Shraddha Shetty<p>Tired of hitting driver memory limits with pandas.read_excel() in Spark?</p><p>With Apache Spark 4.0, you can now scale Excel ingestion efficiently using custom DataSource connectors:<br />✅ Fully distributed Excel read/write<br />✅ Native batch &amp; streaming support<br />✅ No more driver bottlenecks<br />✅ Just use .format(&quot;excel&quot;) in your Spark job</p><p>📖 Dive into the details: <a href="https://medium.com/@dataninsight/scalable-excel-ingestion-in-databricks-6aa87202a1d7" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">https://</span><span class="ellipsis">medium.com/@dataninsight/scala</span><span class="invisible">ble-excel-ingestion-in-databricks-6aa87202a1d7</span></a></p><p><a href="https://me.dm/tags/DataEngineering" class="mention hashtag" rel="tag">#<span>DataEngineering</span></a> <a href="https://me.dm/tags/ApacheSpark" class="mention hashtag" rel="tag">#<span>ApacheSpark</span></a> <a href="https://me.dm/tags/ExcelIngestion" class="mention hashtag" rel="tag">#<span>ExcelIngestion</span></a> <a href="https://me.dm/tags/PySpark" class="mention hashtag" rel="tag">#<span>PySpark</span></a> <a href="https://me.dm/tags/Databricks" class="mention hashtag" rel="tag">#<span>Databricks</span></a> <a href="https://me.dm/tags/InterviewSeries" class="mention hashtag" rel="tag">#<span>InterviewSeries</span></a></p>