoring, near-real-time ops), go streaming. Otherwise, high-frequency micro-batch often wins on simplicity and cost. Avoid cargo-cult streaming for daily KPIs.

How do I handle GDPR deletion with immutable logs?

Use table-level deletes in your lakehouse format (DELETE FROM …) and expire snapshots/compaction to physically remove data within policy. Encrypt and restrict raw Bronze access; propagate deletes through Silver/Gold via CDC or change tables.

Do I still need a warehouse if I have a lakehouse?

Often yes, for semantics, governance, and BI concurrency. Many teams run a warehouse (Snowflake/BigQuery) over curated Gold or use Trino/Databricks SQL. The trick is consistent tables and one source of truth in your catalog.

What about AI-generated pipelines—safe or not?

AI can draft boilerplate Spark/dbt code fast, but we always run a **vibe code cleanup**: enforce the contract, add tests, set file sizes, and wire SLOs. Otherwise you get cute demos and nasty 2am incidents.

Data-engineering · Dec 4, 2025 · 10 minute read

The Data Lake That Stopped Drowning Us: Designing a Lakehouse That Scales Without Torching Trust

You don’t need another petabyte. You need a lakehouse that keeps promises as it scales: reliable data, quality you can prove, and business value delivered on schedule.

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The Data Lake That Stopped Drowning Us: Designing a Lakehouse That Scales Without Torching Trust

Key takeaways

Implementation checklist