updated the title image of the blog

Author: nayanj98

blog/2026-04-22-spark-vs-fusion-compaction.mdx

@@ -4,14 +4,14 @@ title: "50% Cheaper and 2x Faster Iceberg Compaction: OLake Fusion (Open Source)
 description: "We benchmark Spark rewrite_data_files against OLake Fusion compaction on Apache Iceberg by running a full TPCH lineitem load from Postgres to GCP, applying 200k-record CDC batches every 2 minutes, and tracking TPC-H Query 6 performance, runtime, resource usage, and infrastructure cost."
 tags: [iceberg, tpch, compaction, benchmark, spark, olake, fusion]
 authors: [nayan]
-image: /img/blog/2026/4/final_img.png
+image: /img/blog/2026/4/fusion_vs_spark.webp
 ---
 
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
 <div style={{textAlign: 'center'}}>
-  <img src="/img/blog/2026/4/final_img.png" alt="compaction diagram" style={{width: '80%'}} />
+  <img src="/img/blog/2026/4/fusion_vs_spark.webp" alt="compaction diagram" style={{width: '80%'}} />
 </div>
 
 Over the last few years, most data teams have either moved to a lakehouse architecture or are actively moving in that direction. That shift solves several legacy warehouse and data lake limitations, but migration alone is not the finish line. Once you're on a lakehouse, you still need to manage table health carefully to keep performance stable and costs under control.