Eon Yukon vs. Apache Spark
Why teams move Spark ingestion and table maintenance to Eon Yukon
Lower costs to ingest and query
Eon lands and compacts your streams with 4× the compute efficiency of Spark. Run your PySpark and SQL serverless over Spark Connect, with no cluster to size or keep running between queries.
Query-ready tables, with maintenance built in
Eon lands event-level data in query-ready tables and keeps them fast with continuous compaction and sorting. If you turn on upserts, deduplication or aggregates, Eon writes those tables in the same pass as the events, so nothing waits on a separate processing job.
Fresh data without another streaming job
Iceberg recommends Spark streaming triggers of at least one minute. Eon makes new data queryable within seconds, with ingestion and table maintenance handled for you.
Eon vs. Apache Spark at a glance
This comparison focuses on Apache Spark pipelines your team configures and operates for ingestion and table maintenance.
Data freshness
Query-ready data within seconds.
Data becomes queryable after each micro-batch commits. Iceberg recommends triggers of at least one minute.
Data preparation & maintenance
Event-level data lands ready to query, with optional updates, deduplication and aggregates. Table maintenance is built in.
Merge queries apply updates. Separate jobs handle compaction and cleanup.
Pipeline complexity
One managed pipeline produces the tables you choose in one pass, with no separate processing or maintenance jobs.
Streaming, update and maintenance jobs to build and operate.
Context for AI agents
Built-in table descriptions, statistics and change history help agents understand and query the data.
Standard Iceberg statistics. Agent context requires a separate system.
Open formats & interoperability
Iceberg tables, readable by compatible engines.
Reads and writes Iceberg, Delta Lake and other formats.
Querying & compute management
Serverless SQL and Spark Connect, billed per query with no idle compute cost.
Clusters to size and tune, often paid for between jobs.
Real-time application serving
Current values, looked up by key in under 1 ms through Redis-compatible and HTTP APIs.
A separate cache and sync jobs serve current values to apps.
Real-time aggregations
Summaries update alongside event-level data, written to tables and served live, with no state for your team to manage.
Stateful streaming queries, with checkpoints and state to manage.
Operational overhead
Fully managed by Eon, with no tuning required. Dedicated deployment in your region or BYOC.
Your team configures and operates streaming, update and table maintenance jobs.
Compute efficiency
At 1M events/s: 35 vCPU for ingest and 63 with compaction, 4× Spark's compute efficiency.
At 1M events/s: 135 vCPU for ingest. 270 with compaction.
How Eon Yukon compares with Apache Spark
Fresher than a micro-batch, on a quarter of the compute
Freshness has a cost. More frequent commits take more streaming compute and leave more small files to compact.
Spark Structured Streaming works in micro-batches, where each batch reads, writes and commits before the next one starts and the cluster waits while each commit runs. Iceberg itself recommends triggers of at least a minute. Eon Yukon separates writing from committing, so it keeps ingesting while a commit runs, which makes new data queryable within seconds and lets apps look up current values by key within seconds.
In Eon's public benchmark, landing 1 million events per second from Kafka into Iceberg, Eon kept up on 35 vCPU against 135 vCPU for Spark.
Maintenance that never needs a schedule
Streaming tables pile up small files and snapshots, and queries slow down without table maintenance. Iceberg's own documentation recommends regular compaction, manifest rewrites and snapshot expiration for streaming tables, and on Spark each of those, plus orphan-file cleanup, is a separate job you schedule for every table.
Eon Yukon compacts and sorts continuously, within minutes, and removes expired snapshots and orphaned files on its own. In Eon's public benchmark, with both engines merging the same small files toward the same target file size, Eon's compaction was 5× more compute-efficient than Spark's rewrite jobs.
Upserts and deduplication, built in
Streams carry retries, late updates and deletes, but a useful table holds one current row per order, user or device.
Spark Structured Streaming appends to Iceberg, so upserts require a merge query for each micro-batch. Partition filtering and join tuning reduce the work, but merges still reread the partitions they touch. Deduplication adds more code to maintain.
Eon finds changed rows through an index and applies updates, deletes and deduplication with deletion vectors in the same commit as new data. Applying them in the same commit avoids repeated partition scans and keeps commits small as tables grow. Even against Spark with partition-filtered merges and a tuned join, Eon kept one current row per key with 5× the compute efficiency.
Serverless queries, no idle clusters
Teams size batch ETL clusters for the peak and keep query clusters up all day for ad-hoc and agent questions.
With Spark, you choose instance types, executor counts and autoscaling, and often pay while clusters wait for work.
Eon Yukon runs each query, batch ETL or ad-hoc, on its own workers sized to the data it reads, and scales to zero when the query finishes. It reads the same Iceberg tables it writes, with no cluster to size. Existing PySpark and SQL jobs can connect over Spark Connect.
Built-in context for AI agents
Agents that don't know what a table means sample rows and run exploratory queries first, and on Spark each of those keeps a cluster busy.
Spark writes standard Iceberg statistics, but teams document descriptions, joins and change history by hand, if at all.
Eon gives agents more than column names. Built-in table descriptions, statistics and change history help them understand what the data means and how to query it, with less guessing and exploratory work.
How Spark pipeline costs compound
Software
$0.01 per GB ingested, with everything below included.
Free software. You pay for clusters and the people who run them.
Streaming ingest
Included.
A cluster running Structured Streaming around the clock.
Upserts and dedup
Included, applied as data lands.
A merge query in each micro-batch, on the same cluster.
Compaction and snapshot cleanup
Included, continuous.
Separate maintenance jobs per table.
Batch ETL and queries
Serverless, scales to zero between queries. Spark jobs connect over Spark Connect.
Clusters sized for the peak, often paid while idle.
Engineering time
Managed by Eon.
Setup, tuning, upgrades, checkpoints and on-call.
Where the difference shows up most:
High-volume streams
compute grows with event rate, and Eon needs about a quarter of Spark's compute to land and compact the same stream.
Bursty queries
clusters kept warm for agents and analysts cost money between questions, while Eon scales to zero.
Serving apps
on Spark, current values for apps need a separate cache and the jobs that load it, each with its own bill. Eon serves them by key from the same pipeline.
See how Eon works in your environment
Bring a Spark pipeline you run today and we will show you how it runs on Eon Yukon. See how fresh the pipeline keeps its tables, how much compute it needs and what it costs.