A Bollywood Saga for Your Data Stack
Why This Matters
Once upon a time, in a world full of broken dashboards and brittle schemas, data engineers suffered in silence.
Hive tables ruled with rigidity. Parquet files were dumped without remorse. And schema changes? Total drama.
But then came three disruptors, each with their own superpowers. And like any great Bollywood saga, they couldn’t be more different.
🌟 Meet the Upgraded Heroes
Delta Lake — The Polished Gentleman
- Built by Databricks
- Smooth operator: loves schema evolution, ACID, and time travel
- Doesn’t work well outside its own ecosystem
Think: rich, dependable, and occasionally arrogant
Apache Iceberg — The Open-Source Genius
- Born at Netflix
- Works with Spark, Trino, Flink, Snowflake, GCP, AWS — basically everyone
- Can handle billions of files like it’s no big deal
Think: modern, scalable, and platform agnostic
Apache Hudi — The Scrappy Underdog
- Born at Uber
- Optimized for real-time ingestion and upserts
- Sometimes messy, but always fast
Think: raw power, real-time, hustle-mode always on
Schema‑Change Crisis
- Delta: ALTER TABLE … RENAME COLUMN — done, UniForm syncs Iceberg view instantly
- Iceberg: keeps full schema history plus branching/tagging for safe experiments
- Hudi: handles renames, but needs indexing config
Take‑away ▶ Delta & Iceberg feel seamless; Hudi works when tuned.
Time‑Travel
- Delta & Iceberg store every snapshot as first‑class citizens.
- Iceberg’s
SNAPSHOT_DIFFAPI shows row‑level deltas; Delta’sDESCRIBE HISTORYstays iconic. - Hudi’s “Incremental Query” mode lets you replay stream windows.
Take‑away ▶ All rewind the tape; Iceberg gives the prettiest director’s cut.
Multi‑Engine Love Triangle
| Format | Engines (May 2025) |
|---|---|
| Delta Lake | Spark, Photon, Presto, Trino, Flink, BigQuery, SQL Server PolyBase |
| Iceberg | Spark, Flink, Trino/Presto, Snowflake, BigQuery, Dremio, DuckDB, OneLake, Athena, EMR |
| Hudi | Spark, Flink, Hive, StarRocks, Presto via XTable |
Take‑away ▶ Iceberg is still the most eligible bachelor, but Delta’s UniForm narrows the gap fast.
Upserts & CDC
- Hudi crushes 100 M‑row MERGE with Record‑Level Index (row‑lookup O(log n)).
- Delta boosts batch merges via Bin‑packed writes; streaming Upserts in preview.
- Iceberg shipped
MERGE INTO& position deletes; vendors add vectorized writes.
Winner ▶ Hudi for high‑velocity ingestion; Delta/Iceberg catching up.
Metadata Masterclass
| Metric | Delta | Iceberg | Hudi |
|---|---|---|---|
| Location | Single JSON tx‑log per table | Manifest lists + file‑level stats | Commit metadata + log files |
| Pruning | File‑ & partition‑level | Column‑level metrics; delete‑vectors | Index‑assisted file skipping |
| Catalog | Unity Catalog | REST (Polaris, Nessie), Glue, HMS | Hive metastore / File‑based |
Take‑away ▶ Iceberg’s column‑stats & REST catalog API offer best pruning + vendor neutrality.
Performance & Cost—What Real-World Tests Show
| Format | Runtime sweet-spot | Why it’s fast | What it costs |
|---|---|---|---|
| Delta Lake | Databricks (Photon) | Column pruning + Z-Order ⇒ top-end scan speed | Databricks bills by DBU; Standard starts ≈ $0.20 / DBU, Enterprise tiers higher. Faster scans usually offset the higher unit price, but you are tied to Databricks compute. |
| Iceberg | Any Spark/Flink/Trino/BigQuery/Snowflake cluster | No proprietary features → engines can cost-shop | The format is free. Compute & storage follow the host engine’s normal pricing. Polaris Catalog is Apache 2.0, so you can even self-host at no licence fee. |
| Hudi | High-velocity upserts / CDC on Spark or Flink | Log-structured writes with record indexing | OSS and cloud-agnostic, but frequent compaction + clustering can double or triple Spark hours if not tuned—write-amplification is the hidden charge. |
Key Platform Integrations
| Platform | What it adds |
|---|---|
| Polaris Catalog (Snowflake) | Open-source REST catalog for Iceberg tables; part of Snowflake Arctic. |
| Unity Catalog (Databricks) | Single governance layer for Delta Lake, Iceberg tables, and ML assets. |
| OneLake + XTable (Microsoft Fabric) | Lets Iceberg and Delta coexist and stay in sync; provides read/write access from Fabric, Spark, Snowflake, and Power BI. |
End-of-article cheat-sheet — performance, neutrality and budget
| If you need… | Pick | Cost angle to watch |
|---|---|---|
| Sub-second streaming upserts | Hudi | Keep compaction frequency low to avoid runaway Spark costs. |
| Vendor-neutral, multi-cloud analytics | Iceberg | Shop around: the same table can run on your cheapest Trino, EMR or BigQuery cluster. |
| Tight Databricks integration & simple time-travel | Delta Lake (+ UniForm) | Higher DBU rate, but Photon often finishes sooner—measure total dollars, not unit price. |