Bug Bounty — Troubleshooting using Databricks

Auto Loader silently drops records when new columns are added to source files

Cloud storage access fails due to misconfigured service principal credentials

Streaming pipeline stalls completely after cluster restart due to corrupted checkpoint

Micro-batch jobs timeout intermittently under high ingestion load

Auto Loader performance degrades significantly when ingesting large files in a single batch

Delta table reads return inconsistent results after a failed mid-write operation

Unity Catalog permission denied errors block cross-team access to shared datasets

Schema evolution breaks downstream Silver layer pipeline when new columns are added upstream

Data inconsistency between Bronze and Silver layers after partial reprocessing of events

DLT pipeline fails silently after introducing a new transformation with no visible error

Incremental ETL pipeline reprocesses already-ingested records after an unexpected job restart

PySpark job crashes with OOM error when processing a large join on a skewed dataset

Unexpected query results traced back to a suboptimal Spark execution plan

DQ checks pass successfully despite corrupted records due to incorrect rule logic

Dynamic schema validation breaks when the source system adds optional nullable columns

Complex multi-condition DQ rules generate false positives for valid edge case records

Reconciliation mismatch detected between Azure and GCP pipelines running identical logic

Multi-task Databricks workflow partially completes but fails to trigger downstream tasks

External Airflow DAG fails to trigger Databricks job due to expired API token

Cross-environment inconsistencies appear when promoting jobs from dev to prod using Asset Bundles

Recovery mechanism fails to resume from the correct checkpoint after a mid-run cluster failure

Query performance degrades after Delta table grows due to small file accumulation

Cluster autoscaling fails to trigger during peak load, causing SLA breach

Shuffle-heavy join causes excessive disk spill despite sufficient cluster memory allocation

Aggressive intermediate DataFrame caching causes memory pressure and downstream job slowdowns