Building a Real-Time Data Lakehouse with Apache Iceberg and Databricks
A deep technical dive into designing a medallion architecture lakehouse that handles petabyte-scale data ingestion, ACID transactions, and sub-second query performance for enterprise analytics workloads.
Why the lakehouse?
For years, enterprises ran two separate platforms: a data lake for cheap storage of raw data, and a data warehouse for fast, reliable analytics. Keeping them in sync was expensive and slow. The lakehouse combines both: open file formats on low-cost object storage, with the transactions, schema control and performance that analytics needs.
The role of Apache Iceberg
Apache Iceberg is an open table format that brings warehouse-style reliability to files in object storage. Its key features include:
- ACID transactions, so readers never see half-written data.
- Schema evolution: add, rename or drop columns without rewriting tables.
- Hidden partitioning, so queries stay fast without users needing to know the partition layout.
- Time travel and snapshots for auditing, debugging and reproducible reports.
- Engine independence: the same tables can be read by Spark, Trino, Flink, Snowflake and Databricks.
Databricks supports Iceberg alongside Delta Lake, and its UniForm feature lets one table be read in both formats. This gives teams the Databricks processing engine without locking data into a single vendor.
The medallion architecture
Most lakehouses organise data into three layers:
- Bronze: raw data landed as-is from source systems, event streams and files. It is kept for replay and audit.
- Silver: cleaned, de-duplicated and conformed data with consistent types, keys and quality checks.
- Gold: business-ready tables and aggregates designed for dashboards, reporting and machine learning features.
Making it real-time
A real-time lakehouse streams data in continuously rather than in nightly batches. A typical design looks like this:
- Capture changes from operational databases with change data capture (for example Debezium) and publish events to Kafka.
- Use Spark Structured Streaming or Delta Live Tables to write events into bronze tables within seconds.
- Run incremental pipelines that merge changes into silver tables and refresh gold aggregates.
- Serve queries through Databricks SQL warehouses or other engines that read the same open tables.
Performance and cost tips
- Compact small files. Streaming creates many small files; schedule compaction to keep queries fast.
- Cluster data on common filters such as date, region or customer to reduce how much data each query reads.
- Expire old snapshots to control storage costs while keeping enough history for time travel.
- Right-size compute with autoscaling and separate clusters for ingestion, transformation and BI workloads.
Governance from day one
A lakehouse holds an organisation's most valuable data, so governance cannot be an afterthought. Use a central catalog such as Unity Catalog for access control, lineage and auditing, apply column- and row-level security for sensitive data, and build data-quality checks into every pipeline so problems are caught before they reach a dashboard.
AdharAI designs and builds modern data platforms on Databricks, Apache Iceberg and the major clouds. Talk to our data team about your lakehouse.