Eon Yukon vs. Databricks
Add Eon Yukon to Databricks to land streaming tables merged, sorted and described within seconds, on the same Iceberg tables.
Tables query-optimized on arrival
Teams add Eon when streaming tables need upserts, deduplication and aggregates applied as data lands, without merge queries, OPTIMIZE schedules or another Lakeflow pipeline.
Less compute per event
Our public benchmarks show Eon uses 4× to 7× less compute than open-source Spark and Flink for ingestion and compaction combined.
Open tables Databricks reads
Eon writes Apache Iceberg tables registered in AWS Glue, Google BigLake or any Iceberg REST catalog. Databricks reads the same tables through Unity Catalog federation, with no copies and no load jobs.
Comparison: Eon vs. Databricks
Data freshness
Query-ready data within seconds.
Zerobus Ingest lands appends in about 5 seconds. Current rows wait for a merge query or an AUTO CDC flow afterwards.
Data preparation & maintenance
Event-level data lands ready to query, with optional updates, deduplication and aggregates. Table maintenance is built in.
Liquid clustering and predictive optimization keep tables healthy, each billed separately.
Pipeline complexity
One managed pipeline produces the tables you choose in one pass, with no separate processing or maintenance jobs.
Ingestion, Lakeflow pipelines and maintenance jobs to build, schedule and monitor.
Context for AI agents
Built-in table descriptions, statistics and change history help agents understand and query the data.
AI-suggested comments and metric views that your team reviews and curates. Nothing records distributions, value-matched joins or changes on every commit.
Open formats & interoperability
Iceberg tables, readable by compatible engines.
Zerobus Ingest writes Delta tables into Unity Catalog.
Querying & compute management
Serverless SQL and Spark Connect, billed per query with no idle compute cost.
SQL warehouses and clusters to size, start and stop. Serverless warehouses start in 2 to 6 seconds.
Real-time application serving
Current values, looked up by key in under 1 ms through Redis-compatible and HTTP APIs.
Synced tables copy lake tables into Lakebase Postgres. Continuous sync costs the most and still lags by seconds.
Real-time aggregations
Summaries update alongside event-level data, written to tables and served live, with no state for your team to manage.
Lakeflow pipelines or streaming jobs you write and run.
Operational overhead
Fully managed by Eon, with no tuning required. Dedicated deployment in your region or BYOC.
Platform teams own job schedules, cluster policies and table maintenance settings.
Compute efficiency
One pass produces the tables you choose, with efficient ingestion and built-in maintenance.
Ingest, merge queries, OPTIMIZE and aggregation each run as their own job over the same bytes.
How Eon Yukon complements Databricks
Streaming tables that arrive correct
Streaming data comes with producer retries, late updates and deletes. Analysts and agents need the latest state of each order, user or device, not a log of every version of it.
Zerobus Ingest writes records into Unity Catalog Delta tables within seconds, but it appends them. Getting to one current row per key takes a merge query downstream, or an AUTO CDC flow in a Lakeflow pipeline on the Pro, Advanced or serverless tier. Either way it is a second pipeline your team builds and pays to run.
Eon Yukon merges by key as data lands: upserts and deletes on compound keys, applied with indexes and deletion vectors, and deduplication by any key. Types, transforms and aggregates are applied in the same pass, so the table is correct at every commit and there is nothing to reconcile overnight. Tables also stay predictable for every query: across 21 query shapes on Athena and Trino, Eon tables stayed within 0.63× to 1.57× of the bytes scanned on an untouched table, while a hand-tuned Spark table swung from 9× fewer to 35× more. Eon also sorts the same hourly batch about 13× faster than Spark.
Context agents read before they query
An agent that doesn't know what a table means samples rows, guesses at keys and runs exploratory queries before it can answer. Every one of those queries is billed compute.
Unity Catalog suggests table and column comments with AI, and metric views and the Genie knowledge store hold definitions and joins, all reviewed and curated by your team. Nothing records distributions, value-matched joins or what changed on every commit.
Eon writes distributions, null rates and what changed in the same commit as the data, and agents that study a live sample keep each table's grain, column meaning and joins, found by matching values, current alongside the pipeline. Agents read it over MCP, so one call replaces the exploratory queries.
Serverless queries on the same tables
Clusters sized for the nightly peak run all day, and every dashboard refresh keeps a warehouse warm.
Databricks SQL warehouses and job clusters are sized, started and stopped by your team or by auto-stop settings. Serverless SQL warehouses start in 2 to 6 seconds, but they are billed while they run until auto-stop, and your team sizes them.
Eon Yukon runs serverless SQL on a distributed engine and bills per query, with nothing running when idle. Existing Spark batch jobs can connect over Spark Connect, against the same Iceberg tables Databricks reads; RDD, custom-JVM and GPU jobs stay on Spark.
Runs next to your lakehouse
Adding an engine shouldn't mean moving data or starting a migration.
Eon runs managed in a dedicated account, or in your own AWS or Google Cloud account with your cloud bill and commitments. Tables are Apache Iceberg in the region you pick, registered in AWS Glue, Google BigLake or any Iceberg REST catalog, and read in Databricks through Unity Catalog federation. Databricks notebooks, jobs, BI and SQL warehouses work on the same tables:
No copies and no load jobs between Eon and Databricks
Rows, payloads and credentials stay in the data plane
Eon receives health metrics, column statistics and usage, never row values
How streaming costs compound
Ingestion
$0.01 per GB, with everything below included.
Zerobus Ingest at about $0.05 per GB, with optimization and processing extra.
Upserts and dedup
Included, applied as data lands.
Merge queries, or AUTO CDC in a Pro, Advanced or serverless Lakeflow pipeline.
Sorting and compaction
Included, continuous.
Liquid clustering and predictive optimization, billed separately.
Real-time aggregations
Included, in the same pass.
Lakeflow pipelines.
Rich column statistics
Included, on every commit.
Through Unity Catalog, at extra cost.
Query compute
Per query, with nothing running when idle.
Warehouses and clusters billed while they run.
Where the difference shows up most:
More tables
each new stream on Databricks brings its own ingestion, merge query and maintenance work. With Eon, one read of each event does all of it, so cost follows data volume, not the number of jobs.
Bursty queries
a serverless SQL warehouse is billed while it runs until auto-stop, so bursts of dashboard refreshes keep it running. Eon Yukon bills per query, with nothing to size and nothing running when idle.
See how Eon works in your environment
Bring a stream you land in Databricks today and we will show you that data landing in Eon Yukon tables, with freshness and compute compared side by side.