Delta Lake vs Apache Iceberg vs Hudi

Delta Lake (Databricks-originated, now open-source), Apache Iceberg (Netflix-originated), and Apache Hudi (Uber-originated) are the three dominant open table formats for lakehouse architectures. Delta Lake leads in Databricks and Fabric ecosystems. Iceberg is gaining momentum in multi-engine environments (Snowflake, Spark, Trino). Hudi excels in streaming/CDC use cases. Choose Delta Lake for Databricks or Fabric deployments. Choose Iceberg for vendor-neutral, multi-engine lakes. Choose Hudi for CDC-heavy streaming workloads. All three support ACID transactions, schema evolution, and time travel. See: Fabric vs Databricks and Lakehouse vs Warehouse.

How It Works in Practice

Enterprise implementations follow structured progression: assessment (2-4 weeks), design (2-4 weeks), build (4-12 weeks), stabilization (2-4 weeks). Total: 10-24 weeks. Organizations that skip assessment spend 40-60% more due to mid-build misalignment.

PhaseDurationDeliverableCost
Assessment2-4 weeksArchitecture recommendation$15K-50K
Design2-4 weeksSolution blueprint$25K-75K
Build4-12 weeksProduction implementation$80K-300K
Stabilize2-4 weeksTesting + knowledge transfer$20K-60K

What Does This Cost?

Consulting rates: $120-350/hr. Typical engagement: $75K-300K over 8-20 weeks. Through Xylity, rates are 20-35% below traditional consulting firms — 4.3-day deployment, 92% first-match acceptance rate. Related: Data Engineering Consulting Cost.

How Do You Get Started?

Start with a 2-week paid assessment ($15K-30K) that tells you exactly what to build, how long it takes, and what it costs. Then deploy pre-qualified specialists through Xylity — 4.3 days to first profile, 92% acceptance rate, 200+ delivery partners across 20+ domains and 22 industry verticals. Related: The True Cost of a Vacant Seat — every week of delay costs $12K-24K.

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