Building a Hybrid Lakehouse: Strategic Use of Apache Hudi and Delta Lake in Databricks

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Apache Hudi and Delta Lake are built for different workloads. Hudi is optimised for high-frequency writes; Delta Lake is built for fast, reliable reads. Using one format across the entire data platform forces an unnecessary trade-off high ingestion costs if you go Delta only, or weaker analytical performance if you go Hudi only. This blog covers how to use both formats where each performs best, and what the cost and performance gains look like in practice.

Understanding the Lakehouse Foundation

Databricks built its Lakehouse on Apache Spark, Delta Lake, and MLflow open-source components sitting on top of cheap object storage that answer queries at warehouse speed. The Medallion Architecture splits the work across three layers. Bronze holds raw records exactly as they arrived. Silver cleans and restructures them. Gold is what analysts and ML pipelines actually query.

The Complementary Strengths of Hudi and Delta Lake

Hudi was built for write-heavy ingestion. Delta Lake was built for read-heavy analytics. Forcing one format across both use cases costs more and performs worse than using each where it fits.

Apache Hudi : Key Advantages

• Write Amplification Reduction: Merge-on-Read writes only the changed rows to small log files. Full file rewrites happen during compaction, not on every update CDC pipelines on large tables see up to 60% lower compute spend as a result.
• High-Frequency Mutations: Hudi's record-level index looks up the exact file holding each row before running the upsert. No full table scans, no wasted I/O. When a pipeline pushes millions of changes per run, that lookup is what keeps job times manageable.
• Background Compaction: Compaction runs as a separate process on its own schedule. The ingestion job does not wait for file reorganisation before the next batch starts.
• Streaming-Optimised: CDC workloads inserts, updates, and deletes in a continuous feed are the primary design target. Hudi handles them without the write overhead of rewriting full Parquet files on each commit.

Delta Lake : Key Advantages

• Liquid Clustering: File layout reorganises automatically as query patterns shift. No partition strategy to define at table creation, no manual intervention when access patterns change.
• Photon Acceleration: Photon is a vectorised C++ execution engine inside Databricks Runtime. SQL queries run up to 4x faster than on standard Spark no query or schema changes needed.
• Predictive Optimisation: OPTIMIZE and VACUUM are scheduled against idle cluster windows automatically. The maintenance backlog does not pile up between manual runs.
• Enterprise Reliability: ACID transactions mean a failed write never leaves the table in a broken state. Time-travel lets teams query any prior snapshot both matter in regulated environments where data correctness is auditable.

Delta UniForm: The Bridge Between Formats

UniForm writes Iceberg and Hudi metadata alongside Delta metadata all pointing at the same Parquet files on storage. One physical dataset, three sets of format metadata. Nothing is duplicated.

Metadata generation is asynchronous, so write latency is unaffected. A Spark job writing Delta, a Trino cluster reading Iceberg, and a Hudi client can all hit the same table concurrently no format conversion steps, no extra Databricks compute charges.

Unity Catalog: The Governance Foundation

Running Hudi in Bronze and Delta in Gold means two format readers, multiple compute engines, and access policies that need to work across both. Unity Catalog handles that through one control plane:

• catalog.schema.table namespace works the same whether the underlying table is Hudi, Delta, or Iceberg  no separate naming conventions per format.
• Column-level lineage is captured as data moves from Bronze ingestion through Silver transformation into Gold analytics and ML feature pipelines.
• The built-in classifier scans tables for PII and financial identifiers, tags what it finds, then enforces row- and column-level access at query time. No separate tagging job to schedule.
• One ANSI SQL access control model covers all three formats. Iceberg REST Catalog is supported out of the box for engines outside Databricks.
• Spark, Trino, Flink, and other engines hit Delta tables as Iceberg through the REST interface. That traffic bypasses Databricks compute entirely, so no runtime charge attaches to it.

Strategic Format Selection Across Medallion Layers

Bronze Layer : Apache Hudi

Raw ingestion from source systems typically carries a high ratio of updates to net-new rows  exactly the workload Hudi was designed for. Log-based writes mean only changed rows hit storage on each ingestion cycle. Bronze tables stay current with upstream systems at up to 60% lower compute cost compared to Delta-only ingestion, and the full record history is kept for reprocessing or audit.

Silver Layer : Delta Lake with UniForm

After cleansing and validation, update frequency drops and read patterns become more predictable. Delta Lake fits that profile. Enabling UniForm means Hudi clients from Bronze can still read Silver tables during any transition window. Liquid Clustering picks up layout optimisation automatically as query patterns settle.

Gold Layer : Pure Delta Lake

Gold tables exist to be queried fast, repeatedly, by BI tools expecting consistent response times. Photon handles execution speed. Liquid Clustering keeps file layout current without manual partition work. Predictive Optimisation runs housekeeping in the background. Sub-second performance on large datasets, no ongoing engineering overhead.

Delta Sharing: Governed Data Distribution

Delta Sharing pushes live query access to external partners and internal teams without moving or copying data. The source table stays in one place. Recipients query it directly, subject to the row- and column-level permissions set in Unity Catalog. Data owners see exactly who queried what and when.

Real-World Impact: Cost and Performance

Numbers from live deployments running this architecture:

• Ingestion Cost: Teams migrating Bronze CDC workloads from Delta to Hudi consistently report 60% lower compute spend. Writing 20 GB of log files per cycle instead of rewriting 500 GB of Parquet is the mechanism behind that figure.
• Query Speed: Gold layer Delta tables with Liquid Clustering and Photon return executive dashboard queries in two to three seconds. The same queries on a conventional warehouse took 45 seconds before migration. That gap compounds across every scheduled report, every ad hoc request, every day.
• Storage Overhead: Without UniForm, teams in multi-format environments keep separate file copies per format. UniForm collapses that to one physical copy storage spend drops by up to 67% and all format readers stay compatible.
• Governance Consolidation: Unity Catalog covers access control, lineage, and data classification in one place. Teams that previously licensed separate catalogue and governance tooling have cut that line from the budget entirely.

The Path Forward

Hudi belongs in Bronze because write amplification is a real cost at CDC scale. Delta Lake belongs in Gold because Photon, Liquid Clustering, and Predictive Optimisation are built for analytical query loads. Unity Catalog ties both together under one access control model. Delta Sharing extends data access outward without creating copies.

The teams getting 60% off their ingestion bills and three-second dashboard queries are not using a single format everywhere they are using the right format at each layer. That is the architecture this blog describes, and the results are repeatable.